A guide to MLOps for data scientists: Part 4

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

Photo by Jonatan Pie on Unsplash

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 to enable people to adopt MLOps at your company. While your tools should bring data scientists into the production loop for shipping and maintaining ML, the MLOps mindset involves bringing data scientists in early to share the responsibility with engineering and maintain accurate online features. …


Processes enable the people

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

Photo by Rabie Madaci on Unsplash

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 lifecycle. Here in part 3, we’ll talk about how you can change your processes to enable people as you’re beginning to adopt MLOps at your company.

As a data scientist, if you didn’t come from a software engineering background, it’ll be helpful to read up on DevOps when you’re first…


A guide to MLOps for data scientists: Part 2

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

Photo by Ganapathy Kumar on Unsplash

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 implement new processes and potentially new team structures. It sounds like a massive undertaking, and likely someone else’s job, to look at the entire lifecycle across multiple teams and introduce new tooling to begin instrumenting the ML lifecycle. …


Part 1: The continuous ML lifecycle

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

Photo by fabio on Unsplash

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 has never been higher. To meet the demands, companies are adopting a machine learning operations (MLOps) culture to streamline the development, deployment, management and management of production ML at scale.

MLOps isn’t a single product or idea; it is a set of principles that allows the MLOps culture to exist…


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

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

Photo by Franki Chamaki on Unsplash

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 hit recently: disappearing data and degraded performance. They’re so common that it doesn’t matter the size of your company, whether you work for a “tech” (or “non-tech”) company, or how many teams of people are dedicated to shipping your product.

Disappearing data

Data is constantly changing. Our data warehouses, data lakes, streaming…


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 remember looking at the Spark Summit sessions to make the business case for a team of data scientists to attend, but we ended up sending our data engineers instead. (If you’re interested in a 2019 recap, this one is good).

This year, more than 30% of sessions addressed data science…


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.

You will never hear the words “I could never run for president” come out of my mouth ever again.

Monday I had an idea of what it would take to be president of the United States and today it’s different. Today I can be president. …

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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