Podcasts about cloud automl

Gabi Ferrara and Jon Foust are joined today by fellow Googler Zack Akil to discuss machine learning and AI advances at Google. First up, Zack explains some of the ways AutoML Vision and Video can be used to make life easier. One example is how Google Photos are automatically tagged, allowing them to be searchable thanks to AutoML. Developers can also train their own AutoML to detect specific scenarios, such as laughing in a video. We also talk Cloud Next 2019 and learn how Zack comes up with ideas for his cool demos. His goal is to inspire people to incorporate machine learning into their projects, so he tries to combine hardware and exciting technology to think of fun, creative ways developers can use ML. Recently, he made a smart AI bicycle that alerts riders of possible danger behind them through a system of lights and a project to track and photograph balls as they fly through the air after being kicked. To wrap it all up, Zack tells us about some cool projects he’s heard people use AutoML for (like bleeping out tv show spoilers in online videos!) and the future of the software. Zack Akil When he’s not teaching machine learning at Google, Zack likes to teach machine learning at his hands-on data science meetup, Central London Data Science Project Nights. Although he works in the cloud, most of his hobby projects look at different ways you can embed machine learning into low-power devices like Raspberry Pis and Arduinos. He also likes to have a bit of banter with his mixed tag rugby teams. Cool things of the week Stackdriver Logging comes to Cloud Code in Visual Studio Code blog Open Match v0.8 was released last month site Cloud Spanner now supports the WITH clause blog Interview Zack’s Website site Cloud AutoML site AutoML Video docs AutoML Vision site AutoML Vision Object Detection docs Coral site TensorFlow.js site Central London Data Science Meetup site Question of the week How do I run Cloud Functions in a local environment? Where can you find us next? Zack will be at DevRelCon. Gabi will be taking time to recharge after conference season, then visiting family. Jon will be attending several baby showers. Sound Effect Attribution “Small Group Laugh 4, 5 & 6” by Tim.Kahn of Freesound.org “Sparkling Effect A” by CetSoundCrew of Freesound.org

Our guests Matthew Tamsett and Ravi Upreti join Gabi Ferrara and Aja Hammerly to talk about data science and their project, Qubit. Qubit helps web companies by measuring different user experiences, analyzing that information, and using it to improve the website. They also use the collected data along with ML to predict things, such as which products users will prefer, in order to provide a customized website experience. Matthew talks a little about his time at CERN and his transition from working in academia to industry. It’s actually fairly common for physicists to branch out into data science and high performance computing, Matthew explains. Later, Ravi and Matthew talk GCP shop with us, explaining how they moved Qubit to GCP and why. Using PubSub, BigQuery, and BigQuery ML, they can provide their customers with real-time solutions, which allows for more reactive personalization. Data can be analyzed and updates can be created and pushed much faster with GCP. Autoscaling and cloud management services provided by GCP have given the data scientists at Qubit back their sleep! Matthew Tamsett Matthew was trained in experimental particle physics at Royal Holloway University of London, and did his Ph.D. on the use of leptonic triggers for the detection of super symmetric signals at the ATLAS detector at CERN. Following this, he completed three post doctoral positions at CERN and on the neutrino experiment NOvA at Louisiana Tech University, Brookhaven National Laboratory, New York, and the University of Sussex UK, culminating in a EU Marie Curie fellowship. During this time, Matt co-authored many papers including playing a minor part in the discovery of the Higgs Boson. Since leaving academia in 2016, he’s worked at Qubit as a data scientist and later as lead data scientist where he lead a team working to improve the online shopping experience via the use of personalization, statistics and predictive modeling. Ravi Upreti Ravi has been working with Qubit for almost 4 years now and leads the platform engineering team there. He learned distributed computing, parallel algorithms and extreme computing at Edinburgh University. His four year stint at Ocado helped developed a strong domain knowledge for e-commerce, along with deep technical knowledge. Now it has all