Open Data Science

Use new AI Max tools - like A/B tests and performance planners for budget and bidding changes - to maximize your Search campaigns.

Apply to become an ODSC AI West speaker and share your expertise in ML, Gen AI, NLP, MLOps, LLMs, and more with the AI community.

Data scientists are disappearing. No, not in the physical sense (no rapture here), but in the job market. The term "data science" has been a catch-all term for years, but as companies better learn what goes into hiring data science teams, the generic "dat

Intro To kick off this article, I'd like to explain the interpretability of a machine learning (ML) model. According to Merriam-Webster, interpretability describes the process of making something plain or understandable. In the context of ML, interpretabi

For many new data scientists transitioning into AI and deep learning, the Keras framework is an efficient tool. Keras is a powerful and easy-to-use Python library for developing and evaluating deep learning models. In this article, we'll lay out the welco

Join Comet at Booth 406 in the ODSC East Expo Hall. We will also be speaking at ODSC: - April 30, 9 am - A Deeper Stack for Deep Learning: Adding Visualizations + Data Abstractions to your Workflow (Douglas Blank, Head of Research) Training Session - May

Check out this talk from a London meetup where I spoke on differential programming with Julia! Watch here. We've discussed the idea of differentiable programming, where we incorporate existing programs into deep learning models. But if you're a researcher

As an educator in the field of data science, I'm always interested in new learning resources for machine learning. The industry needs a new crop of data scientists to fill the rising demand. This is why I was pleased to learn of the recent announcement of

Ready to learn machine learning? Download this free ODSC resource: The ODSC Guide to Machine Learning and learn all about ML from industry experts.

For any technology to be successful, it needs to move from the early adopter market segment to the majority, i.e., crossing the chasm. Up until now, machine learning has been primarily in the hype phase and adoption has been mostly driven by the early ado