Adventures in Machine Learning

Let's be honest. We've all copied and pasted code from the internet. There are many great code sources and in this episode, Ben and Michael discuss how to leverage existing code. They explain how to understand a code base and some best practices for contribution.  Sponsors Chuck's Resume Template Developer Book Club starting with Clean Architecture by Robert C. Martin Become a Top 1% Dev with a Top End Devs Membership

Corey Zumar talks about the new release of MLflow, 2.0, and what the new major features that are included in the release. Bilal and Corey then discuss managing feature implementation priorities, and selling large-scale project ideas to internal customers, end-users, executives, and the dev team. The discussion also centers around generalizing feature requests to implementations that will work for the masses and how to effectively do prototype releases for incremental agile development for complex projects.  Sponsors Chuck's Resume Template Developer Book Club starting with Clean Architecture by Robert C. Martin Become a Top 1% Dev with a Top End Devs Membership LinksGitHub: Corey-Zumar

Do you multitask? If so, you'll want to check out this episode. We'll cover... The difference between good and bad context switching Some personal stories about context switching on projects Practical tips you can leverage to improve your productivity Sponsors Chuck's Resume Template Developer Book Club starting with Clean Architecture by Robert C. Martin Become a Top 1% Dev with a Top End Devs Membership

  Get the Black Friday/Cyber Monday "Focus Blocks Bundle" Deal Coupon Code: "THRIVE" for a GIANT discount   Are you looking at all the layoffs and uncertainty going on and wondering if your company is the next to cut back?  Or, maybe you're a freelancer or entrepreneur who is trying to figure out how to deliver more value to gain or retain customers?  Mani Vaya joins Charles Max Wood to discuss the one thing that both of them use to more than double their productivity on a daily basis.  Mani has read 1,000's of productivity books over the last several years and has formulated a methodology for getting more done, but found that he lacked the discipline to follow through on his plans.  The he found the one thing that kept him on track and made him so productive that he is now getting all of his work done and was able to live the life he wants.  Chuck also weighs in on how Mani's technique has worked for him and allows him to spend more time with his wife and kids, run a podcast network, and a nearly full time contract.  Join the episode to learn how Chuck and Mani get into a regular flow state with their work and consistently deliver at work. 

Have you ever wondered how to prioritize your ML projects? Today we will talk about... The difference between applied ML and ML frameworks How to effectively scope projects via Heilmeier's Catechism Sponsors Chuck's Resume Template Developer Book Club starting with Clean Architecture by Robert C. Martin Become a Top 1% Dev with a Top End Devs Membership

Have you ever wondered how to efficiently learn topics? In this episode, we discuss how to conduct a research spike within an ML team setting. Sponsors Chuck's Resume Template Developer Book Club starting with Clean Architecture by Robert C. Martin Become a Top 1% Dev with a Top End Devs Membership

Have you ever wondered why data science is hard? Well, in this episode we cover some common data science challenges and how the founders of DagsHub are looking to solve them.  Sponsors Top End Devs Coaching | Top End Devs LinksDean Pleban

In this show, we cover some practical tips for writing reliable ML code. Here are some of the questions we look to answer... What are tests and why should you use them? What's the difference between unit tests and integration tests? What should you test? How should you write tests in python? (the answer is to use pytest) Sponsors Top End Devs Coaching | Top End Devs Enov8, who provides test data management

Charles Simon, BSEE, MSCs is a nationally recognized entrepreneur and software developer who has many years of computer experience in industry including pioneering work in artificial intelligence (AI).  Mr. Simon's technical experience includes the creation of two unique AI systems along with software for successful neurological test equipment combining AI development with biomedical nerve signal testing that gives him the singular insight.  Today on the show, Charles, Michael, and Ben explore the riveting future of AGI and other illuminating technology concepts.  This is an exciting episode you won't want to miss! In this episode… Charles Simon's extensive background Classical algorithms vs manual design Knowledge and power generated in our brains AI and ML concepts and learning patterns Graph based approaches and deep learning “Terminator” and AGI Future AI and open source simulator Leveraging technology to solve problems  Sponsors Top End Devs Coaching | Top End Devs Links The Team LinkedIn: Charles Simon

