The MLOps Podcast

By Dean Pleban @ DagsHub

A podcast from DagsHub about bringing machine learning into the real world. Each episode features a conversation with top data science and machine learning practitioners, who'll share their thoughts, ... more

· Technology

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FAQs about The MLOps Podcast:

How many episodes does The MLOps Podcast have?

The podcast currently has 34 episodes available.

The MLOps Podcast episodes:

November 21, 2022 🔴🟢🟣Julia Language in Production with Logan Kilpatrick
In this episode, I speak with Logan Kilpatrick, Julia Language Developer Community Advocate. We talk about machine learning at NASA and how he discovered Julia as a student, the age-old Julia vs. Python debate, and how to get into a new scientific and technical field. It was absolutely awesome! Check it out.

Watch the video: https://www.youtube.com/watch?v=3kgRN8hJIro

Join our Discord community: https://discord.gg/tEYvqxwhah

Relevant Links:
➡️ Logan Kilpatrick on LinkedIn – https://www.linkedin.com/in/logankilpatrick/
➡️ Logan Kilpatrick on Twitter – https://twitter.com/OfficialLoganK
➡️ Julia Language on Twitter – https://twitter.com/JuliaLanguage

Recommendation Links:
📚 Three-Body Problem Series: https://www.amazon.com/Three-Body-Problem-Cixin-Liu/dp/0765382032
📹 13 Lives on IMDB: https://www.imdb.com/title/tt12262116/

🌐 Check Out Our Website! https://dagshub.com

Social Links:
➡️ LinkedIn: https://www.linkedin.com/company/dagshub
➡️ Twitter: https://twitter.com/TheRealDAGsHub
➡️ Dean Pleban: https://twitter.com/DeanPlbn
...more
1h 2min
October 18, 2022 🛠 Building tools for MLOps with Guy Smoilovsky
In this episode, I speak with Guy Smoilovsky, my friend, Co-Founder, and the CTO of DagsHub. We talk about quantum computing and AGI, concrete approaches for automating ML deployment, and how DagsHub came to be.

Watch the video: https://www.youtube.com/watch?v=67dByhXPT5g

Join our Discord community: https://discord.gg/tEYvqxwhah

Relevant Links:
➡️ Guy Smoilovsky on LinkedIn – https://www.linkedin.com/in/guy-smoilovsky/
➡️ Guy Smoilovsky on Twitter – https://twitter.com/Guy_T_Sky/
TDD in machine learning – https://towardsdatascience.com/tdd-datascience-689c98492fcc

Recommendation Links:
Astral Codex Ten – https://astralcodexten.substack.com/
Don't Worry About the Vase – https://thezvi.wordpress.com/
The Sandman – https://www.imdb.com/title/tt1751634/
Lady Silver – https://www.ladysilverband.com/

🌐 Check Out Our Website! https://dagshub.com

Social Links:
➡️ LinkedIn: https://www.linkedin.com/company/dagshub
➡️ Twitter: https://twitter.com/TheRealDAGsHub
➡️ Dean Pleban: https://twitter.com/DeanPlbn
...more
1h 21min
September 16, 2022 📈 You Have Too Much Data with Dean Langsam
In this episode, I speak with Dean Langsam, Data Scientist at SentinelOne and one of the organizers of PyData in Israel. We chat about imposter syndrome, the best field in machine learning, why XGBoost is the best model, and the fact that most organizations have too much data. It was fascinating for me, so I hope you enjoy it too.

