Machine Learning is growing in leaps and bounds both in capability and adoption. Listen to our experts discuss the ideas and fundamentals needed to succeed as a Machine Learning Engineer.
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Complex ML Models with Data Scientist Fernando Lopez - ML 089
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
Distributed Time Series in Machine Learning - ML 088
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
Time Series Models in Machine Learning - ML 087
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 (OCR) and Machine Learning with Ahmad Anis - ML 086
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
Innovation and AI Strategies with Award Winning Data Science Leader Vidhi Chugh - ML 085
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
Machine Learning on Mobile Devices and More with Aliaksei Mikhailiuk - ML 084
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
Leveling Up in your Data Science Career with Adam Ross Nelson - ML 083
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
Bioinformatics and Programming with Ken Youens-Clark - ML 082
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
Building AI Data Responsibly with Edouard d’Archimbaud - ML 081
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
From Golf Instructor to Software Developer: Taking Next Steps in your Career - ML 080
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
Hyperparameter Tuning for Machine Learning Models - ML 079
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
Ask Me Anything (AMA) with Host Ben Wilson - ML 078
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
Optimizers in Machine Learning, Featuring Maciej Balawejder - ML 077
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
Part 2: Exploratory Data Analysis (EDA) Next Steps - ML 076
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
Exploratory Data Analysis (EDA) in Machine Learning - ML 075
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