Fernando Lopez joins the show today to share his ML insights with a video interview recruiting platform for candidate hiring.  Michael and Ben also deep dive into various related ML models and AI topics. In this episode… Core software engineering skills Practice data algorithms and structures  Working towards production-grade ML Data engineering and data structures Using state-of-the-art models  Applying labels to data The AI revolution  Eliminating bias and unweighted data set Unconscious and conscious exercises Complex models with structure Sponsors Top End Devs Coaching | Top End Devs Links How to Package and Distribute Machine Learning Models with MLFlow - KDnuggets Twitter: @ferneutronn

Today the panel discusses high level distributed time series models, using a hot dog stand company as the case study to anchor the understanding with these models. In this episode… Understanding use case ML flow models and events KPI forecasts Metadata outputs Prediction intervals for hotdog data Automated time series forecasts Libraries required for optimization Practical tips managing the data and Setting up the data for consumption Managing black swan events Sponsors Top End Devs Coaching | Top End Devs

Today on the show, the panel discusses time series models, practical tips and tricks, and shares stories and examples of various models and the processes for optimal application in your ML workflows. In this episode… Ben's time series model for sales forecasting The flat line model Examples using time series models Understanding your data Lag functions and moving averages  Signal processing in models Manually adding change points Deep learning in time series models  Sponsors Top End Devs Coaching | Top End Devs

Optical character recognition, or OCR for short, is used to describe algorithms and techniques (both electronic and mechanical) to convert images of text to machine-encoded text.  Today on the show, Ahmad Anis shares how he applies Machine Learning to OCR for small hardware applications, for example, blurring a face in a video in real time or on a stream to safeguard privacy using AI.  The panel also discusses various strategies related to learning and soft skills needed for success within the industry. In this episode… Optical character recognition (OCR) defined Multiprocessing vs. multithreading  I/O bound tasks vs. CPU tasks How to handle a retry in Python Strategies for employing on small hardware  Template matching and preprocessing  Gray scaling integrations How to learn and get started within the industry Reducing the scope and industry soft skills  Sponsors Top End Devs Coaching | Top End Devs Links LinkedIn: Ahmad Anis Twitter: @AhmadMustafaAn1

Award winning data evangelist, AI strategist, and innovation leader Vidhi Chugh joins the show today to share her perspective on various topics, including data quality, AI innovation strategies, responsible AI, model intelligence, and much more! In this episode… The importance of innovation Glorified failure projects  Responsible AI Data driven companies and quality scores Tools for autogenerated business insights Model intelligence  Unspoken assumptions Understanding the larger picture Sponsors Top End Devs Coaching | Top End Devs Links Benefits Of Becoming A Data-First Enterprise - KDnuggets Top 3 Challenges for Data & Analytics Leaders - KDnuggets Democratizing Data in Large Enterprises LinkedIn: Vidhi Chugh

Enjoy this intellectually stimulating conversation with Michael Berk and guest on the show, Aliaksei Mikhailiuk, ML/AI engineer at Snapchat as they discuss everything AI computer graphics to techniques on striking the efficiency-accuracy trade-off for deep neural networks on constrained devices.In this episode… From academics to machine learning Machine learning on mobile devices Cloud computing Applying AI to computer graphics Being multi-disciplinary  User experience and latency Model complexity issues Sponsors Top End Devs Coaching | Top End Devs Links Deep Video Inpainting. Removing unwanted objects from videos… | by Aliaksei Mikhailiuk | Towards Data Science On the edge — deploying deep learning applications on mobile | by Aliaksei Mikhailiuk | Jul, 2022 | Towards Data Science Seven Questions to Ask before Introducing AI into Your Project | by Aliaksei Mikhailiuk | Towards Data Science Aliaksei Mikhailiuk Aliaksei Mikhailiuk – Medium LinkedIn: Aliaksei Mikhailiuk Twitter: @mikhailiuka

Adam Ross Nelson helps current and aspiring data professionals enter and level up in the field by uncovering and showcasing their existing data-related talents.  Today on the show, Michael interviews Adam to share his various strategies and approaches on how to become a data scientist or make advanced changes in the data science career path. In this episode… The “distributed portfolio approach” vs. professional portfolio  Leveraging each platforms strengths  Creating a personal brand Find others to network and connect with Create a Rosetta stone in your programming language Pandas-profiling to contribute to the community Working with a career coach Asking the right interview questions Sponsors Top End Devs Coaching | Top End Devs Links Data Science Projects Accepting Community Contributions | by Adam Ross Nelson | May, 2022 | Towards Data Science Adam Ross Nelson LinkedIn: Adam Ross Nelson Twitter: @AdamRossNelson

