Podcasts about machine learning engineer

Master Google Cloud's most advanced AI certification with this definitive 2025 study guide. From TensorFlow and data pipelines to ML ops, model deployment, and ethical AI—this book delivers the knowledge, tools, and confidence to help you ace the Professional Machine Learning Engineer Exam. Backed by real-world examples, mock exams, and hands-on insights.

✅ SUBSCRIBE for more episodes MPF Discussion with Sijuade OguntayoFrom Drowning to Giving: The Gamesville Foundation with Sijuade OguntayoAbout Sijuade Sijuade is an entrepreneur, and a seasoned Machine Learning Engineer with a profound commitment to leveraging technology for societal benefit. With a passion for technology and community service, Sijuade advocates for making technology accessible and useful for underserved populations. He is an advocate for lifelong learning and is continually exploring new ways to expand his knowledge and skills within the tech space.From Drowning to Giving: The Gamesville FoundationWhat happens when a near-tragedy sparks a movement to help others? In this episode of My Perfect Failure, I sit down with Sijuade Oguntayo, a passionate tech enthusiast and trustee of The Gamesville Foundation. From childhood inspiration in Iron Man comics to using tech for social good, Sig shares his journey into philanthropy and the incredible work of The Gamesville Foundation.Tune in to hear how The Gamesville Foundation is tackling loneliness, hardship, and disability while empowering young people through scholarships and recreation.Key Takeaways:1.Turning Tragedy into Purpose – The Foundation was born from a life-changing event, proving that adversity can inspire action.2.The Power of Play – Games and recreation create safe spaces for connection and healing.3.Volunteers Make the Difference – The heart of any nonprofit is its volunteers and partnerships.4.Tech as a Force for Good – AI and innovation can help bridge gaps in underserved communities.5.Giving Back Creates a Ripple Effect – Small acts of kindness lead to lasting change.Links to Sijuade•Website: https://ngo.thegamesvillefoundation.com/ •The Gamesville Foundation LinkedIn:  https://www.linkedin.com/posts/the-gamesville-foundation_our-why-activity-7107594476260315136-TSVU/•Connect with Sijuade on LinkedIn: https://www.linkedin.com/in/cydal/ Please Leave A Review"I have a small favor to ask! If you've been enjoying My Perfect Failure, leaving a review would mean the world to me. It helps the show grow and reach more people who need these stories of resilience and success. Plus, I love hearing your thoughts!.  So please leave a review on Apple, Spotify or the platform of your choice.Paul: Contact DetailsWork with me: paul@myperfectfailure.comMPF Website: https://www.myperfectfailure.com/Paul Padmore Website:d https://stan.store/Paul_PSubscribe to MPF YouTube channel: https://www.youtube.com/@paulpadmore8275

Разбор резюме в прямом эфире. Разбираем CV программистов, которые хотят работать на американские компании. Frontend Software Engineer, Backend Software Engineer, Full Stack Engineer, Mobile Software Engineer, DevOps Engineer, Site Reliability Engineer (SRE), Machine Learning Engineer, Software Architect, Java Developer, Android, iOS Developer, Python, Django, Flask, JavaScript, React, .NET Developer, C# Engineer и так далее.Присылайте свое резюме для разбора в прямом эфире в телеграм канал https://t.me/prodcastUSA.Маша (Мария) Подоляк (Marsha Podolyak)Автор Телеграм канала "

In this podcast episode, we talked with Alexander Guschin about launching a career off Kaggle.About the Speaker: Alexander Guschin is a Machine Learning Engineer with 10+ years of experience, a Kaggle Grandmaster ranked 5th globally, and a teacher to 100K+ students. He leads DS and SE teams and contributes to open-source ML tools.00:00 Starting with Machine Learning: Challenges and Early Steps 13:05 Community and Learning Through Kaggle Sessions 17:10 Broadening Skills Through Kaggle Participation 18:54 Early Competitions and Lessons Learned 21:10 Transitioning to Simpler Solutions Over Time 23:51 Benefits of Kaggle for Starting a Career in Machine Learning 29:08 Teamwork vs. Solo Participation in Competitions 31:14 Schoolchildren in AI Competitions42:33 Transition to Industry and MLOps50:13 Encouraging teamwork in student projects50:48 Designing competitive machine learning tasks52:22 Leaderboard types for tracking performance53:44 Managing small-scale university classes54:17 Experience with Coursera and online teaching59:40 Convincing managers about Kaggle's value61:38 Secrets of Kaggle competition success63:11 Generative AI's impact on competitive ML65:13 Evolution of automated ML solutions66:22 Reflecting on competitive data science experience

A software engineer based in Delft, Alex Strick van Linschoten recently built Ekko, an open-source framework for adding real-time infrastructure and in-transit message processing to web applications. With years of experience in Ruby, JavaScript, Go, PostgreSQL, AWS, and Docker, I bring a versatile skill set to the table. I hold a PhD in History, have authored books on Afghanistan, and currently work as an ML Engineer at ZenML. Beyond the ChatBot Hype: A Deep Dive into Real LLM Success Stories // MLOps Podcast #287 with Alex Strick van Linschoten, ML Engineer at ZenML. // Abstract Alex Strick van Linschoten, a machine learning engineer at ZenML, joins the MLOps Community podcast to discuss his comprehensive database of real-world LLM use cases. Drawing inspiration from Evidently AI, Alex created the database to organize fragmented information on LLM usage, covering everything from common chatbot implementations to innovative applications across sectors. They discuss the technical challenges and successes in deploying LLMs, emphasizing the importance of foundational MLOps practices. The episode concludes with a call for community contributions to further enrich the database and collective knowledge of LLM applications. // Bio Alex is a Software Engineer based in the Netherlands, working as a Machine Learning Engineer at ZenML. He previously was awarded a PhD in History (specialism: War Studies) from King's College London and has authored several critically acclaimed books based on his research work in Afghanistan. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://mlops.systems https://www.zenml.io/llmops-database https://www.zenml.io/llmops-database https://www.zenml.io/blog/llmops-in-production-457-case-studies-of-what-actually-works https://www.zenml.io/blog/llmops-lessons-learned-navigating-the-wild-west-of-production-llms https://www.zenml.io/blog/demystifying-llmops-a-practical-database-of-real-world-generative-ai-implementations https://huggingface.co/datasets/zenml/llmops-database --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Alex on LinkedIn: https://www.linkedin.com/in/strickvl

Hugo speaks with Alex Strick van Linschoten, Machine Learning Engineer at ZenML and creator of a comprehensive LLMOps database documenting over 400 deployments. Alex's extensive research into real-world LLM implementations gives him unique insight into what actually works—and what doesn't—when deploying AI agents in production. In this episode, we dive into: - The current state of AI agents in production, from successes to common failure modes - Practical lessons learned from analyzing hundreds of real-world LLM deployments - How companies like Anthropic, Klarna, and Dropbox are using patterns like ReAct, RAG, and microservices to build reliable systems - The evolution of LLM capabilities, from expanding context windows to multimodal applications - Why most companies still prefer structured workflows over fully autonomous agents We also explore real-world case studies of production hurdles, including cascading failures, API misfires, and hallucination challenges. Alex shares concrete strategies for integrating LLMs into your pipelines while maintaining reliability and control. Whether you're scaling agents or building LLM-powered systems, this episode offers practical insights for navigating the complex landscape of LLMOps in 2025. LINKS - The podcast livestream on YouTube (https://youtube.com/live/-8Gr9fVVX9g?feature=share) - The LLMOps database (https://www.zenml.io/llmops-database) - All blog posts about the database (https://www.zenml.io/category/llmops) - Anthropic's Building effective agents essay (https://www.anthropic.com/research/building-effective-agents) - Alex on LinkedIn (https://www.linkedin.com/in/strickvl/) - Hugo on twitter (https://x.com/hugobowne) - Vanishing Gradients on twitter (https://x.com/vanishingdata) * Vanishing Gradients on YouTube (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA) * Vanishing Gradients on Twitter (https://x.com/vanishingdata) * Vanishing Gradients on Lu.ma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk)

