A guide to MLOps for data scientists: Part 4

Originally posted on Kaskada’s “Machine Learning Insights” blog here.

In parts 1, 2 and 3 of this series, we covered the ML lifecycle, discussed how to select tools to instrument the ML lifecycle and provided an example to update your processes…


Processes enable the people

Originally posted on Kaskada’s “Machine Learning Insights” blog here.

MLOps can empower us as data scientists to bring more of our models to production faster. In part 1 we covered the ML lifecycle and in part 2 discussed how to select tools to instrument the ML…


A guide to MLOps for data scientists: Part 2

Originally posted on Kaskada’s “Machine Learning Insights” blog here.

In part 1 of this series we talked about the continuous ML lifecycle and what it means for data scientists to adopt MLOps. You’ll be adopting new tools, enjoy increased transparency and…


Part 1: The continuous ML lifecycle

Originally posted on Kaskada’s “Machine Learning Insights” blog here.

MLOps can empower us as data scientists to bring more of our models to production faster. But what is MLOps? The demand for using machine learning in every application is ever expanding, and the need for rapid and constantly improving performance…


A look into disappearing data and degraded performance preventing ML models from shipping

Originally posted on Kaskada’s “Machine Learning Insights” blog here

Surprising but true: 80% of your models never make it out of the lab and into production, and when they do, they more often than not become stale and hard to update. Today, we’ll cover two common problems you might have…


A guide to the three major themes from this year’s Summit: MLOps, feature stores, and deep learning.

Originally posted on Kaskada’s “Machine Learning Insights” blog here

The era of the data scientist has arrived! It seems like a dream come true — for years data scientists haven’t been able to spend their time focused on what we’re trained for: feature engineering and training models.

In 2019 I…


Pre-election it was impossible to predict the vote of people who wouldn’t openly admit they would vote Trump. Post-election these people are now backed and feeling validated by ~59.9M others that elected the least qualified person to office. One who has encouraged violence against entire groups of people I love.

Charna Parkey, Ph.D.

Data science lead @kaskadainc. Startup leader. Applied scientist. Engineer. Language & culture speaker. EE-PhD in DSP. Formerly #6 @textio.

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