Michael Berk interviews Ken Youens-Clark today to discuss various topics including bioinformatics and programming, plus his career progressions including jazz drumming, technical writing, programming, academia, writing books, and solutions engineering.   In this episode… Writing tests and type annotations Project development lifecycle Grading programmers with pass / fail Soft skills within the industry Bioinformatics and computer science Prototyping and improving efficiencies Connect with Ken Youens-Clark via email and LinkedIn Sponsors Top End Devs Coaching | Top End Devs Links Mastering Python for Bioinformatics Command-Line Rust GitHub - kyclark/command-line-rust LinkedIn: Ken Youens-Clark GitHub: kyclark

Data excellence is the foundation of better AI.  Today on the show, Michael Berk interviews Edouard d'Archimbaud, co-founder of Kili Technology, a Training Data Platform that turns raw, unstructured data to high-quality training data, at scale.  Enjoy this engaging conversation about building AI responsibly on a foundation of good data. In this episode… What is cautious AI and trustworthy AI? Defining clean data vs. diverse data Image classification How was Kili founded? How does the labeler and model work together? Algorithms and model structures   Scaling with large data sets Sponsors Top End Devs Coaching | Top End Devs Links Kili Technology Blog posts LinkedIn: Edouard d'Archimbaud

Jesse Langford spent the first half of his career as a golf instructor before pivoting to software engineering.  Today on the show, Ben interviews Jesse to learn why and how he made this pivot, plus relevant career advice for all developers.  Specific topics include taking ownership of your work, being comfortable making mistakes, and how to stretch yourself every day. In this episode… Taking ownership Being comfortable taking risks and making mistakes From back end to front end development Self taught and self paced Dunning-Kruger effect Continually growing and learning Sponsors Top End Devs Coaching | Top End Devs Links The Most Important Thing I Did to Become a Senior Developer Jesse Langford LinkedIn: Jesse Langford

When developing ML models, defining and selecting the model architecture will be fundamental to ensure the best possible outcomes.  Parameters that define the model architecture are referred to as hyperparameters and the process of searching for the ideal model architecture is referred to as hyperparameter tuning.  Today on the show, Ben and Michael discuss hyperparameter tuning and how to implement this into your ML modeling. In this episode… Why do we tune? Optimizing the models Hyperparameter tuning Steps for tuning Data splits Linear based models How do you know when you know enough? Basic rules of thumb Buffer in time for spikes Grid searching and automation Sponsors Top End Devs Coaching | Top End Devs

Enjoy this engaging AMA conversation with Michael Berk asking Ben Wilson various questions related to industry, strategy, and approaches in data science and ML engineering.In this episode… Why should people trust you? What will you lose by hearing about other people's failures vs. personally failing to learn? How do you view the current industry? Do you think data scientists and ML engineers are overpaid? What are 3 things important to ROI? How do you set the tone for culture? Sponsors Top End Devs Coaching | Top End Devs LinksMachine Learning Engineering in Action

Ben and Michael interview Maciej Balawejder, a mechanical engineering student passionate about AI, ML, and robotics.  As an active contributor on Medium.com, Maciej has already made significant contributions to the AI and ML communities.  On the show, they discuss Maciej's recent article about optimizers in Machine Learning, plus their personal philosophies and approaches to deep learning.Sponsors Top End Devs Coaching | Top End Devs Links Maciej Balawejder - Medium Optimizers in Machine Learning

After ensuring your data has surpassed the hyper parameter tuning phase, what is the next step in your EDA protocol? Today on the show, Ben and Michael continue the discussion on EDA methodology within Machine Learning and discuss linear regression with OLS, decision trees, and common visualization tools for data scientists. In this episode... Linear regression with OLS Accuracy metrics Decision trees Shapley values Common visualization tools Sponsors Top End Devs Coaching | Top End Devs

EDA is primarily used in machine learning to see what data can reveal beyond the formal modeling or hypothesis testing task and provides a better understanding of data set variables and the relationships between them. It can also help determine if the statistical techniques you are considering for data analysis are appropriate. Today on the show, Ben and Michael discuss how to use EDA in machine learning models. In this episode... What is EDA? Tips and Tricks and steps for EDA How to approach downsampling Understanding feature sets relative to your labels Optimizing models Motivating yourself to get into the data Tools for EDA A few scenarios for discussion What is the most detrimental EDA mistake for ML Sponsors Top End Devs Coaching | Top End Devs