Our guest today is Başak Eskili, Machine Learning Engineer at Booking.com and C-Founder of Marvelous MLOps. In our conversation, we first dive into MLOps, its key components and how Başak got into the field. We then talk about Marvelous MLOps and her new course: "End to end MLOps with Databricks". Başak finally shares more about her current role at Booking with a focus on building feature stores. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.From beginner to advanced LLM developer course by Towards AI (use the code AISTORIES10 to get a 10% discount): https://academy.towardsai.net/courses/beginner-to-advanced-llm-dev?ref=63e5e3To learn more about Marvelous MLOps: https://www.marvelousmlops.io/End to End MLOps course with Databricks: https://maven.com/marvelousmlops/mlops-with-databricksFollow Başak on LinkedIn: https://www.linkedin.com/in/ba%C5%9Fak-tu%C4%9F%C3%A7e-eskili-61511b58/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ---(00:00) Intro  (02:18) How Başak Got into AI & MLOps  (06:55) Key Components of MLOps  (12:05) Deploying First ML Model  (15:58) Joining Booking.com  (18:11) Best Practices for Building Scalable and Reliable ML Systems  (23:01) Databricks (27:50) Batch vs. Real-Time Predictions  (31:15) Marvelous MLOps  (33:52) Role at Booking.com  (35:45) Feature Stores (45:45) Career Advice  

In this podcast episode, we talked with Isabella Bicalho about Career advice, learning, and featuring women in ML and AI. About the Speaker: Isabella is a Machine Learning Engineer and Data Scientist with three years of hands-on AI development experience. She draws upon her early computational research expertise to develop ML solutions. While contributing to open-source projects, she runs a newsletter dedicated to showcasing women's accomplishments in data science. During this event, the guest discussed her transition into machine learning, her freelance work in AI, and the growing AI scene in France. She shared insights on freelancing versus full-time work, the value of open-source contributions, and developing both technical and soft skills. The conversation also covered career advice, mentorship, and her Substack series on women in data science, emphasizing leadership, motivation, and career opportunities in tech. 0:00 Introduction 1:23 Background of Isabella Bicalho 2:02 Transition to machine learning 4:03 Study and work experience 5:00 Living in France and language learning 6:03 Internship experience 8:45 Focus areas of Inria 9:37 AI development in France 10:37 Current freelance work 11:03 Freelancing in machine learning 13:31 Moving from research to freelancing 14:03 Freelance vs. full-time data science 17:00 Finding first freelance client 18:00 Involvement in open-source projects 20:17 Passion for open-source and teamwork 23:52 Starting new projects 25:03 Community project experience 26:02 Teaching and learning 29:04 Contributing to open-source projects 32:05 Open-source tools vs. projects 33:32 Importance of community-driven projects 34:03 Learning resources 36:07 Green space segmentation project 39:02 Developing technical and soft skills 40:31 Gaining insights from industry experts 41:15 Understanding data science roles 41:31 Project challenges and team dynamics 42:05 Turnover in open-source projects 43:05 Managing expectations in open-source work 44:50 Mentorship in projects 46:17 Role of AI tools in learning 47:59 Overcoming learning challenges 48:52 Discussion on substack 49:01 Interview series on women in data 50:15 Insights from women in data science 51:20 Impactful stories from substack 53:01 Leadership challenges in projects 54:19 Career advice and opportunities 56:07 Motivating others to step out of comfort zone 57:06 Contacting for substack story sharing 58:00 Closing remarks and connections

AI & Triết học: Cùng Là Sự An Ủi, Cùng Là Một Cái Tát |Trần Việt Long, Pencil Philosophy |Từ Tốn Học "Đã bao giờ bạn nghĩ, AI không triệt tiêu tính người trong con người, mà chính nó đang làm sống lại dòng chảy triết học trong con người đó?" Đến với số thứ 02 của TỪ TỐN HỌC, chúng ta sẽ cùng gặp gỡ anh Trần Việt Long, Co-Founder của dự án Triết học Bút chì - Pencil Philosophy. Sau khi tốt nghiệp Đại học Colombia (Mỹ), anh Long từng theo đuổi ngành Phân tích Tài chính và Tư vấn Đầu tư tại "trái tim tài chính" Wall Street (New York). Anh tiếp tục lấy bằng Thạc sĩ Toán thống kê tại ĐH Stanford. Thế nhưng hai điểm dừng tiếp theo của anh lại là ở vị trí Machine Learning Engineer tại TikTok Và Quora. Hiện tại, anh là Co-Founder của dự án Pencil Philosophy & một Tech Startup tại Singapore. Trong tập thứ hai này, chúng ta sẽ cùng host Nga Levi - CoFounder Spiderum - lắng nghe những góc nhìn và chia sẻ của anh Trần Việt Long về cách AI làm rung chuyển thế giới, vẻ đẹp của triết học và cách mà AI và triết học đã, đang và sẽ khai phóng con người. TỪ TỐN HỌC là một series podcast hoàn toàn mới của Spiderum, nhằm cổ vũ tinh thần học tập suốt đời. Chúng tôi nỗ lực đóng gói những bài học cụ thể qua từng câu chuyện với khách mời.

I interviewed Pavel Iakubovskii, Machine Learning Engineer at Hugging Face, on The Ravit Show at YOLO Vision 2024. Pavel shared valuable insights on the intersection of AI and computer vision, alongside Hugging Face's role in this space. In our discussion, Pavel talked about: -- His journey at Hugging Face and what brought him to YOLO Vision 2024 -- The focus of his talk at the event and how it aims to empower the community -- His perspective on the new Ultralytics YOLO11 release and its potential impact on the field -- His thoughts on the future of computer vision and how Hugging Face is contributing to its advancement It was a fascinating conversation filled with forward-thinking insights on AI and the evolving field of computer vision. Stay tuned for more highlights from YOLO Vision 2024! #datashow #ai #yolo2024 #YV24 #theravitshow

Our guest today is Loubna Ben Allal, Machine Learning Engineer at Hugging Face

TechSpective Podcast Episode 139   In the latest TechSpective Podcast, I had the pleasure of speaking with Wilson Tang, a Machine Learning Engineer on Adobe's threat hunting team. Our conversation delved into one of the most exciting and critical areas […] The post Unlocking the Power of AI in Threat Hunting appeared first on TechSpective.

Victoria Kortan, an experienced Machine Learning Engineer and leader in building an AI Center for Excellence shares her journey navigating the AI boom, the impact on her career, and how she's fostering growth by supporting upskilling opportunities, enabling collaboration, and promoting independent projects within her organization.

In this episode, Steven Batifol, a Developer Advocate at Zilliz, discusses his role in fostering the MLOps community, the significance of vector databases like Milvus, and the importance of open source ecosystems. We covered the excitement of developing creative demos, the challenges facing developers in the AI space, and the rapid advancements in LLMs and AI agents. We even learn some trivia about Germany and fax machines!    00:00 Introduction 00:16 Developer Advocacy 01:02 The MLOps Community in Berlin 01:51 Joining Zilliz and Working with Milvus 04:46 Fun and Creative Demos 10:21 Challenges in the AI/ML Community 13:00 The Importance of Open Source 17:02 Upcoming Open Source Summit Presentation 20:14 Future of AI and LLMs 24:24 Conclusion   Guest: Stephen Batifol is a Developer Advocate at Zilliz. He previously worked as a Machine Learning Engineer at Wolt, where he created and worked on the ML Platform, and previously as a Data Scientist at Brevo. Stephen studied Computer Science and Artificial Intelligence. He is a founding member of the MLOps.community Berlin group, where he organizes Meetups and hackathons. He enjoys boxing and surfing.   