come together, as he gets to apply all these learnings to Qubit, at scale. Cool things of the week A developer goes to a DevOps conference blog Cloud Build brings advanced CI/CD capabilities to GitHub blog Cloud Build called out in Forrester Wave twitter 6 strategies for scaling your serverless applications blog Interview Qubit site Qubit Blog blog Pub/Sub site BigQuery site BigQuery ML site Cloud Datastore site Cloud Memorystore site Cloud Bigtable site Cloud SQL site Cloud AutoML site Goodbye Hadoop. Building a streaming data processing pipeline on Google Cloud blog Question of the week How do you deploy a Windows container on GKE? Where can you find us next? Gabi will be at the Google Cloud Summit in Sao Paulo, Brazil. Aja will be at Cloud Next London. Sound Effect Attribution “Small Group Laugh 6” by Tim.Kahn of Freesound.org

On the show today, we speak with Developer Advocate and fellow Googler, Sherol Chen about machine learning and AI. Jon Foust and Aja Hammerly learn about the history and impact of AI and ML on technology and gaming. What does it mean to be human? What can machines do better than humans, and what can humans do better than machines? These are the large questions that we aim to solve in order to understand and use AI. Sherol goes on to explain the types of deep learning machines can achieve, from neural networks to decision trees. Sherol also went into depth about the potential social impact of AI as it assists doctors parsing through medical records and plans agricultural endeavors to maximize food production and safety. Sherol also elaborates on the ethical responsibilities we must realize when developing AI projects. For developers looking to build a new AI project, Sherol outlines the pros and cons of using existing tools like Cloud Speech-to-Text, AutoML and AutoML Tables. Sherol Chen Sherol advocates for Machine Learning for Google Cloud, and works in Research at Google Brain for Machine Learning in Music and Creativity for the Magenta team. She’s taught Artificial Intelligence at Stanford and around the world in six different countries. Her PhD work is in Computer Science, researching storytelling and Artificial Intelligence at the Expressive Intelligence Studio. Cool things of the week AMD EPYC processors come to Google—and to Google Cloud blog Kaggle Petfinder Dataset site Streaming data from Cloud Storage into BigQuery using Cloud Functions blog App Engine Standard Ruby site Thagomizer blog Interview AutoML Tables site AutoML Tables Promo Video video Can Machines Think? article AI Impact Challenge site NeurIPS site ICLR site ICML site Machine Learning Crash Course site TensorFlow site Project Magenta site Cloud Speech-to-Text site Cloud AutoML site Sherol’s Blog blog Question of the week You mentioned that you can run App Engine + Rails, how do you handle migrations? Where can you find us next? Jon will be at PAX Dev and PAX West, the internal game summit at Google in Sunnyvale, and taking some personal time to travel to Montreal. Aja will be hanging around at home, on the internet, and at Seattle.rb. Sound Effect Attribution “Coins 1.wav” by ProjectsU012 of Freesound.org “Wedding Bells.wav” by Maurice_J_K of Freesound.org “Small Group Laugh.wav” by Tim.Kahn of Freesound.org

On the podcast today, we have two more fascinating interviews from Melanie’s time at Deep Learning Indaba! Mark helps host this episode as we speak with Karim Beguir and Muthoni Wanyoike about their company, Instadeep, the wonderful Indaba conference, and the growing AI community in Africa. Instadeep helps large enterprises understand how AI can benefit them. Karim stresses that it is possible to build advanced AI and machine learning programs in Africa because of the growing community of passionate developers and mentors for the new generation. Muthoni tells us about Nairobi Women in Machine Learning and Data Science, a community she is heavily involved with in Nairobi. The group runs workshops and classes for AI developers and encourages volunteers to participate by sharing their knowledge and skills. Karim Beguir Karim Beguir helps companies get a grip on the latest AI advancements and how to implement them. A graduate of France’s Ecole Polytechnique and former Program Fellow at NYU’s Courant Institute, Karim has a passion for teaching and using applied mathematics. This led him to co-found InstaDeep, an AI startup that was nominated at the MWC17 for the Top 20 global startup list made by PCMAG. Karim uses TensorFlow to develop Deep Learning and Reinforcement Learning products. Karim is also the founder of the