MLlib is Apache Spark's scalable machine learning library. Today, Ben and Michael discuss the ease of use, performance, algorithms, and utilities included in this library and how to execute the best ML workflow with MLlib. In this episode... Why stick with Spark libraries vs. a single node operation? What algorithms are not in Spark Lib? What is the min. package set to use for supervised learning? Modeling and validation Down-sampling your data MLlib vs. scikit-learn Resources Sponsors Top End Devs Coaching | Top End Devs Links MLlib | Apache Spark What is PySpark? | Domino Data Science Dictionary UCI Machine Learning Repository

Apache Spark is a lightning-fast unified analytics engine for large-scale data processing and machine learning. In this episode, Ben and Michael unpack Spark by ping-ponging questions and answers, supplemented by various examples applicable to machine learning workflows. In this Episode… How does Spark work? What makes Apache Spark effective? Dot repartition in Spark Parallel processing systems What is an aggregation in Spark sequel? Analytics with Spark What is MPP? Testing for production Spark algorithms Sponsors Top End Devs Coaching | Top End Devs Sponsored By: Coaching | Top End Devs: Do you want to level up your career? or go freelance? or start a podcast or youtube channel? Let Charles Max Wood Help You Achieve Your Dreams Top End Devs: Learn to Become a Top 5% Developer. Join our community of ambitious and engaged programmers to learn how.

Ben and Michael walk through two different cases studies relative to production ML infrastructure and recommendation engines. The first is about a free on-line tutoring service for underserved communities called “Learn to Be”, and the second centers around the online course provider “Coursera”. Ben and Michael set up the case studies with fundamental problem statements, followed by their various approaches to executing the objectives to achieve the desired process outcomes. Sponsors Top End Devs Coaching | Top End Devs Sponsored By: Coaching | Top End Devs: Do you want to level up your career? or go freelance? or start a podcast or youtube channel? Let Charles Max Wood Help You Achieve Your Dreams Top End Devs: Learn to Become a Top 5% Developer. Join our community of ambitious and engaged programmers to learn how.

Ben interviews Michael Griffiths, Director of Data Science at ASAPP, a company leveraging AI and ML to augment and automate human work, improve operational efficiencies and customer experiences, and ultimately empower people to be their best. Michael shares specific examples of how this can be done for human agent productivity within contact centers. They also discuss fully human controlled vs automated systems, delivering value with AI and ML, and the future of AI driven technology. In this Episode… How do you deliver value with AI and ML? Fully human controlled vs. fully automated systems ML software engineering vs traditional software Using static training data models and data validation Communicating process improvements and failures What is the future of ASAPP and AI driven technology? Sponsors Top End Devs Coaching | Top End Devs Links How a Level System can Help Forecast AI Costs - KDnuggets AI Research - ASAPP ASAPP Special Guest: Michael Griffiths.Sponsored By: Coaching | Top End Devs: Do you want to level up your career? or go freelance? or start a podcast or youtube channel? Let Charles Max Wood Help You Achieve Your Dreams Top End Devs: Learn to Become a Top 5% Developer. Join our community of ambitious and engaged programmers to learn how.

AutoML (automated machine learning) has become a hot topic over the past few years. Abid Ali Awan joins the show to share his approach to AutoML, when and how to utilize it compared to classic approaches. Ben and Abid also discuss open-source vs. proprietary platforms. What is AutoML? Automated machine learning provides methods and processes to make machine learning available for non-machine learning experts, to improve efficiency of machine learning and to accelerate research on machine learning. 2 levels of implementation: Blackbox AutoML can do one, or all of the things for feature selection with a statistical outset and self optimizing outcome. Whitebox AutoML exposes the code to explain how it behaves and allows you to produce predictions as to the influencing variables. Leveraging open-source toolkits vs. proprietary “The output is the model, the input is the data, and you can use that model to predict anything according to your business problem.” - Abid Ali Awan Sponsors Top End Devs Coaching | Top End Devs Links H2O.ai Special Guest: Abid Ali Awan .Sponsored By: Coaching | Top End Devs: Do you want to level up your career? or go freelance? or start a podcast or youtube channel? Let Charles Max Wood Help You Achieve Your Dreams Top End Devs: Learn to Become a Top 5% Developer. Join our community of ambitious and engaged programmers to learn how.

