Adventures in Machine Learning

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  A...

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 a...

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 mod...

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 a...

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...

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...

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...

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 ind...

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? Def...

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 owne...

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 impleme...

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...

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. Sponsor...

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 t...

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 l...

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?...

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 Wh...

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 a...

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 aut...

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 learn...

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, an...

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 sta...

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...

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.” - Kat...

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, n...