🎬 Watch the video: https://www.youtube.com/watch?v=Akz_PpDdLlQ
Join our Discord community: https://discord.gg/tEYvqxwhah

Relevant Links:
⭐️ Join PyData Tel Aviv: https://pydata.org/telaviv2022/ ⭐️
➡️ Dean Langsam on LinkedIn – https://www.linkedin.com/in/deanla/
➡️ Dean Langsam on Twitter – https://twitter.com/dean_la

Recommendation Links:
🌐 Check Out Our Website! https://dagshub.com

Social Links:
➡️ LinkedIn: https://www.linkedin.com/company/dagshub
➡️ Twitter: https://twitter.com/TheRealDAGsHub
➡️ Dean Pleban: https://twitter.com/DeanPlbn
...more
1h 11min
August 22, 2022 🏗 Reasonable Scale MLOps with Jacopo Tagliabue
In this episode, I had the pleasure of speaking with Jacopo Tagliabue, Director of AI at Coveo. We talk about Reasonable Scale MLOps, how to approach building your ML platform, and how quickly you might hit the limits of model deployment (hint: it's pretty surprising)

Join our Discord community: https://discord.gg/tEYvqxwhah

Relevant Links:
➡️ Jacopo on LinkedIn – https://www.linkedin.com/in/jacopotagliabue/
➡️ Jacopo on Twitter – https://twitter.com/jacopotagliabue

Recommendation Links:
📺The Boys – https://www.imdb.com/title/tt1190634/
📚Gödel, Escher, Bach – https://www.goodreads.com/book/show/24113.G_del_Escher_Bach
📚The Three-Body Problem – https://www.goodreads.com/book/show/20518872-the-three-body-problem

🌐 Check Out Our Website! https://dagshub.com

Social Links:
➡️ LinkedIn: https://www.linkedin.com/company/dagshub
➡️ Twitter: https://twitter.com/TheRealDAGsHub
➡️ Dean Pleban: https://twitter.com/DeanPlbn
...more
1h 21min
July 18, 2022 🦾 Made With ML - Learning How to Apply MLOps with Goku Mohandas
In this episode, I had the pleasure of speaking with Goku Mohandas, founder of Made With ML. Goku has an incredible amount of experience building and teaching the community about machine learning and MLOps systems. We dive into system thinking and solving for ML workflows, his journey in the machine learning world, and how he chooses what to learn next. We discuss the most common mistakes he's seen in productionizing ML models and why building models no one will use is not necessarily bad.

Join our Discord community: https://discord.gg/tEYvqxwhah

Relevant Links:
🤩 Check out Made With ML and thank us later – https://madewithml.com/
➡️ Goku on LinkedIn – https://www.linkedin.com/in/goku/
➡️ Goku on Twitter – https://twitter.com/gokumohandas

🌐 Check Out Our Website! https://dagshub.com

Social Links:
➡️ LinkedIn: https://www.linkedin.com/company/dagshub
➡️ Twitter: https://twitter.com/TheRealDAGsHub
➡️ Dean Pleban: https://twitter.com/DeanPlbn
...more
1h 29min
June 20, 2022 🤹‍♀️ Building models that actually perform with Kyle Gallatin
In this episode, I had the pleasure of speaking with Kyle Gallatin, a Machine Learning Software Engineer at Etsy. We talk about how he built the machine learning platform at Etsy, experimentation in production (yes, you heard right), and how to optimize model performance at very large scales. It was awesome, and I'm sure many of you can learn a ton from this one!
Join our Discord community: https://discord.gg/tEYvqxwhah
Relevant Links:
➡️ Kyle on LinkedIn – https://www.linkedin.com/in/kylegallatin/
🌐Check Out Our Website! https://dagshub.com
Social Links:
LinkedIn: https://www.linkedin.com/company/dagshub
Twitter: https://twitter.com/TheRealDAGsHub
Dean Pleban: https://twitter.com/DeanPlbn
...more
59min
May 16, 2022💬 MLOps for NLP Systems with Charlene Chambliss
In this episode, I'm speaking with Charlene Chambliss, Software Engineer at Aquarium. Charlene has vast experience getting NLP models to production. We dive into the intricacies of these models and how they differ from other ML subfields, the challenges in productionizing them, and how to get excited about data quality issues.
Join our Discord community: https://discord.gg/tEYvqxwhah
Relevant Links:
➡️Charlene on LinkedIn – https://www.linkedin.com/in/charlenechambliss/