Video is considered the most complicated data to process and the volumes of video production are growing from day to day. Ben and Michael talk with Oleg and Anastasiya about how to leverage robotics and advanced cognitive computing-based video processing algorithms to automate the most routine parts of editing and post-production. Specifically, they discuss American sports such as the NFL, NBA, and NHL, and how to use AI to automate sports highlight reels can automate content post-processing video analytics to save time and streamline employee workflows. This is an exciting video you won't want to miss! Sponsors Top End Devs Coaching | Top End Devs Special Guest: Anastasiya Vishnevskaya.Sponsored By: Coaching | Top End Devs: Do you want to level up your career? or go freelance? or start a podcast or youtube channel? Let Charles Max Wood Help You Achieve Your Dreams Top End Devs: Learn to Become a Top 5% Developer. Join our community of ambitious and engaged programmers to learn how.

Michael and Ben talk about how to pick extra projects to build up your resume and become recognized as more of an expert. They discuss the specific ways to contribute within the community and who to interact with to strengthen your resume if you're new. Sponsors Top End Devs Coaching | Top End Devs Sponsored By: Coaching | Top End Devs: Do you want to level up your career? or go freelance? or start a podcast or youtube channel? Let Charles Max Wood Help You Achieve Your Dreams Top End Devs: Learn to Become a Top 5% Developer. Join our community of ambitious and engaged programmers to learn how.

Machine learning is getting bigger by the second, so it's good to know how to leverage it. In this episode, Michael asks Ben hypothetical questions around how to effectively deploy machine learning in multiple fields, including the stock market. In This Episode 1) How to get started in the stock market without having gobs of hedge fund money 2) The ONE thing you NEED to know before even starting a project in this space (or you're wasting your time AND money) 3) The BEST way to leverage open source software and have a competitive advantage Sponsors Top End Devs Coaching | Top End Devs Sponsored By: Coaching | Top End Devs: Do you want to level up your career? or go freelance? or start a podcast or youtube channel? Let Charles Max Wood Help You Achieve Your Dreams Top End Devs: Learn to Become a Top 5% Developer. Join our community of ambitious and engaged programmers to learn how.

What happens when you teach ML and data science to kids? You learn a whole lot, too. In this episode, Ben and Michael sit down with Kathryn, a prolific writer and author who simplifies advanced concepts for kids to foster their passion for science. “I just love how curious kids are. I really connect with the questions they ask and how curious they are, so that's why I love writing for them.” - Kathryn Hulick In This Episode 1) What you NEED to remember when trying to teach complicated topics to kids 2) The BIGGEST misconception around AI that kids need to understand 3) How we can approach ML and data science to learn better (and how kids already do this!) Sponsors Top End Devs Coaching | Top End Devs Special Guest: Kathryn Hulick.Sponsored By: Coaching | Top End Devs: Do you want to level up your career? or go freelance? or start a podcast or youtube channel? Let Charles Max Wood Help You Achieve Your Dreams Top End Devs: Learn to Become a Top 5% Developer. Join our community of ambitious and engaged programmers to learn how.

Even an amazing algorithm can't fix communication problems. In this episode, Ben and Michael sit down with Joe Reis, a data scientist and ML developer who's passionate about helping people level up their communication and build solid business infrastructure. “I feel like the infrastructure piece is getting better. Once you get past the technical layer, it's about basic things like communication, no matter how much money is thrown into it.” - Joe Reis In This Episode 1) SUPER exciting trends for data science for the first time in 10+ years 2) Why you NEED to have a marketing-focused approach in the ML space nowadays 3) The BIGGEST things you should consider when hiring and scaling to keep you business infrastructure intact Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Links Joe Reis ( JoeReis ) (https://github.com/JoeReis)

Ever feel like you can't see the forest through the trees? We get it. In this episode, Michael sits down with Maria Zentsova, an ML developer and data analyst who teaches us how to get a handle on our data. “We all know that more data leads to more accuracy, so it's important to get hands on.” - Maria Zentsova In This Episode 1) What KDNuggets is making data science and machine learning easier this year 2) The Do's and Don'ts of data feeds and analysis in 2022 3) Why understanding this ONE issue is the key to avoiding relevance issues and finding your way out of the weeds Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Links Build a Serverless News Data Pipeline using ML on AWS Cloud - KDnuggets (https://www.kdnuggets.com/2021/11/build-serverless-news-data-pipeline-ml-aws-cloud.html) Special Guest: Maria Zentsova.

