Machine Learning Engineered

What I've learned from hosting the ML Engineered podcast (PLUS: the research area you NEED to know about, data science project management, and more...)

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...

over 4 years ago • 3 min read

A study guide for ML engineering, a new Google paper on "Data Cascades", and more...

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...

over 4 years ago • 2 min read

Can machine learning solve scarcity? This founder thinks so... (PLUS: Interest in MLOps "exploding" while ML research is stagnating?)

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,...

over 4 years ago • 3 min read

How to evaluate online models, a primer on real-time recommender systems, all the things that can go wrong on a real-world ML project, and more!

First, an apology: due to some technical issues, the release of the second “Best of ML Engineered in 2020” episode is delayed until this weekend. Sorry about that! Until then, you can check out last week’s compilation episode of the best ML engineering highlights: Click here to listen to the episode, or find it in your podcast player of choice: https://www.mlengineered.com/listen Onto this week’s newsletter! Evaluating online machine learning models “Batch models are meant to be used when you...

over 4 years ago • 2 min read

A compilation of the best podcast clips on ML engineering (PLUS: Lessons learned from 45 years in software, OpenAI's recent breakthroughs, and more!)

I hope you've had a good start to 2021! By way of holiday travel, annual review, and additional content production, it's been more hectic for me than I would prefer, but I'm starting to get back in a rhythm of good habits. Onto the newsletter! In this week's edition: A compilation episode of the best podcast clips on ML engineering Lessons Learned after 45 Years in Software OpenAI's Multi-Modal Breakthroughs A Cambrian explosion of Vision Transformer research Machine Learning Is Going...

over 4 years ago • 3 min read

Best of ML Engineered in 2020 (Most Popular Episodes and Most Clicked Links)

For the last newsletter of 2020, I wanted to highlight the “best of” content I produced and featured since starting ML Engineered. Below you’ll find the top 5 most-downloaded podcast episodes and most-clicked newsletter links. But before we get to that, a follow-up from last week: More on Auto-Encoders for Out of Distribution Detection In last week’s newsletter, I linked to Alejandro Saucedo’s excellent article on production ML monitoring, noting his use of auto-encoders for out of...

over 4 years ago • 1 min read

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...

over 4 years ago • 2 min read

ML for music at Spotify and the future of data science tools (PLUS: a framework for ML projects, BERT in production, and a story from the data trenches)

A brief housekeeping note: the podcast will be taking a holiday break for the next two weeks and will be back on Jan 5. Keep an eye out for a special compilation episode being released then! Onto this week's newsletter: Music Information Retrieval at Spotify and the Future of ML Tooling In this week's episode, I interviewed Andreas Jansson, the co-founder of Replicate, a version control tool for machine learning. He holds a PhD from City University of London in Music Informatics and was...

over 4 years ago • 4 min read

Luigi Patruno on writing ML in Production, thinking from a business perspective, and the future of software (PLUS: 2 open source ML tools and a new online course)

Looks like you missed last week’s ML Engineered newsletter, so I wanted to send it again in case it got lost in your inbox. Have a great week! Luigi Patruno: ML in Production, Adding Business Value with Data Science, "Code 2.0" In this week’s episode, I interviewed the Luigi Patruno, the man behind ML in Production, my favorite blog on the topic of building machine learning systems for the real world. He discusses best practices for putting ML into production, how to make sure your data...

over 4 years ago • 3 min read

New episodes with Yannic Kilcher and swyx! PLUS: NIPS survey paper on production ML, protein folding solved, the GPT-3 debate continued...

Hope you had a great Thanksgiving (or great Thursday if you're not in the US) last week! Featured in this newsletter: New podcast episodes with Yannic Kilcher and Shawn “swyx” Wang NIPS survey paper on challenges in deploying ML models DeepMind solves the protein folding grand challenge Continuing the great GPT-3 debate Yannic Kilcher: Explaining Papers on Youtube, Why Peer Review is Broken, and the Future of the Field In last week’s episode, I interviewed the creator of the best ML...

over 4 years ago • 2 min read