ACESS VIDEO VERSION BELOW, PURE AUDIO ABOVE Welcome to Season 11 of the Thoth-Hermes Podcast.  On this first episode, Rudolf meets with guest Karin Valis to expand the growing conversation around Artificial Intelligence (AI) and Occultism.  Karin is Slovakian, a Machine Learning Engineer and former teenage atheist “GO” game fanatic turned occultist  This is to say that Karin is both a brilliant analytical mind and committed spiritual explorer.  Karin is a blogger, conference speaker, musician,  magazine editor and AI-integrated tarot deck creator.  Most recently, she has been engaged in creating a philosophical learning platform designed to counterbalance the funneling manipulation effect of Social Media on societal viewpoint dialogue.  A core question in the conversation today centers on how AI development can offer us insight into our very human development of occultism.  Distinguishing the windows of learning available to us, Karin describes the mapping of semantic space in the meaning-making processes of AI model development.  Karin highlights the parallels between Sacred Alphabet and Sacred Language concepts.  Karin comments on theories of AI as a “super positioned simulacrum” of language model AI functioning as a multiverse generator.   Karin and Rudolf, as both musicians and polyglots, describe the variance of spoken and written human language, embodied and disembodied: and the challenges that this presents to AI as a human tool.    0 – The Fool 10 The Wheel of Fortune 21 – The World 17 – The Star Tarot of The Latent Spaceshttps://mercurialminutes.substack.com/p/tarot-of-the-latent-spaces-by-hermetechnics Far from idealizing AI technology, several times Karin acknowledges the problems of AI: human workforce impact, and the “soulless” nature of AI prose.  Her emphasis is AI as a tool to greater human creativity, as opposed to a replacement.  Resourcing the knowledge of AI development as an opportunity- not replacement- for occult epistemology is an on-going theme in the episode.  Along the way, we hear about Karin's experiences creating a recent tarot deck, a shocking synchronicity encountered during AI-enhanced editing of a shamanic ritual recording, and her recent exploration of AI in the context of Vanessa Sinclair and Carl Abrahamsson's Cut-Up classes. Karin shares regarding her magazine, Gnostic Technology and her involvement with the European Foundation for Shamanic Studies. We hear about her expriences with technological, academic and occult conference audiences.  Karin shares her intent to “soften the gaze” of the “tech bros” -which in response to Rudolf's question- she describes as “complicated” in reception. INTERESTING LINKS Access Karin Valis' Blog And this is her Substack Here are some more direct links: Glitched Encounters with the visual exampleshttps://mercurialminutes.substack.com/p/glitched-encounters Divine Embeddings: The Large Language Models and Sacred Alphabets Talk from the Occulture https://www.youtube.com/watch?v=1inYnG8gNVo And finally, the very exciting Palinode Productions - Deep Leraing Philosophy! https://www.palinode.productions/   Music played in this episode All tracks today are by EVA KADMON! And Eva Kadmon is no one else than our guest today: Karin Valis! Find her music on Spotify: https://open.spotify.com/album/7EToOYFiAgQsNSObqSYuBa?

In this episode, we are joined by Vikas Nair, Co-Founder of Openlayer, a pioneering company in the AI testing and debugging space. Vikas shares his journey from being a Machine Learning Engineer at Apple to founding Openlayer, a platform that helps startups and Fortune 500 companies alike evaluate and monitor their AI models. Vikas discusses the challenges in the machine learning industry and how Openlayer provides solutions with its comprehensive testing, tracking, and monitoring tools. Learn about the key features of Openlayer that enable developers to run tests, track and version models, and monitor production requests efficiently. Discover the impact of Openlayer on the AI development process and how it is making AI evaluation more reliable and accessible for developers. Vikas also shares insights on the future of AI and machine learning, and the role of innovative platforms like Openlayer in shaping this future. Whether you are an AI enthusiast, a developer, or someone interested in the latest advancements in machine learning, this episode offers valuable insights and practical knowledge from one of the leading figures in the industry. This show is supported by www.matchrelevant.com. A company that helps venture-backed Startups find the best people available in the market, who have the skills, experience, and desire to grow. With over a decade of experience in recruitment across multiple domains, they give people career options to choose from in their career journey.

Ron Heichmn is an AI researcher specializing in generative AI, AI alignment, and prompt engineering. At SentinelOne, Ron actively monitors emerging research to identify and address potential vulnerabilities in our AI systems, focusing on unsupervised and scalable evaluations to ensure robustness and reliability. Harnessing AI APIs for Safer, Accurate, & Reliable Applications // MLOps Podcast #252 with Ron Heichman, Machine Learning Engineer at SentinelOne. // Abstract Integrating AI APIs effectively is pivotal for building applications that leverage LLMs, especially given the inherent issues with accuracy, reliability, and safety that LLMs often exhibit. I aim to share practical strategies and experiences for using AI APIs in production settings, detailing how to adapt these APIs to specific use cases, mitigate potential risks, and enhance performance. The focus will be testing, measuring, and improving quality for RAG or knowledge workers utilizing AI APIs. // Bio Ron Heichman is an AI researcher and engineer dedicated to advancing the field through his work on prompt injection at Preamble, where he helped uncover critical vulnerabilities in AI systems. Currently at SentinelOne, he specializes in generative AI, AI alignment, and the benchmarking and measurement of AI system performance, focusing on Retrieval-Augmented Generation (RAG) and AI guardrails. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://www.sentinelone.com/ All the Hard Stuff with LLMs in Product Development // Phillip Carter // MLOps Podcast #170: https://www.youtube.com/watch?v=DZgXln3v85s&ab_channel=MLOps.community --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Ron on LinkedIn: https://www.linkedin.com/in/heichmanron/

David is a passionate investor, and wonderful writer, who shares his insights, and observations in his popular blog. My guest today is David Diranko. We met at Guy Spier's VALUEx earlier this year, and spoke since. David is a very thoughtful investor who writes about his process, which rhymes with my philosophy in many ways, though we might be drawn to different hunting grounds. I greatly appreciate his perspective and insights that combine both his passion for investing, and background in data science and machine learning. David shares his investment research on his blog, http://contrariancashflows.com/ . To gain an edge in the market, he focuses on small companies in overlooked or neglected regions and sectors. When he is not researching companies or writing for his blog, David works as a Data Scientist and Machine Learning Engineer for IBM, and he enjoys spending time with his wife and son. David holds a BSc in Business Mathematics from LMU Munich and an MSc in Mathematics in Data Science from the Technical University of Munich. David's blog: https://contrariancashflows.com/ David's twitter: https://x.com/DavidDiranko David's LinkedIn: https://www.linkedin.com/in/david-diranko/ David shares insights on how to look for temporary issues or challenges in businesses that can be resolved over time. My guest talks about considering the contrarian aspect of investing, going in when others are not interested. David tells me that math and numbers can provide comfort and a framework for investing. My guest talks about thinking probabilistically and considering risk as a subjective factor in investing. We discuss the accelerating shift in technology and how businesses can change rapidly within a few years. David emphasizes the importance of continuous learning and adjusting expectations in investing, especially with a longer time horizon. My guest talks about buying tailwinds at a discount and recognizing optionality for successful investments. We discuss how AI has the potential to empower and enhance human decision-making, but it also has limitations and requires transparency and data ownership. David shares the value of being part of a community of like-minded investors for support, ideas, and feedback. Stay tuned until the end, when my guest talks about how success in investing is not just about financial gains but also about maintaining a balanced and fulfilling personal life. Blue Infinitas Capital, LLC is a registered investment adviser and the opinions expressed by the Firm's employees and podcast guests on this show are their own and do not reflect the opinions of Blue Infinitas Capital, LLC. All statements and opinions expressed are based upon information considered reliable although it should not be relied upon as such. Any statements or opinions are subject to change without notice. Information presented is for educational purposes only and does not intend to make an offer or solicitation for the sale or purchase of any specific securities, investments, or investment strategies.  Investments involve risk and unless otherwise stated, are not guaranteed.  Podcast Program – Disclosure Statement

Today's episode features Raj Shah, a Machine Learning Engineer at Snowflake. Raj built his career talking to various companies and their data teams, showing them how #GenAI (and which tools) could help them reach their goals. Now at Snowflake, he gets to share Snowflake's suite of #GenerativeAI tools, teaching them how to use them to take their business to the next level. Zach and Raj dive even deeper into Snowflake, how it works, and why companies may want to take advantage. Like, Subscribe, and Follow: YouTube: https://www.youtube.com/channel/UCAIUNkXmnAPgLWnqUDpUGAQ LinkedIn: https://www.linkedin.com/company/keyhole-software Twitter: @KeyholeSoftware Find even more Keyhole content on our website (https://keyholesoftware.com/).

