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Tag: Model Management

How to Use MLflow, TensorFlow, and Keras with PyCharm
At Spark + AI Summit in June, we announced MLflow, an open-source platform for the complete machine learning cycle. The platform’s philosophy is simple: work with any popular machine learning library; allow machine learning developers experiment with their models, preserve the training environment, parameters, and dependencies, and reproduce their results; …
MLflow 0.2 Released
At this year’s Spark+AI Summit, we introduced MLflow, an open source platform to simplify the machine learning lifecycle. In the 3 weeks since the release, we’ve already seen a lot of interest from data scientists and engineers in using and contributing to MLflow. MLFlow’s GitHub repository already has 180 forks, …
Put Models at the Core of Business Processes
At Rev, Nick Elprin, Domino’s CEO, continued to provide insights on managing data science based upon years of candid discussions with customers. He also delved into how data science leaders can utilize model management and help their companies become successful model-driven organizations. This blog post provides a distilled summary of …
Model Evaluation
This Domino Data Science Field Note provides some highlights of Alice Zheng’s report, “Evaluating Machine Learning Models“, including evaluation metrics for supervised learning models and offline evaluation mechanisms. The full in-depth report also includes coverage on offline vs online evaluation mechanisms, hyperparameter tuning and potential A/B testing pitfalls is available …
Data Science Models Build on Each Other
Alex Leeds, presented “Building Up Local Models of Customers” at a Domino Data Science Popup. Leeds discussed how the Squarespace data science team built models to address a key business challenge as well as utilized a complex organizational structure to accelerate data science work. This Domino Data Science Field Note …
On Ingesting Kate Crawford’s “The Trouble with Bias”
Kate Crawford discussed bias at a recent SF-based City Arts and Lectures talk and a recording of the discussion will be broadcast, May 6th, on KQED and local affiliates. Members of Domino were in the live audience for the City Arts talk. This Domino Data Science Field Note provides insights …
Data Science is more than Machine Learning
This Domino Data Science Field Note provides highlights and video clips from Addhyan Pandey’s Domino Data Pop-Up talk, “Leveraging Data Science in the Automotive Industry”. Addhyan Pandey is the Principal Data Scientist at Cars.com. Highlights covered in this blog post include Pandey using word2vec to identify duplicate vehicles on the …
Data Scientist? Programmer? Are They Mutually Exclusive?
This Domino Data Science Field Note blog post provides highlights of Hadley Wickham’s ACM Chicago talk, “You Can’t Do Data Science in a GUI”. In his talk, Wickham advocates that, unlike a GUI, using code provides reproducibility, data provenance, and the ability to track changes so that data scientists have …
Bias: Breaking the Chain that Holds Us Back
Speaker Bio: Dr. Vivienne Ming was named one of 10 Women to Watch in Tech by Inc. Magazine, she is a theoretical neuroscientist, entrepreneur, and author. She co-founded Socos Labs, her fifth company, an independent think tank exploring the future of human potential. Dr. Ming launched Socos Labs to combine …
The Machine Learning Reproducibility Crisis
Pete Warden is the Technical Lead on the TensorFlow Mobile Embedded Team at Google doing Deep Learning. He is formerly the CTO of Jetpac, which was acquired by Google. He is also an Apple alumnus and blogs at petewarden.com. This post candidly discusses some of the real world reproducibility challenges …