➡️Charlene on Twitter – https://twitter.com/blissfulchar
Recommendations:
🎬3blue1brown – Awesome YouTube channel about math & science: https://www.youtube.com/c/3blue1brown

🎙NLP Highlights – Allen AI Insititute podcast about NLP research: https://soundcloud.com/nlp-highlights

🎙Software engineering daily: https://softwareengineeringdaily.com/

🎙TWiML – Another great podcast about machine learning and AI: https://twimlai.com/

📰Sebastian Ruder's blog and newsletter about NLP and ML: https://ruder.io/

📰Taming the Tail: Adventures in Improving AI Economics: https://a16z.com/2020/08/12/taming-the-tail-adventures-in-improving-ai-economics/

📰State of AI report (2021): https://www.stateof.ai/

📕Learn to learn – Ultralearning by Scott Young: https://www.scotthyoung.com/

🌐Check Out Our Website! https://dagshub.com
Social Links:
🟦LinkedIn: https://www.linkedin.com/company/dagshub

🐦Twitter: https://twitter.com/TheRealDAGsHub

🐦Dean Pleban: https://twitter.com/DeanPlbn
...more
1h 2min
April 18, 2022🧩 Simplifying Complex Ideas with Yannic Kilcher
In this episode, I'm speaking with the one and only, Yannic Kilcher! We talk about sunglasses 😎, the value and methodologies behind taking complex machine learning research, and making the idea accessible and digestible. We also discuss reproducibility in machine learning and the moving between research and entrepreneurship.
If you haven't seen his videos you should definitely check them out on his YouTube channel (https://www.youtube.com/c/YannicKilcher).
Join our Discord community: https://discord.gg/tEYvqxwhah
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Relevant Links:
➡️Yannics's amazing YouTube channel – https://www.youtube.com/c/YannicKilcher

➡️Yannic on LinkedIn – https://www.linkedin.com/in/ykilcher/

➡️Yannic on Twitter – https://twitter.com/ykilcher
Recommendations:
🎬Veritasium – YouTube channel about science with really good explanations about complex topics: https://www.youtube.com/c/veritasium

📖The Fifth Season – Good sci-fi fantasy book: https://www.amazon.com/Fifth-Season-Broken-Earth/dp/0316229296
🌐Check Out Our Website! https://dagshub.com Social
Links:
➡️LinkedIn: https://www.linkedin.com/company/dagshub

➡️Twitter: https://twitter.com/TheRealDAGsHub

➡️Dean PlbnTwitter: https://twitter.com/DeanPlbn
...more
1h 4min
February 14, 2022🔥 Getting Data Scientists to Write Better Code with Laszlo Sragner
In this episode, we dive into the challenging but very important topic of getting data scientists to write better code. How to approach complex machine learning projects and break them down, and why growing unicorns 🦄 is better than hunting them. Check out this is an awesome conversation with Laszlo Sragner, Founder at 🔥 Hypergolic.
Join our Discord community: https://discord.gg/tEYvqxwhah
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Timestamps:
00:00 Podcast intro

01:00 Guest introduction

02:34 Why is writing better code important for data scientists?

03:40 How to improve your code

08:17 Don't be afraid of your code.

10:42 Breaking experiments into manageable pieces

12:35 How did your past experiences teach you to strive for better code?

15:21 Proving better code is worth it

18:07 What could be adopted from software development

23:06 What's the most interesting/challenging part of taking models to production?

27:12 What is the hardest part about building a machine learning model?

29:30 How it looks when it works well – a detailed example

36:23 The difference in writing better code in smaller startups compared to larger organizations

39:18 Laszlo's process for the first iteration in a machine learning project

44:33 Breaking data problems down into vertical slices

47:55 End-To-End Platforms vs. Best-of-breed tools

50:30 Obligatory job title discussion...