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Syed Asad is an Innovator, Generative AI & Machine Learning Engineer, and a Champion for Ethical AI MLOps podcast #233 with Syed Asad, Lead AI/ML Engineer at KiwiTech // Retrieval Augmented Generation. A big thank you to @ for sponsoring this episode! AWS - // Abstract Everything and anything around RAG. // Bio Currently Exploring New Horizons: Syed is diving deep into the exciting world of Semantic Vector Searches and Vector Databases. These innovative technologies are reshaping how we interact with and interpret vast data landscapes, opening new avenues for discovery and innovation. Specializing in Retrieval Augmented Generation (RAG): Syed's current focus also includes mastering Retrieval Augmented Generation Techniques (RAGs). This cutting-edge approach combines the power of information retrieval with generative models, setting new benchmarks in AI's capability and application. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://sanketgupta.substack.com/ Our paper on this topic "Generalized User Representations for Transfer Learning": https://arxiv.org/abs/2403.00584 Sanket's blogs on Medium in the past: https://medium.com/@sanket107 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Syed on LinkedIn: https://www.linkedin.com/in/syed-asad-76815246/

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com Simon Karasik⁠ is a proactive and curious ML Engineer with 5 years of experience. Developed & deployed ML models at WEB and Big scale for Ads and Tax. Huge thank you to Nebius AI for sponsoring this episode. Nebius AI - https://nebius.ai/ MLOps podcast #228 with Simon Karasik, Machine Learning Engineer at Nebius AI, Handling Multi-Terabyte LLM Checkpoints. // Abstract The talk provides a gentle introduction to the topic of LLM checkpointing: why is it hard, how big are the checkpoints. It covers various tips and tricks for saving and loading multi-terabyte checkpoints, as well as the selection of cloud storage options for checkpointing. // Bio Full-stack Machine Learning Engineer, currently working on infrastructure for LLM training, with previous experience in ML for Ads, Speech, and Tax. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Simon on LinkedIn: https://www.linkedin.com/in/simon-karasik/ Timestamps: [00:00] Simon preferred beverage [01:23] Takeaways [04:22] Simon's tech background [08:42] Zombie models garbage collection [10:52] The road to LLMs [15:09] Trained models Simon worked on [16:26] LLM Checkpoints [20:36] Confidence in AI Training [22:07] Different Checkpoints [25:06] Checkpoint parts [29:05] Slurm vs Kubernetes [30:43] Storage choices lessons [36:02] Paramount components for setup [37:13] Argo workflows [39:49] Kubernetes node troubleshooting [42:35] Cloud virtual machines have pre-installed mentoring [45:41] Fine-tuning [48:16] Storage, networking, and complexity in network design [50:56] Start simple before advanced; consider model needs. [53:58] Join us at our first in-person conference on June 25 all about AI Quality

Meet Pau Bajo, Machine Learning Engineer and Educator at Real-World Machine Learning. Pau talks to Saron about transitioning from working daily in Excel to Python, why data is everything, and what skills early developers need to foster if they want a career in machine learning. Show Links Partner with Dev & CodeNewbie! (sponsor) Machine Learning Python Pau's GitHub Pau's Instagram Pau's Twitter

In this episode of the Crazy Wisdom Podcast, Stewart Alsop is joined by Jack Thomas. Jack shares his transitions from nuclear engineering to philosophy and now machine learning, revealing his approach to learning and understanding complex concepts. They explore existential risks and the advantages and challenges of AI, the importance of understanding the mathematics behind machine learning, and the future of programming. Jack also shares his views on the positive and negative impacts of disruptive technologies, and the significance of adhering to ethics and morality when innovating. Check out this GPT we trained on the conversation Timestamps 00:00 Introduction and Guest Background 01:00 The Art of Interviewing and the Impact of Celebrity 02:17 Exploring the Concept of Identity 03:07 The Balance Between Internal and External Identification 04:08 The Influence of Fame and Public Perception 12:43 The Journey of Self-Discovery and Career Transition 16:27 Exploring the World of Machine Learning 19:11 The Role of University in Career Development 19:23 The Path to Becoming a Machine Learning Engineer 28:53 The Intersection of Consciousness and Machine Learning 30:16 The Future of OpenAI and Machine Learning 30:43 The Evolution of Programming and the Role of ChatGPT 31:27 The Shift from Commercialization to Research 31:44 The Intersection of Research, Development, and Venture Capital 32:49 The Future of Machine Learning Engineers 33:09 The Impact of AI on Job Market and Workflows 35:23 The Concept of Mechanistic Interpretability in AI 37:02 The Existential Risks of AI 37:13 The Ethics and Morality in AI Research 38:24 The Influence of Funding on Research Direction 40:04 The Existential Risks of AI: A Deeper Dive 43:13 The Impact of AI on Human Behavior and Society 48:39 The Need for Decentralization in AI 56:02 The Role of Mathematics in Machine Learning 01:00:30 Closing Thoughts and Contact Information Key Insights Existential Risks of AI: The conversation delves into the potential existential risks associated with AI, emphasizing the need for ethical considerations and safeguards to prevent unintended consequences as AI systems become more advanced and autonomous. Balancing Identity Sources: A significant part of the discussion revolves around the importance of finding a balance between internal and external sources of self-identity. This balance is crucial in navigating the complexities of modern society and the influence of technology on personal identity. AI's Potential and Ethical Implications: The episode explores the vast potential of AI in transforming various aspects of society, while also stressing the ethical implications of its development and deployment. It underscores the necessity of ethical frameworks to guide AI research and application. Learning Machine Learning Without Deep Mathematics: Stewart Alsop and Jack Thomas talk about the journey of understanding and applying machine learning without a heavy reliance on deep mathematical knowledge. They advocate for a more intuitive and application-focused approach to learning AI technologies. Disruptive Innovation and Technology: The concept of disruptive innovation is discussed, highlighting how new technologies can fundamentally change industries and societal norms. The conversation touches on the importance of being aware of these changes and adapting to them thoughtfully. Technological Advancement vs. Ethical Considerations: A key theme is the balancing act between leveraging the benefits of technological advancements, such as AI, and ensuring that ethical considerations are at the forefront of these developments to mitigate risks and societal impacts. Practical Applications of AI: The discussion also covers the practical applications of AI and machine learning in various fields, illustrating the transformative potential of these technologies in solving real-world problems while also pondering the ethical boundaries of such applications.