In this episode, Ben and Michael cover more of Shreya Shankar's deep dive into ML monitoring, including the biggest production challenges, what you NEED to know about adversarial attacks, and how to conduct effective tests and never make past mistakes again. In This Episode 1) The BIGGEST production challenges when it comes to ML in 2022 2) What you NEED to know about adversarial attacks (and how to keep your model secure) 3) How to know if your model tests are actually preventing past mistakes Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching)

If you're feeling a little nervous about your baby leaving the nest, we get it. In this episode, Ben and Daniel talk with Abhilash Pattnaik, where they discuss the ONE fact about ML you can't forget, the do's and don'ts about applying alerts, and the often-forgotten truth about data drift. “Machine learning models are not static; they are dynamic.” - Abhilash Pattnaik In This Episode: 1) This one UNFORGETTABLE fact for anyone entering the machine learning world (especially if you have little experience) 2) What you NEED to remember about how to measure data accuracy and relevance 3) The Do's and Don'ts of applying alerts within your models (and how NOT to make it problematic) 4) The often-ignored truth around data drift and why companies react well or poorly Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Links Censius (https://censius.ai/) Special Guest: Abhilash Pattnaik.

Ready to dive DEEP into predictive modeling? You've come to the right podcast. In this episode, Ben and Michael sit down with Maarit Widmann, a data scientist whose bread and butter is making models more accurate. They discuss how to effectively use confusion matrices and other tools, why you need to avoid THIS misconception to get accurate churn rates, and the BIG question you should be asking if your data seems off. “If your model is failing, it's probably been adding up for two weeks already. Instead of monitoring the accuracy, monitor the features. Ask ‘is the data changing?'” - Maarit Widmann In This Episode 1) The THREE techniques to help simplify these advanced concepts for beginners in the ML space (and why they need to know this stuff!) 2) Why you need to remember THIS misconception to avoid getting inaccurate churn rates from your models 3) How to effectively use confusion matrices, down sampling, and other popular tools for your models (and the biggest mistakes developers are making today) 4) The REAL question you need to ask yourself if your data seems off (and how this question helps prevent fraud!) Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Special Guest: Maarit Widmann.

Mo' advancements mean mo' problems, and today, Michael and Ben are diving into the biggest issues of ML monitoring in 2022. They lay out the ELEVEN (cause nothing good comes easy) potential pitfalls that you should know this year, the important questions that you NEED to ask yourself before launching your baby, and this ONE phenomenon that reveals a fundamental flaw in models versus the real deal. In This Episode The checklist of ELEVEN problems with ML monitoring that you need to consider in 2022 Why THIS is inevitable with ML monitoring no matter what (and why it's not the end of the world…unless you make it) Before you make an assumptions about your model, ask yourself THESE questions and tread carefully This ONE phenomenon that reveals a HUGE flaw in models vs. real world Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching)

If you're looking for a team that actually cares about the issues you're facing, look no further than Databricks, and they've got something exciting out. In this episode, Michael and Ben welcome on the development team of MLflow, an open-source lifecycle manager for machine learning. They cover how Databricks is redefining how developers and engineers collaborate, the reason behind Databricks' crazy success, and the number ONE most important testing structure for any development team. “A lot of the success was attributed to process and dedicated focus on the interface, understanding what major problems we were going after. ” - Corey Zumar In This Episode How Databricks allows data analysis, engineers, and developers to collaborate effectively Why Databricks was able to rake in 800,000 downloads per MONTH in their first year A simple but powerful methodology that helps Databrick identify the highest ROI problems to tackle (not just the most popular ones) The number one MOST important testing structure that reveals how Databricks keeps their work top-notch What makes Databricks unique from everyone else and is the KEY to putting users first in 2022 Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching) Special Guests: Corey Zumar, Harutaka Kawamura, Weichen Xu, and Zhang Jin.

What do cows and camels have to do with the human brain? The latest developments in machine learning, of course! In this episode, Michael and Ben dive into a new white paper from Facebook AI researchers that reveals a LOT about the future of modeling. They discuss “cows and camels”, the question of predictive vs causal modeling, and how algorithms are getting scary good at emulating the human brain these days. In This Episode Why Facebook's new research is VERY exciting for AI learning and causality (but what does it have to do with cows and camels?) The answer to “Is predictive or causal modeling more accurate?” (and why it's not the best question to ask) Not sure if you need machine learning or just plain data modeling? Michael lays it out for you What algorithms are learning about human behavior to accurately emulate the human brain in 2022 and beyond Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching)

