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ML Operationalization: From What and Why? to How and Who?
Operationalization may be the newest 18 letter word in AI, but there are specific steps to removing your AI initiative from the silos and putting it into production at scale. Sivan Metzger of ParallelM is here to share his experiences, mistakes and all, deploying machine learning and building a... Read more
Interpretable Machine Learning – Fairness, Accountability, and Transparency in ML systems
Editor’s note: Sayak is a speaker for ODSC West in San Francisco this November! Be sure to check out his talk, “Interpretable Machine Learning – Fairness, Accountability and Transparency in ML systems,” there! The problem is it is much harder to evaluate machine learning systems than to train them.... Read more
Watch: Introduction to Reinforcement Learning
Reinforcement Learning (RL), the field of sequential decision making, has evolved significantly within the last few years, achieving super-human performance in solving complex board games, 2D Atari and 3D games (Doom, Quake, StarCraft). But this is not just about games, this is about solving arbitrary problems with truly general... Read more
The Past, Present, and Future of Automated Machine Learning
As a consultant in data science and machine learning, and also a tech journalist, I’m in a position to recognize current trends in the industry. One of the latest crazes centers around “automated machine learning” or AutoML as many call it. In fact, I’ve written a couple of articles... Read more
The Best Machine Learning Research of June 2019
Machine Learning and the data science industry is always changing. To keep you updated on the most recent discoveries, we’ve compiled the 5 most exciting machine learning research pieces that expand what we thought we knew about machine learning and the industries to which it relates.  [Related Article: The... Read more
A Manager’s Guide to Starting a Computer Vision Program
So you’re thinking of starting a computer vision program, but you’ve realized now that the logistics are overwhelming. What framework do you use? What infrastructure? Do you go with an out of the box solution or take the time to build your own? Cloud GPU or on-premise? What’s your... Read more
Best Practices for Deploying Machine Learning in the Enterprise
If you’re an organization worried about being left behind with deploying machine learning, it’s not just you. According to Gartner’s Hype Cycle Chart, machine (and deep) learning are the biggest hyped trends of the year. More businesses, organizations, and startups are talking about deep learning and what it means... Read more
An Introduction to Active Learning
The current utility and accessibility of machine learning is in part due to the exponential increase in the availability of data over time. While data is abundant, labels that are required for specific supervised machine learning tasks can be difficult to obtain. At ODSC West in 2018, Dr. Jennifer... Read more
Watch: Kubeflow and Beyond: Automation of Model Training, Deployment and Testing
Very often a workflow of training models and delivering them to the production environment contains loads of manual work. Those could be either building a Docker image and deploying it to the Kubernetes cluster or packing the model to the Python package and installing it to your Python application.... Read more
OS for AI: How Serverless Computing Enables the Next Gen of ML
Jon Peck is a Full Spectrum Developer & Advocate for Algorithmia, an open marketplace for algorithms. At ODSC West 2018, he delivered a talk “OS for AI” which discussed how serverless computing enables the next generation of machine learning. The slides for Peck’s presentation can be found HERE.  The... Read more