Data Retention

We need to have some kind of graceful degradation im ins. And so i think a lot about like one, making sure we're only collecting the data that is useful,. An way that we know that is through like, validated clinical studies. A for some signal that we don't have to day a too, making sure that we have the right language and what not to communicate to people.

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MLOps Coffee Sessions #65 with Skylar Payne, The Future of Data Science Platforms is Accessibility.

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// Abstract
The machine learning and data science space is blowing up -- new tools are popping up every day. While we seem to have every type of "Flow" and "Store" you could imagine, few people really understand how to glue this stuff together. Despite all the tools we have available, we still see companies failing to leverage data science effectively to drive business results.
Instead of spending time driving business results, data scientists spend their time fiddling with Kubernetes, trying to debug that Spark serialization error figuring out how to map their code into the awkward "AI Pipeline" SDK. We have an industry filled with tools built by engineers... for engineers, rather than for data scientists. It's deeply disempowering.
Meanwhile, data is still used effectively to drive decisions in many companies. Analysts have been solving very similar problems on the back of applications like Excel, Tableau, and Mode for literally decades. While there are still challenges in analytics, the MLOps space could learn something from analytics tools. Analytics tools better understand how to make their tools accessible. Analytics tools better understand the value of iterability. Analytics tools better understand that data problems are wicked problems:

- We have to iterate on the formulation and solution simultaneously

- They involve many stakeholders with different opinions
- There's no "right" answer
- The problems are never 100% solved.

If we're going to really drive the most business value from data science, we need to understand how to design our teams and tools to effectively work against such problems.

The future of data science platforms is accessibility and iterability.

// Bio
Data is a superpower, and Skylar has been passionate about applying it to solve important problems across society. For several years, Skylar worked on large-scale, personalized search and recommendation at LinkedIn -- leading teams to make step-function improvements in our machine learning systems to help people find the best-fit role. Since then, he shifted my focus to applying machine learning to mental health care to ensure the best access and quality for all. To decompress from his workaholism, Skylar loves lifting weights, writing music, and hanging out at the beach!

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Connect with Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/

Timestamps:

[00:00] Introduction to Skylar Payne

[00:25] Skylar's blog post overview

[00:55] Data is Wicked

[02:22] Bundling & unbundling

[05:48] ML world vs Analytics world

[08:40] Startups from various perspectives

[11:27] Setting the right building blocks

[15:05] Defining process and interfaces

[19:51] KubeFlow success stories accessibility

[21:17] Machine Learning + Data Science