We are so excited today to be joined by Brandon Duderstadt, CEO + Cofounder, and Zach Nussbaum, Machine Learning Engineer, from Nomic AI. They discuss how Nomic AI is building tools like Atlas + GPT4all that enable everyone to interact with AI scale datasets and run models on consumer computers - and - stay tuned for an exciting announcement about their newest product release later in the podcast.Thanks for joining us for the first episode of Season 2 of the MAD Podcast. We will be back to our regular weekly schedule with new conversations with leaders in the Machine Learning, AI and data landscape. If you like this show, you can find the video recording of this episode -- along with many, many more -- on the Data Driven NYC channel on YouTube.NOMIC AIwww.nomic.aitwitter.com/nomic_aiwww.linkedin.com/in/bstadt/www.linkedin.com/in/zach-nussbaum/FIRSTMARKfirstmark.comtwitter.com/FirstMarkCapMatt Turck (Managing Director)www.linkedin.com/in/turck/twitter.com/mattturckData Driven NYC YouTube ChannelFirstMark Capital Eventbrite0:46 - What is Nomic AI & how it got started5:57 - Building GPT4ALL7:23 - Running LLMs on a personal computer16:00 - Nomic Atlas21:33 - Launching Nomic Embed28:10 The Importance of Data in AI31:10 - Benchmarking LLMs32:56 - The Future of Nomic AI36: 22 - Building an AI Startup in New York39:10 - Nomic AI is hiring

In this episode, Jon talks to Jacob Marks, Machine Learning Engineer and Developer Evangelist at Voxel51, whose background is rooted in scientific research having formally been a Ph.D. resident at X the moonshot factory. In this episode, Jacob and Jon unpack the current hype surrounding the implementation of AI in computer vision and software development, assessing its current impact on the industry. Join them as discuss the importance of practical and interactive learning resources and the benefits of contributing to open-source projects.

MLOps Coffee Sessions Special episode with Weights & Biases, Model Management in a Regulated Environment, fueled by our Premium Brand Partner, Weights & Biases. // Abstract Step into the fascinating world of Language Model Management (LLMs) in a Regulated Environment! Join us for an enlightening chat where we'll explore the intricacies of managing models within highly regulated settings, focusing on compliance and effective strategies. This is your opportunity to be part of a dynamic conversation that delves into the challenges and best practices of Model Management in Regulated Environments. Secure your spot today and stay tuned for an enriching dialogue on navigating the complexities of navigating the regulated terrain. Don't miss out on the chance to broaden your understanding and connect with peers in the field! // Bio Darek Kłeczek Darek Kłeczek is a Machine Learning Engineer at Weights & Biases, where he leads the W&B education program. Previously, he applied machine learning across supply chain, manufacturing, legal, and commercial use cases. He also worked on operationalizing machine learning at P&G. Darek contributed the first Polish versions of BERT and GPT language models and is a Kaggle Competitions Grandmaster. Mark Huang Mark is a co-founder and Chief Architect at Gradient, a platform that helps companies build custom AI applications by making it extremely easy to fine-tune foundational models and deploy them into production. Previously, he was a tech lead in machine learning teams at Splunk and Box, developing and deploying production systems for streaming analytics, personalization, and forecasting. Prior to his career in software development, he was an algorithmic trader at quantitative hedge funds where he also harnessed large-scale data to generate trading signals for billion-dollar asset portfolios. Oliver Chipperfield Oliver Chipperfield is a Senior Data Scientist and Team Lead at M-KOPA, where he utilizes his expertise in machine learning and data-driven innovation. At M-KOPA since October 2021, Oliver leads a diverse tech team, making improvements in credit loss forecasting and fraud detection. His career spans multiple industries, where he has applied his extensive knowledge in Python, Spark, R, SQL, and Excel. He also specialized in the building and design of production ML systems, experimentation, and Bayesian statistics. Michelle Marie Conway As an Irish woman who relocated to London after completing her university studies in Dublin, Michelle spent the past 12 years carving out a career in the data and tech industry. With a keen eye for detail and a passion for innovation, She has consistently leveraged my expertise to drive growth and deliver results for the companies she worked for. As a dynamic and driven professional, Michelle is always looking for new challenges and opportunities to learn and grow, and she's excited to see what the future holds in this exciting and ever-evolving industry. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Fine-Tuning LLMs: Best Practices and When to Go Small // Mark Kim-Huang // MLOps Meetup #124 - https://youtu.be/1WSUfWojoe0 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Darek on LinkedIn: https://www.linkedin.com/in/kleczek/ Connect with Mark on LinkedIn: https://www.linkedin.com/in/markhng525/ Connect with Oliver on LinkedIn: https://www.linkedin.com/in/oliver-chipperfield/ Connect with Michelle on LinkedIn: https://www.linkedin.com/in/michelle-conway-40337432

In this episode, I talk with Praveen Kolli about the transformative role of AI in the delivery industry. As a Staff Machine Learning Engineer at DoorDash, Praveen shares insights into how deep learning and ads ranking algorithms are shaping the future of door-to-door services, enhancing user experiences, and optimizing platform efficiency. Join us as we explore the intersection of machine learning and delivery, uncovering the innovations that drive the seamless and efficient delivery experiences we all rely on. Invest in AI Box: https://republic.com/ai-box Get on the AI Box Waitlist: ⁠⁠https://AIBox.ai/⁠⁠ Facebook Community: ⁠https://www.facebook.com/groups/739308654562189 Follow me on X: ⁠⁠https://twitter.com/jaeden_ai⁠⁠

Hugging Face was founded in 2016 and has grown to become one of the most prominent ML platforms. It's commonly used to develop and disseminate state-of-the-art ML models and is a central hub for researchers and developers. Sayak Paul is a Machine Learning Engineer at Hugging Face and a Google Developer Expert. He joins the The post Hugging Face with Sayak Paul appeared first on Software Engineering Daily.

Hugging Face was founded in 2016 and has grown to become one of the most prominent ML platforms. It's commonly used to develop and disseminate state-of-the-art ML models and is a central hub for researchers and developers. Sayak Paul is a Machine Learning Engineer at Hugging Face and a Google Developer Expert. He joins the The post Hugging Face with Sayak Paul appeared first on Software Engineering Daily.

No episódio de hoje temos um time de especialistas que vão nos contar tudo sobre a rotina e os desafios de um Machine Learning Engineer. Eles também compartilham dicas valiosas para quem quer se tornar um Engenheiro de Machine Learning. Se você quer aprender mais sobre essa área, bora ouvir que esse episódio tá INCRÍVEL! Edição completa por Rádiofobia Podcast e Multimídia: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://radiofobia.com.br/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ --- Nos siga no Twitter e no Instagram: @luizalabs @cabecadelab Dúvidas, cabeçadas e sugestões, mande e-mail para o ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠cabecadelab@luizalabs.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ou uma DM no Instagram Participantes: RODRIGO CUNHA | ⁠https://www.linkedin.com/in/rodra-go/⁠ SAMUEL SILVA |⁠ ⁠https://www.linkedin.com/in/samuhs/ JÚLIA SCHUBERT PEIXOTO | ⁠https://www.linkedin.com/in/julia-schubert-peixoto/

Meet Sayak Paul, a Machine Learning Engineer specializing in diffusion models at Hugging Face and GDE for ML and Google Cloud. He shares how his community contributions led him towards getting his current dream job at Hugging Face. Join Ashley, Gus, and Sayak for a chat about resources for developers to get into machine learning, how diffusion models have exploded in the past year, the role of responsible AI and much more.  Resources mentioned: Google Developer Expert Program → https://goo.gle/3S6IVGo TF Hub → https://goo.gle/3S5t9LY Hugging Face →https://goo.gle/45KyBXC Sayak bio and website → https://goo.gle/3Mas9Cv Sayak's Twitter → https://goo.gle/3QtxEO7  Courses: Google Summer of Code→ https://goo.gle/3Fv2CA4 fast.ai course → https://goo.gle/45HRLxp Coursera Deep Learning specialization → https://goo.gle/3S8Kljx CS 231N - Stanford → https://goo.gle/3QvIt3o Books: Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop (Author) → https://goo.gle/493iJm3 Grokking Deep Learning First Edition, by Andrew Trask (Author) → https://goo.gle/40fNX5y   

Adam Hendel started his career in Military Intelligence before transitioning into the private sector as a Machine Learning Engineer for companies like UnitedHealth, C.H. Robinson, and Shipt. Now, as a Founding Engineer at Tembo, Adam spends his time creating extensions and figuring out how to make “Postgres for Everything” a reality. 