TensorFlow Tunis Meetup. He regularly organises educational events and workshops to share his experience with the community. Karim is on a mission to democratize AI and make it accessible to a wide audience. Muthoni Wanyoike Muthoni Wanyoike is the team lead at Instadeep in Kenya. She is Passionate about bridging the skills gap in AI in Africa and does this by co-organizing the Nairobi Women in Machine Learning community. The community enables learning, mentorship, networking, and job opportunities for people interested in working in AI. She is experienced in research, data analytics, community and project management, and community growth hacking. Cool things of the week Is there life on other planets? Google Cloud is working with NASA’s Frontier Development Lab to find out blog In this Codelab, you will learn about StarCraft II Learning Environment project and to train your first Deep Reinforcement Learning agent. You will also get familiar some of the concepts and frameworks to get to train a machine learning agent. site A new course to teach people about fairness in ML blog Serverless from the ground up: Building a simple microservice with Cloud Functions (Part 1) blog Superposition Podcast from Deep Learning Indaba with Omoju Miller and Nando de Freitas tweet and video Interview Instadeep site Nairobi Women in Machine Learning and Data Science site Neural Information Processing Systems site Google Launchpad Accelerator site TensorFlow site Google Assistant site Cloud AutoML site Hackathon Lagos site Deep Learning Book book Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization research paper Lessons learned on building a tech community blog Kenya Open Data Initiative site R for Data Science GitHub site and book TWIML Presents Deep Learning Indaba site Question of the week If I want to create a GKE cluster with a specific major kubernetes version (or even just the latest) using the command line tools, how do I do that? GCloud container clusters create site Specifying cluster version site Where can you find us next? Our guests will be at Indaba 2019 in Kenya. Mark will be at KubeCon in December. Melanie will be at SOCML in November.

In our last (but not least!) interview from NEXT, Mark and Melanie talked with Sivan Aldor-Noiman and Erik Andrejko about Wellio, an awesome new platform that combines AI and healthy eating. Wellio was developed as a way to not only educate users on the importance of proper nutrition for well-being but to give them their own personal nutritionist. The data scientists at Wellio started from scratch (pun intended) to create their own food-related database and then began training models so the data could be organized and personalized. Using a combination of human power and machine learning techniques, Wellio learns your preferences, allergies, diets, etc. and will make healthy decisions for you based on these key facts. It chooses recipes, populates a grocery list, and even has the ingredients delivered to your door in time for dinner! Sivan Aldor-Noiman Sivan heads Data Science for Wellio, an early stage startup in the FoodTech space that is helping people eat better. In Wellio, her team delivers models that help inspire, empower and adapt to people’s eating needs, cooking abilities and health constraints. She began her career in the Israeli military serving as an instructor for an anti-tank missile unit (please don’t think Rambo, think more like a classroom teacher). Sivan then transitioned to school and received her undergraduate degree in Industrial Engineering and a Master in Statistics from the Technion, Israel Institute of Technology. She moved to the U.S. to complete a Ph.D. degree in Statistics from The Wharton School, University of Pennsylvania. In her previous job, Sivan ended up leading several Data Science teams and learned that she really liked leading technical people since she got to learn a lot from them. Ultimately, she missed the smaller company mentality, so she is back in the startup world. Sivan was once asked to define herself so here goes: “I am an enthusiastic disagreeable giver and a constant empirical driven learner”. Erik Andrejko Erik has spent his career making a positive impact on the world through mathematics. He is a co-founder and Chief Technology Officer of Wellio - an early stage startup applying AI to the intersection of food and human health. Previously, Erik lead the data science and research organization at The Climate Corporation, which applies data science to solve challenging problems in numerous domains including climatology, agronomic modeling and