Holiday Gratitude (PLUS: my content plans for 2021, an exceptional blog post on ML monitoring, and interactive visuals for understanding language models)
Happy holidays if you’re celebrating them, and happy Thursday if not!
A quick reminder that there will not be a podcast next week, and I’ll be back in the new year with a special compilation episode featuring the highlights of the interviews I’ve done thus far. I can’t wait for you to listen to it.
Holiday Gratitude
This week’s episode was a short solo-cast where I expressed my gratitude for the guests who have come on the show as well as for you, the listener–thank you for your continued support and I look forward to continuing to bring you these interviews.
I also detailed some of my plans for more content coming in 2021:
- If you prefer to read rather than listen, you can look forward to extended show notes for all episodes on a spiffy new website.
- I’ll be experimenting with writing weekly blog posts featuring some of my own experiences first self-learning ML and then more recently, on the job as an ML Engineer.
- I’ll start to look into the highly requested medium of Youtube. I’m not sure if it’ll be full episodes, highlights, or videos un-related to the podcast. That will probably come Q2 or Q3, whenever I feel I have the processes set for the writing above.
Click here to listen to the episode, or find it in your podcast player of choice: https://www.mlengineered.com/listen
Production Machine Learning Monitoring
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Last week, Alejandro Saucedo published the most in-depth article I’ve read so far on monitoring ML in production. While some of it reads like an ad for his company, Seldon, there’s a TON to be learned from the overall design patterns of the monitoring setups.
Of particular note to me were the sections on outlier detection and data drift using auto-encoders. I’ve been curious for a long time as to how these things should work on complex data and have never heard of this technique before reading this article. I will be digging much more into this as it is directly relevant to the work I do. If anyone knows of other resources, please hit reply and send them my way.
Needless to say, I HIGHLY recommend you read this article. Check it out here.
Interfaces for Explaining Transformer Language Models
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Jay Alammar is an absolute legend for the technical, visual blog posts he publishes. His articles on transformers and BERT were instrumental to my learning of those concepts and I still occasionally reference them as a refresher.
He has a new post out containing some of the interactive visuals he’s created to understand what’s going on under the hood of these language models. Check it out here.
Machine Learning Engineered
I mentioned last week that I had to skip an episode release because of guest reschedulings. Since then, I’ve recorded five episodes, which was fun but extremely tiring. Rest assured, I won’t be missing another release! Also, in case you missed it, I wrote up a study guide for aspiring ML engineers that lays out a clear starting path and contains a list of resources that I and my friends have learned from. Read the Study Guide In this week's edition: My Interview on the MLOps Community podcast...
There’s no podcast episode this week due to an unfortunate coincidence of multiple guests needing to reschedule. My apologies, I’ll be doing my best in the future to not let this happen again. That doesn’t mean I don’t have any new content for this week, though! Today I’m releasing an article that answers one of the most common questions I get: “I want to learn machine learning, where do I start / what do I do?” When I was first getting started in ML, it was pretty straightforward: there was...
After a month off from releasing original interviews on the podcast feed, I’m so excited to be sharing this episode with all of you! Aether Biomachines is one of the most interesting machine learning startups I’ve ever come across and I was thrilled to interview the founder, Pavle Jeremic. Building a Post-Scarcity Future using Machine Learning “How can we make sure that the economy is so productive that the desperation that leads people to commit atrocities never happens?” In this episode,...