The second of three episodes recorded at the AI and Big Data Expo, that we produced in partnership with Exfluency, the AI driven translation and localisation system. The event took place at the RAI in Amsterdam on 26th-27th September . We recorded a series of interviews from the Exfluency booth with a number of the speakers and attendees at the event. Our guests for this episode were: 1/ Shahin Shahkarami, Global Director of Data and Analytics, IKEA 2/ Maarten van den Outenaar, Head of Data, Royal Schiphol Group 3/ Hana Duchackova, Strategic Automation & AI Transformations, Deloitte 4/ Marc Steen, Senior Research Scientist, TNO 5/ Jaromir Dzialo, Chief Technology Officer, Exfluency 6/ Timea Töltszéki, Head of Data and Platforms, Boehringer Ingelheim 7/ Damian Bogunowicz, Machine Learning Engineer, Neural Magic

This episode features Lewis Tunstall, machine learning engineer at Hugging Face and author of the best selling book Natural Language Processing with Transformers. He currently focuses on one of the hottest topic in NLP right now reinforcement learning from human feedback (RLHF). Lewis holds a PhD in quantum physics and his research has taken him around the world and into some of the most impactful projects including the Large Hadron Collider, the world's largest and most powerful particle accelerator. Lewis shares his unique story from Quantum Physicist to Data Scientist to Machine Learning Engineer. Resources to learn more about Lewis Tunstallhttps://www.linkedin.com/in/lewis-tunstall/https://github.com/lewtunReferences from the Episodehttps://www.fast.ai/https://jeremy.fast.ai/SetFit - https://arxiv.org/abs/2209.11055Proximal Policy OptimizationInstructGPTRAFT BenchmarkBidirectional Language Models are Also Few-Shot LearnersNils Reimers - Sentence TransformersJay Alammar - Illustrated TransformerAnnotated TransformerMoshe Wasserblat, Intel, NLP, Research ManagerLeandro von Werra, Co-Author of NLP with Transformers, Hugging Face ResearcherLLMSys - https://lmsys.org/LoRA - Low-Rank Adaptation of Large Language ModelsResources to learn more about Learning from Machine Learninghttps://www.linkedin.com/company/learning-from-machine-learninghttps://mindfulmachines.substack.com/https://www.linkedin.com/in/sethplevine/https://medium.com/@levine.seth.p

MLOps Coffee Sessions #178 with Stephen Batifol, Building an ML Platform: Insights, Community, and Advocacy. // Abstract Discover how Wolt onboard data scientists onto the platform and build a thriving internal community of users. Stephen's firsthand experiences shed light on the importance of developer relations and how they contribute to making data scientists' lives easier. From top-notch documentation to getting-started guides and tutorials, the internal platform at Wolt prioritizes the needs of its users. // Bio From Android developer to Data Scientist to Machine Learning Engineer, Stephen has a wealth of software engineering experience at Wolt. He believes that machine learning has a lot to learn from software engineering best practices and spends his time making ML deployments simple for other engineers. Stephen is also a founding member and organizer of the MLOps.community Meetups in Berlin. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Stephen on LinkedIn: https://www.linkedin.com/in/stephen-batifol/ Timestamps: [00:00] Stephen's preferred coffee [00:32] Takeaways [01:35] Please like, share, and subscribe to our MLOps channels! [03:00] Creating his own team! [04:44] DevRel [06:32] The door dash of Europe [11:28] Data platform underneath [12:55] Cellular core deployment uses open source [14:21] Alibi [16:08] Kafka [16:59] Selling points to data scientists [20:05] Language models concerns of data scientists [22:12] Incorporating LLMs into the business [23:55] Feedback from data scientists and end users [27:37] User surveys [30:11] Evangelizing and giving talks [35:25] Tech Hub Culture in Berlin [38:38] Kubernetes lifestyle [42:55] Interacting with SREs [45:28] Wrap up

In this episode, Karen and Cecilia are joined by Abi Aryan, a Machine Learning Engineer and author for O'Reilly book on LLMOps. Throughout the episode Karen, Cecilia, and Abby open up about their personal experiences working in very small teams. They chat about the pros and cons and dive into the obstacles they've come across in these setups. They tackle the reality of having a smaller collective voice and the significant responsibility of being the sole data representative, which can sometimes feel overwhelming. Such challenges stress the resourcefulness and resilience required in technical roles, as data professionals must often rely on their own expertise and problem-solving skills to navigate complex challenges.  Enjoy!

In this episode Lauren Hawker Zafer is joined by Damien Benveniste PhD⁠  Who is Damien Benveniste PhD? 10 years ago, after a Ph.D. in theoretical Physics, Damien started his career in Machine Learning and Data Science. He has been a Data Scientist, Machine Learning Engineer, and a Software Engineer. In these roles he has led various machine learning projects in diverse industry sectors such as: AdTech, Market Research, Financial Advising, Cloud Management, Online Retail, Marketing, Credit Score Modeling, Data Storage, Healthcare and Energy Valuation.  Recently, he was a machine learning tech lead at Meta on the automation at scale of model optimisation for Ads ranking. He is also a machine learning top voice on LinkedIn. At present, Damien focuses on a more entrepreneurial journey where he builds tech businesses and writes about his experience. If you are interested in reading about his experience then check out his newsletter: ⁠The AI Edge.⁠ Why this Episode?

Spotlight Seventeen is a snippet from our upcoming episode: Damien Benveniste PhD - Machine Learning 101. Listen to the full episode, as soon as it comes out by subscribing to Redefining AI. Who is Damien Benveniste PhD? 10 years ago, after a Ph.D. in theoretical Physics, Damien started his career in Machine Learning and Data Science. He has been a Data Scientist, Machine Learning Engineer, and a Software Engineer. In these roles he has led various machine learning projects in diverse industry sectors such as: AdTech, Market Research, Financial Advising, Cloud Management, Online Retail, Marketing, Credit Score Modeling, Data Storage, Healthcare and Energy Valuation. Recently, he was a machine learning tech lead at Meta on the automation at scale of model optimisation for Ads ranking. He is also a machine learning top voice on LinkedIn. At present, Damien focuses on a more entrepreneurial journey where he builds tech businesses and writes about his experience. If you are interested in reading about his experience then check out his newsletter: The AI Edge. Why this Episode?

Our guest today is Vid Kocijan, a Machine Learning Engineer at Kumo AI. Vid has a Ph.D. in Computer Science at the University of Oxford. His research focused on common sense reasoning, pre-training in LLMs, pretraining in knowledge-based completion, and how these pre-trainings impact societal bias. He joins us to discuss how he built a BERT model that solved the Winograd Schema Challenge.