geospatial applications. When not analyzing interesting datasets, Erik can often be found riding up some incline on a bicycle or cooking. Cool things of the week Summary of Google Cloud Next Tokyo site Deep Learning Indaba GCP Credit Awards site Data Studio and Dataprep are now generally available blog DS: BI analyze more than 500 other data sources via more than 100 partner-built connectors and used by over a million people globally DP: new look, team collab and more analytics features blog Announcing general availability of Cloud Memorystore for Redis blog Coursera Advanced Machine Learning with TensorFlow with GCP blog Webinar on October 9th at 9AM PST to learn more Simplifying ML predictions with Google Cloud Functions blog 50 Best Cloud Security Podcasts site GCP Podcast Episode #100: Vint Cerf: past, present, and future of the internet podcast Interview Wellio site GKE site Cloud Storage site Pub/Sub site Cloud Composer site Cloud ML Engine site Stackdriver site Cloud Functions site TensorFlow site Keras site Scikit Learn site Cloud TPU site Cloud AutoML site Cloud Vision site DevOps201 for Application Developers video Cloud Firestore site Day 3 Keynote: Made Here Together video Spinnaker site Contact Wellio email Questions of the week Is Inbox going away? Inbox is signing off: find your favorite features in the new Gmail blog 5 ways the new Gmail can help you get more done blog Where can you find us next? We’ll both be at Strangeloop. Mark will probably be at Unite L.A. in October. Melanie speaking at Monktoberfest Oct 4th in Portland, Maine.

On this very special episode of the Google Cloud Platform Podcast, we have live interviews from the first day of NEXT! Melanie and Mark had the chance to chat with Melody MeckFessel, VP of Engineering at Google Cloud and Pavan Srivastava of Deloitte. Next we spoke with Sandeep Dinesh about Open Service Broker and Raejeanne Skillern of Intel. Melody Meckfessel Melody Meckfessel is a hands-on technology leader with more than 20 years experience building and maintaining large-scale distributed systems and solving problems at scale. As VP of Engineering, she leads the team building DevOps tools and sharing DevOps best practices across Google and with software development and operations teams around the world. Her team powers the world’s most advanced continuously delivered software, enabling development teams to turn ideas into reliable, scalable production systems. After graduating from UC Berkeley, Melody programmed for startups and enterprise companies. Since joining Google in 2004, Melody has led teams in Google’s core search systems, search quality and cluster management. Melody is passionate about making software development fast, scalable and fun. Pavan Srivastava Pavan is a technology leader with 20 years of experience in developing strategies and implementation of SAP focused technology solutions. Pavan leads Deloitte’s SAP technology capability that focuses on helping clients adopt innovative technology solutions such as cloud and SAP HANA to improve business efficiencies. Pavan has led several engagements helping clients develop strategy, architecture and implement SAP on the cloud and SAP HANA platform. Sandeep Dinesh Sandeep Dinesh is a Developer Advocate for Google Cloud. He blends and creates new opportunities for businesses and people by leveraging the best technology possible. Raejeanne Skillern Raejeanne Skillern is the VP of Data Center and General Manager of Intel’s cloud service provider (CSP) business. Her goal is to make it easier, more cost-effective and more efficient for CSPs to build new infrastructure and services. She is privileged to lead an exceptional team that manages Intel’s business, products and technologies for cloud infrastructure deployments and works closely with the world’s largest cloud providers to ensure Intel’s data center products are optimized for their unique needs. Interviews Cloud AutoML site GKE On-Prem site Melody Meckfessel’s Speaking Schedule at NEXT site DevOps site Google Open Source site Cloud Build site Spinnaker site Kubernetes site Stackdriver site Application Performance Management site OpenCensus site Deloitte site SAP site Deloitte and Google Cloud blog Google Cloud Platform Service Broker site Open Service Broker site Pub/Sub site Cloud Spanner site Intel Cloud Computing site Intel Xeon site Intel Optane DC Persistent Memory site Partnering with Intel and SAP on Intel Optane DC Persistent Memory for SAP HANA blog Where can you find us next? We’ll both be at Cloud NEXT in Moscone West on the first floor! Come by and say hi!

