Welcome August have we got some plans for you! Thats right this week we have what I am calling "the big reveal"! We've got double announcements to go around so nobody feels left out of the fun.
MLOps Day
Heat ye hear ye! Big time announcement!
The founding collaborators of this whole gravy train Bristech are going to be having their first-ever MLOps day-long conference. I sat down and talked with Nic and Thomas 2 of the Bristech organizers to hear what we can expect from the conference and where the inspiration came from.
If you are already sold on the idea, I got you covered. In fact, I got you a 15% off discount code! Use code BRISTECH-MLOPS-COMMUNITY at the checkout. All the details here 💃🕺
Engineering Labs
And the second announcement!
Our newest MLOps community initiative will be our engineering labs. These Labs are an opportunity for you to have hands-on experience solving problems that tend to crop up in the MLOps universe.
In particular, we will be-Exploring system architectures (batch, real-time, streaming)
Promote best practices (modularity, reproducibility)
Develop in different environments (docker, k8s, cloud)
Test several
technologies (kubeflow, MLflow etc)
Check out this video explanation From David and Ivan who add a bit of color around what we are setting out to do. You can also
find all the info here on this Technical Design Document.
We are still looking for 1 more person to help with the organizational efforts, please reach out if you want to be a part of this!
Coffee, Café, Caffeine
Heard of Airflow? Maybe you are using it to help you with some parts of your ML workflow?
If you don' know, airflows a renowned tool for data engineering. It helps with
orchestrating ETL workloads and it's well regarded amongst machine learning engineers as well. So, how does Airflow work and how is it applied to MLOps?
For our latest Coffee Sessions David, Byron, Simon and I sat down to talk about the nuts and bolts of Airflow and they taught me a few things about DAG's in general.
The Disconnect: From POC to Prod
For this week's meetup we will be talking with Axel Goblet and Bertjan Broeksema about Scaling Machine Learning Capabilities in Large Organizations. As ML has become an increasingly important means for organizations to extract value from their data many companies struggle to scale after a successful POC.
By generalizing engineering problems and solving them centrally, scaling becomes much more feasible. Model serving platforms generalize the problem of turning a machine learning model in a value-generating application. Axel and Bertjan have tested more serving platforms than I knew existed and I plan to chat about them in depth
Have a great week! Check out our slack, youtube, and podcasts if you haven't already. Also, it would mean a lot to me if you filled out this form so I can learn more about the community.