Tricks to Fine Tuning // Prithviraj Ammanabrolu // MLOps Podcast #318
Skills:
Fine-tuning LLMs90%
Tricks to Fine Tuning // MLOps Podcast #318 with Prithviraj Ammanabrolu,
Research Scientist at @Databricks.
Join the Community: https://go.mlops.community/YTJoinIn
Get the newsletter: https://go.mlops.community/YTNewsletter
// Abstract
Prithviraj Ammanabrolu drops by to break down Tao fine-tuning—a clever way to train models without labeled data. Using reinforcement learning and synthetic data, Tao teaches models to evaluate and improve themselves. Raj explains how this works, where it shines (think small models punching above their weight), and why it could be a game-changer for efficient deployment.
// Bio
Raj is an Assistant Professor of Computer Science at the University of California, San Diego, leading the PEARLS Lab in the Department of Computer Science and Engineering (CSE). He is also a Research Scientist at Mosaic AI, Databricks, where his team is actively recruiting research scientists and engineers with expertise in reinforcement learning and distributed systems.
Previously, he was part of the Mosaic team at the Allen Institute for AI. He earned his PhD in Computer Science from the School of Interactive Computing at Georgia Tech, advised by Professor Mark Riedl in the Entertainment Intelligence Lab.
// Related Links
Website: https://www.databricks.com/
~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~
Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore
Join our Slack community [https://go.mlops.community/slack]
Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]
Sign up for the next meetup: [https://go.mlops.community/register]
MLOps Swag/Merch: [https://shop.mlops.community/]
Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Raj on LinkedIn: /rajammanabrolu
Timestamps:
[00:00] Raj's preferred coffee
[00:36] Takeaways
[01:02] Tao Naming Decision
[04:19] No Labels Machine Learning
[08:09] Tao and TAO breakdown
[13:20] Reward Model Fine-Tun
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Playlist
Uploads from MLOps.community · MLOps.community · 0 of 60
← Previous
Next →
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
MLOps.community
Remote Collaboration as a Data Scientist
MLOps.community
MLOps Manifesto with Luke Marsden from Dotscience
MLOps.community
MLOps lifecycle description
MLOps.community
What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
MLOps.community
Life purpose and too many spreadsheets
MLOps.community
Explainability, Black boxes and EU white paper on reproducibility
MLOps.community
Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
MLOps.community
Automatically Retrain Machine Learning Models? Are best practices worth it?
MLOps.community
Building an MLOps Team? Key ideas to keep in mind
MLOps.community
Hierarchy of MLOps Needs
MLOps.community
Bare necessities for getting an ML model into production
MLOps.community
MLOps and Monitoring
MLOps.community
How Phil Winder got into Data Science and Software Engineering
MLOps.community
Provenance and Reproducibility in Machine Learning; what is it and why you need it?
MLOps.community
Friction Between Data Scientists and Software Engineers
MLOps.community
MLOps Problems in different size companies
MLOps.community
ML tooling in large companies
MLOps.community
ML Platforms - The build vs buy question
MLOps.community
ML Services Gateway at SurveyMonkey
MLOps.community
Message buses, Async and sync architecture
MLOps.community
MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
MLOps.community
Hybrid Data Science Teams @SurveyMonkey
MLOps.community
How do you handle ML version control at SurveyMonkey
MLOps.community
Doing ML with Personal Information
MLOps.community
Evolution of the ML feature store @SurveyMonkey
MLOps.community
Developing a Machine Learning Feature Store
MLOps.community
Auto retrain ML models is not the question
MLOps.community
3 key parts to Machine Learning monitoring
MLOps.community
MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
MLOps.community
MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
MLOps.community
MLOps: Airflow Pros and Cons
MLOps.community
Specific challenges in Machine Learning
MLOps.community
Current State Of Machine Learning
MLOps.community
Humans in the Loop are a defining factor in Machine Learning
MLOps.community
Learning from real life Machine Learning failures
MLOps.community
Survivorship Bias in machine learning tutorials
MLOps.community
Swiss Cheese model in Machine Learning
MLOps.community
Resume driven development in Machine learning & software engineering
MLOps.community
Who has the highest standards in ML?
MLOps.community
Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
MLOps.community
Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
MLOps.community
Speed, Trust, Evolution and Scale in MLOps
MLOps.community
More difficult transition for data scientists to become ML engineers
MLOps.community
How many models in prod til I need a dedicated ML platform?
MLOps.community
Deeper thinking from data scientists around platform blackholes
MLOps.community
Checkpointing, metadata, and confidence in your data
MLOps.community
Adjacent usecases and multistep feature engineering
MLOps.community
Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
MLOps.community
Reproducability flaws in end to end Machine Learning debugging
MLOps.community
3rd wave of data scientists
MLOps.community
MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
MLOps.community
MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
MLOps.community
Are Kubeflow and Airflow complementary?
MLOps.community
Why Kubeflow gained so much traction=open community
MLOps.community
Who decides the dirrection of Kubeflow
MLOps.community
What do Kubeflow and Arrikto do and how do they work together?
MLOps.community
Versioning your ML steps with Kubeflow
MLOps.community
Machine Learning Lifecycles//Perception vs Reality
MLOps.community
Kubeflow vs SageMaker in Machine Learning
MLOps.community
More on: Fine-tuning LLMs
View skill →
🎓
Tutor Explanation
DeepCamp AI