Our biggest week of events yet

For our first of 2 meetups on Tuesday this week at 5pm UK/ 9am PT we will be talking to Lina Weichbrodt about how to monitor ML stacks.

Monitoring usually focusses on the “four golden signals”: latency, errors, traffic, and saturation. Machine learning services can suffer from special types of problems that are hard to detect with these signals. The talk will introduce these problems with practical examples and suggests additional metrics that can be used to detect them. A case study demonstrates how these new metrics work for the recommendation stacks at Zalando, one of Europe’s largest fashion retailers.

By the way, we are breaking new ground at MLOps.community as she will be the first female guest on the meetup! I know it has taken entirely too long for this and I'm sorry 😅

Is there such a thing? Are they the rare unicorns that only exist in the place that I go to right before I fall asleep?

On Wednesday we will have Linkedin influencer and lead data scientist Alexey Grigorev share his thoughts on the subject.

We all know what we need to do to be good data scientists: know machine learning, be able to program, be fluent in SQL and Python. That’s enough to do our job quite well.

But what does it take to be a better data scientist?

The best way to grow as a data scientist is to step out of direct responsibilities and try on the hats of a product manager as well as a DevOps engineer.

In particular, we should:

- be pragmatic and product-oriented
- communicate more
- get into infrastructure

After attending this chat, you will know exactly how we can do it.

Another first for the community... on Thursday this week at 5pm UK/ 9am PT, Dan Gerlanc will give us a hands-on workshop around calling the DataFrame with Python and Dask.

Python's most popular data science libraries—pandas, numpy, and scikit-learn—were designed to run on a single computer, and in some cases, using a single processor. Whether this computer is a laptop or a server with 96 cores, your compute and memory are constrained by the size of the biggest computer you have access to.

In this workshop, you'll learn how to use Dask, a Python library for parallel and distributed computing, to bypass this constraint by scaling our compute and memory across multiple cores. Dask provides integrations with Python libraries like pandas, numpy, and scikit-learn so you can scale your computations without having to learn completely new libraries or significantly refactoring your code.