Where I express personal thoughts in words
Eugene Yan is a data scientist and writer. He works at the intersection of consumer data & tech to build machine learning systems to help customers and writes about effective data science, learning, and career. He's currently an Applied Scientist at Amazon, helping users read more and get more out of reading. Previously, he led the data science team at Lazada (acquired by Alibaba in 2016), working on e-commerce ML systems (e.g., ranking, automation, fraud detection).
Matthew McAteer is a Machine learning Researcher at FOR.ai. Before this, he got his career started in biological aging, before moving on to the mission of figuring out ways in which machine learning could be used on large amounts of noisy biomedical data. He has worked for many companies as a machine learning engineer and researcher, ranging from small startups to Google's TensorFlow team. His work on subjects like security and safety in machine learning has also been showcased at top conferences like ICML.
In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for my Master Thesis. Part 7 explores the use of Boltzmann Machines for collaborative filtering. More specifically, I will dissect three principled papers that incorporate Boltzmann Machines into their recommendation architecture. But first, let’s walk through a primer on Boltzmann Machine and its variants.
Carl Osipov is the Chief Technology Officer of CounterFactual.AI - a boutique machine learning consultancy he co-founded with his friend from IBM. Previously, he held engineering and technical leadership roles at Google and IBM, on programs and projects across both United States and Europe, in the areas of machine learning, computational natural language processing, cloud computing, and big data analytics.
Carl is also an inventor with six patents at USPTO and is an author of "Serverless Machine Learning in Action," a book from Manning Publishers, currently available as an ebook subscription and expected in print in early 2021.
Brian T. O'Neill is a designer, advisor, and founder of Designing for Analytics, an independent consultancy that helps companies turn analytics into indispensable decision support applications. For over 20 years, he has worked with companies including Dell EMC, Global Strategy Group, TripAdvisor, Fidelity, JP Morgan Chase, E-Trade, and several SaaS startups. He has spoken internationally, giving talks at O'Reilly Strata, Enterprise Data World, the International Institute for Analytics Symposium, Predictive Analytics World, and Boston College.
Brian also hosts the highly-rated podcast, Experiencing Data, where he reveals the strategies and activities that product, data science, and analytics leaders are using to deliver valuable experiences around data. In addition to consulting, Brian is also a professional percussionist and has performed at Carnegie Hall and The Kennedy Center.
Luigi Patruno is a Data Scientist and the Founder of MLinProduction.com. He’s currently the Director of Data Science at 2U, where he leads a team of data scientists and ML engineers in developing machine learning models and infrastructure to predict student success outcomes. Luigi founded MLinProduction.com to educate data scientists, ML engineers, and ML product managers about best practices for running machine learning systems in production.
As a consultant for Fortune 500s and start-ups, Luigi helps companies utilize data science to create competitive advantages. He has taught graduate-level courses in Statistics and Big Data Engineering and holds a Masters in Computer Science and a BS in Mathematics.
Alexey Grigorev lives in Berlin with his wife and son. He’s a software engineer with a focus on machine learning, currently working at OLX Group as a Lead Data Scientist. Alexey is a Kaggle master, and he wrote a couple of books. One of them is “Mastering Java for Data Science,” and now he’s working on another one — “Machine Learning Bookcamp.”
Two weeks ago, I attended the inaugural MLOps: Production and Engineering World, which is a two-day virtual conference organized by the Toronto Machine Learning Society that explores the best practices, methodologies, and principles of effective MLOps. In this post, I would like to share content from the talks that I attended during this conference.