In this episode I’m joined by Jeff Dean, Google Senior Fellow and head of the company’s deep learning research team Google Brain, who I had a chance to sit down with last week at the Googleplex in Mountain View. As you’ll hear, I was very excited for this interview, because so many of Jeff’s contributions since he started at Google in ‘99 have touched my life and work. In our conversation, Jeff and I dig into a bunch of the core machine learning innovations we’ve seen from Google. Of course we discuss TensorFlow, and its origins and evolution at Google. We also explore AI acceleration hardware, including TPU v1, v2 and future directions from Google and the broader market in this area. We talk through the machine learning toolchain, including some things that Googlers might take for granted, and where the recently announced Cloud AutoML fits in. We also discuss Google’s process for mapping problems across a variety of domains to deep learning, and much, much more. This was definitely one of my favorite conversations, and I'm pumped to be able to share it with you. The notes for this show can be found at twimlai.com/talk/124.

Dr. Fei-Fei Li, the Chief Scientist of AI/ML at Google joins Melanie and Mark this week to talk about how Google enables businesses to solve critical problems through AI solutions. We talk about the work she is doing at Google to help reduce AI barriers to entry for enterprise, her research with Stanford combining AI and health care, where AI research is going, and her efforts to overcome one of the key challenges in AI by driving for more diversity in the field. Dr. Fei-Fei Li Dr. Fei-Fei Li is the Chief Scientist of AI/ML at Google Cloud. She is also an Associate Professor in the Computer Science Department at Stanford, and the Director of the Stanford Artificial Intelligence Lab. Dr. Fei-Fei Li's main research areas are in machine learning, deep learning, computer vision and cognitive and computational neuroscience. She has published more than 150 scientific articles in top-tier journals and conferences, including Nature, PNAS, Journal of Neuroscience, CVPR, ICCV, NIPS, ECCV, IJCV, IEEE-PAMI, etc. Dr. Fei-Fei Li obtained her B.A. degree in physics from Princeton in 1999 with High Honors, and her PhD degree in electrical engineering from California Institute of Technology (Caltech) in 2005. She joined Stanford in 2009 as an assistant professor, and was promoted to associate professor with tenure in 2012. Prior to that, she was on faculty at Princeton University (2007-2009) and University of Illinois Urbana-Champaign (2005-2006). Dr. Li is the inventor of ImageNet and the ImageNet Challenge, a critical large-scale dataset and benchmarking effort that has contributed to the latest developments in deep learning and AI. In addition to her technical contributions, she is a national leading voice for advocating diversity in STEM and AI. She is co-founder of Stanford's renowned SAILORS outreach program for high school girls and the national non-profit AI4ALL. For her work in AI, Dr. Li is a speaker at the TED2015 main conference, a recipient of the IAPR 2016 J.K. Aggarwal Prize, the 2016 nVidia Pioneer in AI Award, 2014 IBM Faculty Fellow Award, 2011 Alfred Sloan Faculty Award, 2012 Yahoo Labs FREP award, 2009 NSF CAREER award, the 2006 Microsoft Research New Faculty Fellowship and a number of Google Research awards. Work from Dr. Li's lab have been featured in a variety of popular press magazines and newspapers including New York Times, Wall Street Journal, Fortune Magazine, Science, Wired Magazine, MIT Technology Review, Financial Times, and more. She was selected as a 2017 Women in Tech by the ELLE Magazine, a 2017 Awesome Women Award by Good Housekeeping, a Global Thinker of 2015 by Foreign Policy, and one of the “Great Immigrants: The Pride of America” in 2016 by the Carnegie Foundation, past winners include Albert Einstein, Yoyo Ma, Sergey Brin, et al. Cool things of the week Terah Lyons appointed founding executive director of Partnership on AI article & site Fully managed export and import with Cloud Datastore now generally available blog How Color uses