Want to know how businesses are measuring their success nowadays? In this episode, Ben talks with Michael Berk about how businesses are leveraging numerous performance metrics to stay on top of their game. The two emphasize what your FIRST step should be, why A/B testing is not just for marketers, and the “elephant in the room” that determines how KPIs should be integrated. “It's the chicken or the egg problem. It's important to think about what model you'll potentially use as you build out these pipelines. Step 1 is defining the business objective.” - Michael Berk In This Episode: Before you focus on the pipeline or the model, Michael warns you to put THIS first on your checklist Why A/B testing is SO powerful and not just for marketers The #1 takeaway that will change how you implement KPIs Michael hammers home why “your data is only as good as your model” and why there's no shortcut around it The “elephant in the room” that determines the what, who, and how of data analysis in any business

Michael and Ben talk about Prediction Intervals. They discuss how to put intervals around your predictions and then aiming for a certain level of confidence. Michael and Ben discuss how to put it together and how to use it. Panel Ben Wilson Michael Berk Sponsors Top End Devs (https://topenddevs.com/) Coaching | Top End Devs (https://topenddevs.com/coaching)

Get Lifetime Access to Mani's Entrepreneurship Pack and Book Club. Use coupon code "GREAT" Mani has summarized hundreds of business books that outline how to build, grow, and operate a business and he shares his expertise with Chuck and the listeners in this special episode. Chuck and Mani discuss what it takes to be a successful entrepreneur. They talk about their businesses on a regular basis and Chuck's been getting a lot of requests for entrepreneurship help. He and Mani talk about the 3 primary things that add momentum to your business and help you keep the momentum up when setbacks come your way. Get Lifetime Access to Mani's Entrepreneurship Pack and Book Club. Use coupon code "GREAT" Special Guest: Mani Vaya.

Ben and Chuck put their areas of expertise together to discuss how you could build Machine Learning into web and mobile applications. They discuss the various ways data scientists and Machine Learning engineers can use their expertise to build engines and provide data and results to web and mobile developers. Panel Ben WilsonCharles Max Wood Sponsors Top End DevsCoaching | Top End Devs Picks Ben- Frank Herbert's Dune Saga 3-BookCharles- The Well of AscensionCharles- Traffic SecretsCharles-  Steampunk Rally FusionCharles- Author | Top End Devs

  Get the Black Friday/Cyber Monday "Double Your Productivity by 5pm Today" Deal Coupon Code: "DEEP" for a GIANT discount Mani provides us with strategies and tactics to get Deep Work time and how to get our minds into that focused state for hours at a time. He has read hundreds of books that have taught him the secrets to getting more done by getting into this state. He starts by telling us how he was passed over for a promotion at Qualcomm in favor of someone younger and less experienced and how that inspired him to figure out what the other guy was doing differently. He learned that he needed to get more done with the time he was spending on his projects. The trick? Deep Work! Deep Work is the ability to spend uninterrupted, focused time on a task to bend your entire mind toward the goal. Other developers call it "Flow" or "the Zone." Mani provides us with strategies and tactics to get Deep Work time and how to get our minds into that focused state for hours at a time. Get the Black Friday/Cyber Monday "Double Your Productivity by 5pm Today" Deal Coupon Code: "DEEP" for a GIANT discount

Aliaksei Mikhailiuk joins the Adventure to discuss the tools he wishes he'd mastered before getting his PhD in Machine Learning. However, the conversation meanders through his background, what qualifications people should have, algorithms, and approaches to machine learning along with the tools discussion. Panel Ben WilsonCharles Max WoodMichael Berk Guest Aliaksei Mikhailiuk Sponsors Top End DevsCoaching | Top End Devs Links Nine Tools I Wish I Mastered Before My PhD in Machine LearningAliaksei MikhailiukAliaksei Mikhailiuk - MediumLinkedIn: Aliaksei MikhailiukTwitter: Aliaksei Mikhailiuk,PhD ( @mikhailiuka ) Picks Aliaksei- The Double Descent HypothesisBen- Aliaksei Mikhailiuk - MediumCharles- Shadow Hunters | Board Game | BoardGameGeekCharles- Author | Top End DevsCharles- XeroCharles- StripeMichael- Increase track pad or mouse speed to the Maximum Special Guest: Aliaksei Mikhailiuk.

