The MLOps Podcast Dean Pleban @ DagsHub

In this episode, I chatted with Uri Goren, founder and CEO of Argmax, about Machine Learning and the future of digital advertising in a world moving away from cookies due to privacy laws like GDPR and CCPA. We chat about challenges in maintaining personalized ads while respecting user privacy, and new methods like probabilistic models and contextual features to cover some of the gap left by removing cookies.

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

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Timestamps:
00:00 Introduction
00:35 The Rise of Privacy Regulations
1:40 The Impact of Losing Cookies
2:48 Understanding Cookies
4:33 Reasons for the Decline of Cookies
8:47 ML Leveraging Cookies in Advertising
10:32 The Shift to Contextual Features
12:53 The Future of ML without Cookies
15:23 New and Old Ways of Generating Contextual Features
20:33 Regulatory Conspiracies
22:33 Unsolved Problems in ML and AI
24:39 Predictions for the Next Year in AI and ML
26:17 Controversial Take: Overuse of LLMs
28:03 Recommendations

➡️ Uri Goren on LinkedIn – https://www.linkedin.com/in/ugoren/

🌐 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

In this episode, I speak with Han-Chung Lee, a machine learning engineer with a lot of interesting takes on ML and AI. We dive into the buzz around natural language processing and the big waves in generative AI. They chat about how newcomers are racing through NLP’s history, mixing old school and new tech, and the shift towards smarter databases. Han-Chung breaks it down with his straightforward takes, making complex AI trends feel like coffee chat topics. It’s a perfect listen for anyone keen on where AI’s headed, minus the jargon.

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

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Timestamps:
00:00 Intro
0:41 State of NLP and LLMs
1:33 Repeating the past in NLP
3:29 Vector databases vs. classical databases
8:49 Choosing the right LLM for an application
12:13 Advantages and disadvantages of LLMs
16:10 Where LLMs are most useful
21:13 The dark side of LLMs and can we detect it?
25:19 Thoughts on LLM leaderboard metrics
31:19 Using LLMs in regulated industries
36:40 Creating a moat in the LLM world
40:20 Evaluating LLMs
44:20 Impact of LLM on non-english languages
48:35 Thoughts on MLOps and getting ML into production
56:48 The Hardest Unsolved Problem in ML and AI
59:09 Predictions for the Future of ML and AI
1:03:25 Recommendations and Conclusion

➡️ Han Lee on Twitter – https://twitter.com/HanchungLee
➡️ Han Lee on LinkedIn – https://www.linkedin.com/in/hanchunglee/

🌐 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

In this episode, I had the pleasure of speaking with Mila Orlovsky, a pioneer in medical AI. We delve into practical applications, overcoming data challenges, and the intricacies of developing AI tools that meet regulatory standards. Mila discusses her experiences with predictive analytics in patient care, offering tips on navigating the complexities of AI implementation in medical environments. This episode is packed with actionable advice and forward-thinking strategies, making it essential listening for professionals looking to impact healthcare through AI.

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

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Timestamps:
00:00 Introduction and Background
4:03 Early Days of Machine Learning in Medicine
5:19 Challenges in Building Medical AI Systems
6:54 Differences Between Medical ML and Other ML Domains
15:36 Unique Challenges of Medical Data in ML
24:01 Counterintuitive Learnings on the Business Side
28:07 Impact and Value of ML Models in Medicine
29:41 The Role of Doctors in the Age of AI
38:44 Explainability in Medical ML
44:31 The FDA and Compliance in Medical ML
48:56 Feedback and Iteration in Medical ML
52:25 Predictions for the Future of ML and AI
53:59 Controversial Predictions in the Field of ML
56:02 Recommendations
57:58 Conclusion

➡️ Mila Orlovsky on LinkedIn – https://www.linkedin.com/in/milaorlovsky/
🩺MeDS – Medical Data Science Israel Community – https://www.facebook.com/groups/452832939966464/


🌐 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

In this episode, I had the pleasure of speaking with Jason Liu, an applied AI consultant and the creator of Instructor – an open-source tool for extracting structured data from LLM outputs. We chat about LLM applications, their challenges, and how to overcome them. We also dive into Instructor, making LLMs interact with existing systems and a bunch of other cool things.

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


➡️ Jason Liu on Twitter – https://twitter.com/jxnlco
🤖 Instructor Blog – https://jxnl.github.io/instructor/

🌐 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


Timestamps:
00:00 Introduction
02:18 Excitement about Machine Learning and AI
03:28 Using LLMs as Backend Developers
04:22 Building Applications with LLMs
07:07 Building Instructor
09:30 Thinking in Logic and Design
10:33 Validating Data and Building Systems with Instructor
11:49 Thoughts About Product and UX in LLMs
17:51 Future of Instructor
20:25 Misconceptions and Unsolved Problems in LLMs
24:57 Improving LLM Applications
26:14 RAG as Recommendation Systems
29:32 Fine-tuning Embedding Models
32:32 Beyond Vector Similarity in RAG
39:32 Predictions for the Next Year in AI and ML
45:26 Measuring Impact on Business Outcomes
47:06 The Continuous Cycle of Machine Learning
48:38 Unlocking Economic Value through Structured Data Extraction
50:52 Questioning the Status Quo and Making an Impact

In this live episode, I'm speaking with Jinen Setpal, ML Engineer at DagsHub about actually building, deploying, and monitoring large language model applications.

We discuss DPT, a chatbot project that is live in production on the DagsHub Discord server and helps answer support questions and the process and challenges involved in building it. We dive into evaluation methods, ways to reduce hallucinations and much more.

We also answer the audience's great questions.

Join now to take part in our first live MLOps Podcast episode.

I'll be chatting with Jinen Setpal, ML Engineer at DagsHub about his work building LLM applications and getting LLMs into production.



Sign up for the event at the link here:

https://www.linkedin.com/events/7098968036782596096/comments/