the new Variant Transforms tool for breakthrough clinical data science with BigQuery blog & repo Google Cloud and NCAA team up for a unique March Madness copmetition hosted on Kaggle blog Interview AI4All site, they are hiring and how to become a mentor Cloud AI site Cloud AutoML site Cloud Vision API site and docs Cloud Speech API site and docs Cloud Natural Language API site and docs Cloud Translation API site and docs Cloud Machine Learning Engine docs TensorFlow site, github and Dev Summit waitlist ImageNet site & Kaggle ImageNet Competition site Stanford Medicine site & Stanford Children's Hospital site Additional sample resources on Dr. Fei-Fei Li: Citations site Stanford Vision Lab site Fei-Fei Li | 2018 MAKERS Conference video Google Cloud's Li Sees Transformative Time for Enterprise video Past, Present and Future of AI / Machine Learning Google I/O video Research Symposium 2017 - Morning Keynote Address at Harker School video How we're teaching computers to understand pictures video Melinda Gates and Fei-Fei Li Want to Liberate AI from “Guy with Hoodies” article Dr. Fei-Fei Li Question of the week Where can I learn more about machine learning? Listing of some of the many resources out there in no particular order: How Google does Machine Learning coursera Machine Learning with Andrew Ng coursera and Deep Learning Specialization coursera fast.ai site Machine Learning with John W. Paisley edx Machine Learning Columbia University edx International Women's Day March 8th International Women's Day site covers information on events in your area, and additional resources. Sample of recent women in tech events to keep on radar for next year: Women Techmakers site Lesbians Who Tech site Women in Data Science Conference site Where can you find us next? Mark will be at the Game Developer's Conference | GDC in March.

今週のめんてつニュース いよいよ都内で相乗りタクシーが始まる、日本交通らが実験開始 Slackのプライベート共 … "【第４２回】SpotifyがPodcastに, Cloud AutoML, 徴兵制復活, ネット利用が新聞上回る" の続きを読む

Amy Unruh and Sara Robinson join the podcast this week to talk with Mark and Melanie about the alpha launch of Cloud AutoML Vision. Cloud AutoML is a suite of products enabling developers with limited ML expertise to build high quality models using transfer learning and Neural Architecture Search techniques. AutoML Vision is the first product out the gate with a focus on making it easy to train customized vision models. About Amy Unruh Amy is a developer relations engineer for the Google Cloud Platform, where she focuses on machine learning and data analytics as well as other Cloud Platform technologies. Amy has an academic background in CS/AI and has also worked at several startups, done industrial R&D, and published a book on App Engine. About Sara Robinson Sara is a developer relations engineer on Google's Cloud Platform team, focusing on big data and machine learning. She worked on providing initial product feedback and building a demo for the AutoML Vision launch. Cool things of the week Google Brain Looking Back on 2017 blog Shout-out to Kaz Sato for his TensorFlow Rock Paper Scissors example Running dedicated game servers in Kubernetes Engine blog Kaggle Learn site Honorable mention… - Scientists put a worm brain in a lego robot blog Interview Cloud AutoML: Making AI accessible to every business blog Cloud AutoML Vision site Cloud AutoML Vision Access Request | Whitelist Application form Cloud images example video Shout-out thanks to Rob Carver for domain expertise in helping label cloud images. Coastline images example readme and filenames csv Using Machine Learning to Explore Neural Network Architecture blog Learning Transferable Architecture for Scalable Image Recognition arXiv paper Neural Architecture Search with Reinforcement Learning arXiv paper Progressive Neural Architecture Search arXiv paper Learning2learn video Cloud Vision site docs Question of the week How does someone in academia get GCP credits? Google Cloud Platform Education Grants site Where can you find us next? Melanie is speaking at AI Congress in London Jan 30th and she will be at FOSDEM in Brussels in Feb. Mark will be at the Game Developer's Conference | GDC in March.