Michael Berk joins the adventure to discuss how he uses Machine Learning within the context of A/B testing features within applications and how to know when you have a viable test option for your setup. Panel Ben WilsonCharles Max Wood Guest Michael Berk Sponsors Dev Influencers AcceleratorLevel Up | Devchat.tv Links How to Find Weaknesses in your Machine Learning ModelsLinkedIn: Michael BerkMichael Berk - Medium Picks Ben- David Thorne BooksCharles- Shadow HunterCharles- Top End DevsCharles- Become an Author | Top End DevsCharles- Coaching | Top End DevsMichael- Stuart Russell Contact Ben: DatabricksGitHub | BenWilson2/ML-EngineeringGitHub | databrickslabs/automl-toolkitLinkedIn: Benjamin Wilson Contact Charles: Devchat.tvDevChat.tv | FacebookTwitter: DevChat.tv ( @devchattv ) Special Guest: Michael Berk.

Pier Paolo Ippolito joins the adventure to discuss some of the paradoxes or counterintuitive generalizations people make about their datasets. Ben, Chuck, and Pier dive into how to look at statistical data and how to identify trends in the data. Panel Ben WilsonCharles Max Wood Guest Pier Paolo Ippolito Sponsors Dev Influencers AcceleratorLevel Up | Devchat.tv Links Paradoxes in Data SciencePier Paolo IppolitoTwitter: Pier Paolo Ippolito( @Pier_Paolo_28 ) Picks Ben- Python Type Checking Charles- The 360 Degree LeaderCharles- Top End DevsCharles- Become An Author | Top End DevsCharles- Coaching | Top End DevsCharles- The Search for Planet X Contact Ben: DatabricksGitHub | BenWilson2/ML-EngineeringGitHub | databrickslabs/automl-toolkitLinkedIn: Benjamin Wilson Contact Charles: Devchat.tvDevChat.tv | FacebookTwitter: DevChat.tv ( @devchattv ) Special Guest: Pier Paolo Ippolito.

The panel jumps in to attempt to break your mental build regarding testing your ML Ops. They advocate for good testing practices around your code and systems and discuss how you can reliable test the various parts of your applications including your Machine Learning models. Panel Ben WilsonCharles Max WoodFrancois Bertrand Sponsors Dev Influencers AcceleratorLevel Up | Devchat.tv Picks Ben- The Boston Housing DatasetCharles- Coaching | Top End DevsCharles- The 360 Degree LeaderCharles- The Laws of Wealth: Psychology and the Secret to Investing Success Francois- Dune Contact Ben: DatabricksGitHub | BenWilson2/ML-EngineeringGitHub | databrickslabs/automl-toolkitLinkedIn: Benjamin Wilson Contact Charles: Devchat.tvDevChat.tv | FacebookTwitter: DevChat.tv ( @devchattv ) Contact Francois: Francois BertrandGitHub | fbdesignpro/sweetviz

Ahmad Mustafa Anis joins the adventure to discuss how he deploys his Machine Learning models using FastAPI. FastAPI is a system for connecting and running Python programs. If your model is built in Python, you can use FastAPI to deploy to Heroku or similar services. Panel Ben WilsonCharles Max WoodFrancois Bertrand Guest Ahmad Mustafa Anis Sponsors Dev Influencers AcceleratorLevel Up | Devchat.tv Links How to deploy Machine Learning/Deep Learning models to the webMachine Learning Guide PodcastAhmad Anis, Author at cnvrgAhmad Anis - KDnuggetsLinkedIn: Ahmad AnisGitHub: Ahmad Mustafa Anis ( ahmadmustafaanis ) Twitter: Ahmad Mustafa Anis ( @AhmadMustafaAn1 ) Picks Ahmad- Deep Learning for Coders with Fastai and PyTorchBen- Arcadia: A NovelCharles- JSJ 278 Machine Learning with Tyler RenelleCharles- X: Multiply Your God-Given Potential Charles- The Art of Impossible: A Peak Performance Primer Charles- Lost Ruins of ArnakCharles- Steampunk Rally Charles- Gods Love Dinosaurs Francois- Will and Vision: How Latecomers Grow to Dominate Markets Contact Ben: DatabricksGitHub | BenWilson2/ML-EngineeringGitHub | databrickslabs/automl-toolkitLinkedIn: Benjamin Wilson Contact Charles: Devchat.tvDevChat.tv | FacebookTwitter: DevChat.tv ( @devchattv ) Contact Francois: Francois BertrandGitHub | fbdesignpro/sweetviz Special Guest: Ahmad Mustafa Anis.