53:30 Hunting for data science unicorns

56:33 Traits to look for when building a data science team

58:30 Build vs. Buy? What's better?

59:56 What is the most exciting trend in ML and MLOps?

1:00:47 How do you stay up to date?

1:01:40 Recommendations for the audience
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Relevant Links:
➡️Laszlo's awesome substack – https://laszlo.substack.com/

➡️Laszlo's LinkedIn – https://www.linkedin.com/in/laszlosragner/

➡️Laszlo's Twitter – https://twitter.com/xLaszlo
Recommendations:
👀Explore/Expand/Extract by Kent Beck: https://www.youtube.com/watch?v=FlJN6_4yI2A

👩‍💻Code Quality – Refactoring by Martin Fowler: https://martinfowler.com/books/refactoring.html

📐Geometric Deep Learning by Bronstein/Velickovic: https://www.youtube.com/watch?v=5h6MbQ_65-o

✍️Online Writing by Nicolas Cole: https://www.youtube.com/watch?v=Od5J2V-Lmlg

📕The Last Shadow by Orson Scott Card: https://www.goodreads.com/en/book/show/7108926-the-last-shadow

🎬7 minutes, 26 seconds, and the Fundamental Theorem of Agile Software Development: https://www.youtube.com/watch?v=WSes_PexXcA
🌐Check Out Our Website! https://dagshub.com
Social Links:
➡️LinkedIn: https://www.linkedin.com/company/dagshub

➡️Twitter: https://twitter.com/TheRealDAGsHub

➡️Dean PlbnTwitter: https://twitter.com/DeanPlbn
...more
1h 6min
November 04, 2021🎓 MLOps lessons learned helping companies build their ML systems with Lee Harper, Lead DS at Catapult
In this episode, I'm speaking with Lee Harper, Principal Data Scientist at Catapult Systems. Lee holds a Ph.D. in Physical and Theoretical Chemistry. Lee is a teacher-turned-data scientist. We cover the various entry paths into the world of data science, the value of background diversity, security in ML production, and even AI fairness.
Join our Discord community: https://discord.gg/tEYvqxwhah
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Timestamps:
00:00 Podcast intro

01:00 Guest introduction

01:39 How did you get into the fields of data science and machine learning?

05:04 Coding boot camps vs. academia & diversity of backgrounds in ML

09:37 How does the process of bringing your work into production change over the years?

13:02 How has the change in the languages used for data science affected production processes?

16:01 How do you accelerate the timeframes for getting from POC to production in ML?

18:19 Do data scientists reinvent the wheel more often than software developers, and why?

22:14 The value of learning how to Google

23:00 Recurring themes, challenges, and common issues in data science

27:50 Solving for security in ML in production

31:57 ML security considerations for startups

34:30 Data security considerations in ML

35:18 What is the most interesting topic in machine learning right now?

38:05 ML fairness, bias, and responsible AI

41:44 What does it mean to build a fair or unbiased model?

47:15 If you had to choose one challenge in bringing models to production, what would it be?

51:00 What are the tools and processes that you use to make the transition to production easier?

55:35 About "vendor lock-in"

58:00 Your favorite tool recommendations

1:03:35 Recommendations for the audience
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Relevant Links:
Linux Command Line and Shell Scripting Bible – https://www.amazon.com/Linux-Command-Shell-Scripting-Bible/dp/1119700914

Project Hail Mary – https://www.amazon.com/Project-Hail-Mary-Andy-Weir/dp/0593135202
Social Links:
https://www.linkedin.com/company/dagshub/

https://www.linkedin.com/company/catapult-systems/

https://www.linkedin.com/in/leeharper2425/

https://twitter.com/DeanPlbn

https://twitter.com/TheRealDAGsHub
...more
1h 9min

FAQs about The MLOps Podcast:

How many episodes does The MLOps Podcast have?

The podcast currently has 34 episodes available.