Introduction: David Hundley is a Machine Learning Engineer who has been deeply involved with experimenting with Large Language Models (LLMs). Follow along on his twitter Key Insights & Discussions: Discoveries with LLMs: David recently explored a unique function of LLMs that acted as a 'dummy agent'. This function would prompt the LLM to search the internet for a current movie, bypassing its training limitations. David attempted to utilize this function to generate trivia questions, envisaging a trivia game powered by the LLM. However, he faced challenges in getting the agent to converge on the desired output. Parsing the LLM's responses into a structured output proved especially difficult. Autonomous Agents & AGI: David believes that AGI (Artificial General Intelligence) essentially comprises autonomous agents. The prospect of these agents executing commands directly on one's computer can be unnerving. However, when LLMs run code, they operate within a contained environment, ensuring safety. Perceptions of AI: There's a constant cycle of learning and revisiting motivations and goals in the realm of AI. David warns against anthropomorphizing LLMs, as they don't possess human motivations. He stresses that the math underpinning AI doesn't align with human emotions or motivations. Emergent Behavior & Consciousness: David postulates that everything in the universe sums up to a collective result. There's debate over whether living organisms possess true consciousness, and what it means for AGI. The concept of AGI emulating human intelligence is complex. The human psyche is shaped by countless historical experiences and stimuli. So, if AGI were to truly replicate human thought, it would require vast amounts of multimodal input. A challenging question raised is how one tests for consciousness in AGI. David believes that as we continue to push technological boundaries, our definition of consciousness will keep shifting. Rights & Ethics of AI: With advancing AI capabilities, the debate around the rights of AI entities intensifies. David also touches upon the topic of transhumanism, discussing the trajectory of the universe and the evolution of humans. He contemplates the potential paths of evolution, like physically merging with technology or digitizing our consciousness. AI's Impact on Coding & Jobs: David reflects on the early days of AI in coding. He acknowledges the transformative potential of AI in the field but remains unworried about AI taking over his job. Instead, he focuses on how AI can aid in problem-solving. He describes LLMs as "naive geniuses" - incredibly capable, yet still requiring guidance. Open Source & OpenAI: David discusses the concept of open source, emphasizing the transparency it offers in understanding the data and architecture behind AI models. He acknowledges OpenAI's significant role in the AI landscape and predicts that plugins like ChatGPT will bridge the gap to further automation. Math's Role in AI: The conversation delves into the importance of math in AI, with David detailing concepts like gradient descent and its role in building neural networks. David also touches on the evolution of AI models, comparing the capabilities of models with 70 billion parameters to those with 7 billion. He predicts that models with even more parameters, perhaps in the trillions, will emerge, further emulating human intelligence. Future Prospects & Speculations: David muses on the future trajectory of LLMs, drawing parallels with the evolution of AlphaGo to AlphaZero. The episode concludes with philosophical musings on the nature of consciousness and its implications on world religions.

MLOps Coffee Sessions #172 with LLMs in Production Conference part 2 Building LLM Products Panel, George Mathew, Asmitha Rathis, Natalia Burina, and Sahar Mor Using hosted by TWIML's Sam Charrington. We are now accepting talk proposals for our next LLM in Production virtual conference on October 3rd. Apply to speak here: https://go.mlops.community/NSAX1O // Abstract There are key areas we must be aware of when working with LLMs. High costs and low latency requirements are just the tip of the iceberg. In this panel, we hear about common pitfalls and challenges we must keep in mind when building on top of LLMs. // Bio Sam Charrington Sam is a noted ML/AI industry analyst, advisor and commentator, and host of the popular TWIML AI Podcast (formerly This Week in Machine Learning and AI). The show is one of the most popular Tech podcasts with nearly 15 million downloads. Sam has interviewed over 600 of the industry's leading machine learning and AI experts and has conducted extensive research into enterprise AI adoption, MLOps, and other ML/AI-enabling technologies. George Mathew George is a Managing Director at Insight Partners focused on venture-stage investments in AI, ML, Analytics, and Data companies as they are establishing product/market Fit. Asmitha Rathis Asmitha is a Machine Learning Engineer with experience in developing and deploying ML models in production. She is currently working at an early-stage startup, PromptOps, where she is building conversational AI systems to assist developers. Prior to her current role, she was an ML engineer at VMware. Asmitha holds a Master's degree in Computer Science from the University of California, San Diego, with a specialization in Machine Learning and Artificial Intelligence. Natalia Burina Natalia is an AI Product Leader who was most recently at Meta, leading Responsible AI. During her time at Meta, she led teams working on algorithmic transparency and AI Privacy. In 2017 Natalia was recognized by Business Insider as “The Most Powerful Female Engineer in 2017”. Natalia was also an Entrepreneur in Residence at Foundation Capital, advising portfolio companies and working with partners on deal flow. Prior to this, she was the Director of Product for Machine Learning at Salesforce, where she led teams building a set of AI capabilities and platform services. Prior to Facebook and Salesforce, Natalia led product development at Samsung, eBay, and Microsoft. She was also the Founder and CEO of Parable, a creative photo network bought by Samsung in 2015. Natalia started her career as a software engineer after pursuing Bachelor's degree in Applied and Computational Mathematics from the University of Washington. Sahar Mor Sahar is a Product Lead at Stripe with 15y of experience in product and engineering roles. At Stripe, he leads the adoption of LLMs and the Enhanced Issuer Network - a set of data partnerships with top banks to reduce payment fraud. Prior to Stripe he founded a document intelligence API company, was a founding PM in a couple of AI startups, including an accounting automation startup (Zeitgold, acq'd by Deel), and served in the elite intelligence unit 8200 in engineering roles. Sahar authors a weekly AI newsletter (AI Tidbits) and maintains a few open-source AI-related libraries (https://github.com/saharmor). // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Alex (Melkey Dev) is a Lead Machine Learning Engineer at Twitch (Amazon). In this episode, he not only shares his journey into the world of AI but also gives invaluable advice on how you can do the very same thing yourself. This refreshing episode has lit a fire under me to go harder than ever in tech, and I hope it will do the same for you. Enjoy! Melkey's Socials: Twitter: https://twitter.com/MelkeyDev YouTube: https://t.co/WGegciszSw Twitch: https://t.co/rbbsKAciU4 --- Support this podcast: https://podcasters.spotify.com/pod/show/chrisseantalks/support

MLOps Coffee Sessions #159 with Maria Vechtomova, Lead ML engineer, and Basak Eskili Machine Learning Engineer, at Ahold Delhaize, Why is MLOps Hard in an Enterprise? co-hosted by Abi Aryan. // Abstract MLOps is particularly challenging to implement in enterprise organizations due to the complexity of the data ecosystem, the need for collaboration across multiple teams, and the lack of standardization in ML tooling and infrastructure. In addition to these challenges, at Ahold Delhaize, there is a requirement for the reusability of models as our brands seek to have similar data science products, such as personalized offers, demand forecasts, and cross-sell. // Bio Maria Vechtomova Maria is a Machine Learning Engineer at Ahold Delhaize. Maria is bridging the gap between data scientists infra and IT teams at different brands and focuses on standardization of machine learning operations across all the brands within Ahold Delhaize. During nine years in Data&Analytics, Maria tried herself in different roles, from data scientist to machine learning engineer, was part of teams in various domains, and has built broad knowledge. Maria believes that a model only starts living when it is in production. For this reason, last six years, her focus was on the automation and standardization of processes related to machine learning. Basak Eskili Basak Eskili is a Machine Learning Engineer at Ahold Delhaize. She is working on creating new tools and infrastructure that enable data scientists to quickly operationalise algorithms. She is bridging the space between data scientists and platform engineers while improving the way of working in accordance with MLOps principles. In her previous role, she was responsible for bringing models to production. She focused on NLP projects and building data processing pipelines. Basak also implemented new solutions by using cloud services for existing applications and databases to improve time and efficiency. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links MLOps Maturity Assessment Blog: https://mlops.community/mlops-maturity-assessment/ The Minimum Set of Must-Haves for MLOps Blog: https://mlops.community/the-minimum-set-of-must-haves-for-mlops/ Traceability & Reproducibility Blog: https://mlops.community/traceability-reproducibility/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Abi on LinkedIn: https://www.linkedin.com/in/goabiaryan/ Connect with Maria on LinkedIn: https://www.linkedin.com/in/maria-vechtomova/Connect with Basak on LinkedIn: https://www.linkedin.com/in/ba%C5%9Fak-tu%C4%9F%C3%A7e-eskili-61511b58/

If you're new to the world of data, you might be curious about what role is right for you - Data Scientist, Analyst, or Machine Learning Engineer. This bonus episode deviates from our usual content to help you get a rough picture of the differences (and similarities) between these roles. Become a Paid Subscriber for access to our full library: ⁠⁠⁠⁠⁠⁠⁠https://podcasters.spotify.com/pod/show/data-science-interview/subscribe⁠⁠⁠⁠⁠⁠⁠ ----------------------------------------------------------- If you enjoy The Data Science Interview Prep Podcast, please consider becoming a premium member on either ⁠Patreon⁠ ($5 donation) or ⁠Spotify⁠ ($2.99 donation). Your donation goes directly to supporting this channel and the human labor that goes into each of these episodes. For each episode, we do research and fact-check our content to make sure that you get the best information possible, even on cutting edge topics. Become a Paid Subscriber: ⁠https://podcasters.spotify.com/pod/show/data-science-interview/subscribe⁠ Becoming a premium member also gives you access to our locked episodes, which include helpful content such as: - NLP - Deep Learning - Recurrent Neural Networks - Imbalanced Data - The Bias-Variance Tradeoff - Transformers in NLP - Self-Attention in NLP - Distributions - Statistics - A/B Testing - ROC-AUC .... and so much more!

Today I'm sitting down with Talha Baig to talk about a new to me organization, the Integrity Institute. On the show, I've spent a lot of time talking about what I see as a new workforce emerging in the tech sector, of people working in jobs in the tech sector to try and understand, assess, and mitigate some of the harms caused by technologies. That's why I was excited to learn about the Integrity Institute, a cohort of engineers, product managers, researchers, analysts, data scientists, operations specialists, policy experts and more, who are coming together to leverage their combined experience and their understanding of the systemic causes of problems on the social internet to help mitigate these problems. They want to bring this experience and expertise directly to the people theorizing, building, and governing the social internet. So I wanted to talk to Talha, who hosts the Trust in Tech podcast out of the institute, about the concept, the function, and the future of integrity work. Talha Baig is an expert on using machine learning to address platform integrity issues. He has spent 3 years as a Machine Learning Engineer reducing human, drugs, and weapon trafficking on Facebook Marketplace. He has insider knowledge on how platforms use AI for both good and bad, and shares his thoughts on his new podcast Trust in Tech, where he has in-depth conversations about the social internet with other platform integrity workers. They discuss the intersections between internet, society, culture, and philosophy with the goal of helping individuals, societies, and democracies to thrive.

Join my fan group here for exclusive news and giveawayshttps://www.facebook.com/groups/theofficialantoniotsmithjrfanclubCatch me live on tour here: https://antoniotsmithjr.comGet ready for a thrilling adventure with the United Cities of Salleria series by Antonio T. Smith Jr. This series follows "The Ghost", Jace, a Special Forces soldier who inadvertently starts World War III while trying to complete his mission. With multiple serial killers on his tail, Jace must save the world before they end his life. Don't miss out on this action-packed series! Check it out now at https://antoniotsmithjr.com/united-cities-of-salleria-series/ #UnitedCitiesofSalleria #AntonioTSmithJr #actionpacked #thriller #mustreadAre you looking to master AI-powered conversations and take your communication skills to the next level? Look no further than "ChatGPT Prodigy: The Definitive Guide to Mastering AI-Powered Conversations" by Antonio T Smith Jr. This comprehensive guide provides practical tips and strategies for communicating effectively with AI language models, such as ChatGPT. Don't miss out on the opportunity to enhance your communication skills and improve your interactions with AI-powered systems. Click the link to learn more and get your copy today.https://antoniotsmithjr.com/chatgpt-prodigy-the-definitive-guide-to-mastering-ai-powered-conversations/In this podcast, a Machine Learning Engineer delves into the role of artificial intelligence (AI) in shaping and enhancing digital experiences in the metaverse. The metaverse, a virtual world with a high degree of immersion and social interaction, is becoming increasingly popular as a new frontier for digital experiences. Intelligent agents powered by AI have the potential to revolutionize the way we interact with technology and each other in the metaverse and beyond. The podcast explores the use of intelligent agents to create personalized companions, virtual personal assistants, automated customer service, emotional support, virtual educators, virtual event hosts, smart environments, intelligent advertising, virtual concierge, and virtual healthcare services. Additionally, the podcast explains how AI-generated content can be used to create immersive and interactive environments in the metaverse. It is important to consider the ethical implications of these technologies and ensure that they are designed and implemented in a way that is transparent, accountable, and respectful of individual privacy and autonomy.Support this podcast at — https://redcircle.com/the-secret-to-success/exclusive-contentAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Looking to master the art of AI-powered conversations? Look no further than ChatGPT Prodigy: The Definitive Guide To Mastering AI-Powered Conversations, written by expert Machine Learning Engineer and Artificial Intelligence Expert Antonio T. Smith Jr.https://antoniotsmithjr.com/chatgpt-prodigy-the-definitive-guide-to-mastering-ai-powered-conversations/This comprehensive guide covers everything you need to know about AI-powered conversations, from breaking down complex concepts to providing practical tips and strategies. Whether you're a beginner or an experienced conversationalist, this book is designed to be a one-stop shop for mastering the art of AI-powered conversations.With ChatGPT Prodigy, you'll learn how to harness the power of artificial intelligence to improve your conversation skills and build stronger connections with others. This book has everything from mastering small talk to navigating difficult conversations.Don't miss out on this valuable resource - get your copy of ChatGPT Prodigy today and take your conversation skills to the next level! Click the link https://antoniotsmithjr.com/chatgpt-prodigy-the-definitive-guide-to-mastering-ai-powered-conversations/Support this podcast at — https://redcircle.com/the-secret-to-success/exclusive-contentAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Machine Learning Engineer is one of the fastest growing professions on the planet.  Liran Hason, co-founder and CEO of Aporia, joins us to discuss this new field and how folks can learn the skills and gain the experience needed to become an ML Engineer!00:00:59 Introductions00:01:44 How Liran got started making websites00:07:03 College advice for getting involved in real-world experience00:12:51 Jumping into the unknown00:15:22 ML engineering00:20:50 The missing part in data science development00:29:16 How to build skills in the ML space00:37:01 A horror story00:41:34 Model loading questions00:47:36 Must-have skills in an ML resume00:50:41 Deciding about data science00:59:08 Rust01:06:27 How Aporia contributes to the data science space01:14:26 Working at Aporia01:16:53 FarewellsResources mentioned in this episode:Links: Liran Hason:Linkedin: https://www.linkedin.com/in/hasuni/ Aporia: Website: https://www.aporia.com/ Twitter: https://twitter.com/aporiaai Linkedin: https://www.linkedin.com/company/aporiaai/ Github: https://github.com/aporia-ai The Mom Test (Amazon): Paperback: https://www.amazon.com/Mom-Test-customers-business-everyone/dp/1492180742 Audiobook: https://www.amazon.com/The-Mom-Test-Rob-Fitzpatrick-audiobook/dp/B07RJZKZ7F References: Shadow Mode: https://christophergs.com/machine%20learning/2019/03/30/deploying-machine-learning-applications-in-shadow-mode/ Blue-green deployment: https://en.wikipedia.org/wiki/Blue-green_deployment Coursera ML Specialization (Stanford): https://www.coursera.org/specializations/machine-learning-introduction Auto-retraining: https://neptune.ai/blog/retraining-model-during-deployment-continuous-training-continuous-testing If you've enjoyed this episode, you can listen to more on Programming Throwdown's website: https://www.programmingthrowdown.com/Reach out to us via email: programmingthrowdown@gmail.comYou can also follow Programming Throwdown on Facebook | Apple Podcasts | Spotify | Player.FM Join the discussion on our DiscordHelp support Programming Throwdown through our Patreon ★ Support this podcast on Patreon ★

4:00 - The ChatGPT app rocked the tech world just before the holidays, providing us a glimpse into the commercial capabilities of Artificial Intelligence. Former Twitter engineer and machine learning expert Nikolai Yakovenko joins us to break down this technology and its anticipated impact on our lives. 47:16 - Aaron Sibarium has been documenting the Woke takeover over critical institutions ranging from medicine and education to corporate American and finance...and even law. How the Culture Wars are impacting more than just "culture".  Learn more about your ad choices. Visit podcastchoices.com/adchoices