Are your agents specialized enough?

MLOps.community · Advanced ·🚀 Entrepreneurship & Startups ·1y ago

Key Takeaways

Discussion on specializing agents for niche tasks using fine-tuning and prompt management

Original Description

Paper: A Taxonomy of AgentOps for Enabling Observability of Foundation Model-based Agents - https://arxiv.org/pdf/2411.05285v1 Fine-tuning models is essential for training specialized agents to perform niche tasks effectively. Explore prompt management and evaluation to enhance and accurately utilize specialized agents at each step. Enrich your agents' capabilities with implicit and explicit human feedback, ensuring they operate intelligently within desired parameters. // Abstract In the December Reading Group session, we explored A Taxonomy of Agents for Enabling Observability of Foundation Model-Based Agents. Key participants discussed the challenges of building agentic AI systems, focusing on four key capabilities: perception, planning, action, and adaptation. The paper highlighted issues like lack of controllability, complex inputs/outputs, and difficulty monitoring AI systems. Early-stage insights drew on DevOps and MLOps practices, and the need for improved tools and evaluation strategies for agent observability. The session fostered a collaborative exchange of ideas and practical solutions. // Hosts Nehil Jain: Stealth AI Startup @ Co-Founder - https://www.linkedin.com/in/nehiljain/ Adam Becker: IRL @ MLOps Community - https://www.linkedin.com/in/adamissimo/ Valdimar Eggertsson: AI Development Team Lead @ Snjallgögn (Smart Data inc.) - https://www.linkedin.com/in/valdimar-%C3%A1g%C3%BAst-eggertsson-8210236/ Moderator: Binoy Perera: Community Operations @ MLOps Community -https://www.linkedin.com/in/binoy-perera-811282204/ // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Subscribe to this calendar to receive updates about all future sessions- https://lu.ma/mlreadinggroup Info page: https://www.notion.so/mlops/MLOps-Community-Reading-Group-2764a02352734213b25af07e5f835d45 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/
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1 Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
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 2 Remote Collaboration as a Data Scientist
Remote Collaboration as a Data Scientist
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 3 MLOps Manifesto with Luke Marsden from Dotscience
MLOps Manifesto with Luke Marsden from Dotscience
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 4 MLOps lifecycle description
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 5 What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
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 6 Life purpose and too many spreadsheets
Life purpose and too many spreadsheets
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 7 Explainability, Black boxes and EU white paper on reproducibility
Explainability, Black boxes and EU white paper on reproducibility
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 8 Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
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 9 Automatically Retrain Machine Learning Models? Are best practices worth it?
Automatically Retrain Machine Learning Models? Are best practices worth it?
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 10 Building an MLOps Team? Key ideas to keep in mind
Building an MLOps Team? Key ideas to keep in mind
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 11 Hierarchy of MLOps Needs
Hierarchy of MLOps Needs
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 12 Bare necessities for getting an ML model into production
Bare necessities for getting an ML model into production
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 13 MLOps and Monitoring
MLOps and Monitoring
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 14 How Phil Winder got into Data Science and Software Engineering
How Phil Winder got into Data Science and Software Engineering
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 15 Provenance and Reproducibility in Machine Learning; what is it and why you need it?
Provenance and Reproducibility in Machine Learning; what is it and why you need it?
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 16 Friction Between Data Scientists and Software Engineers
Friction Between Data Scientists and Software Engineers
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 17 MLOps Problems in different size companies
MLOps Problems in different size companies
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 18 ML tooling in large companies
ML tooling in large companies
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 19 ML Platforms - The build vs buy question
ML Platforms - The build vs buy question
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 20 ML Services Gateway at SurveyMonkey
ML Services Gateway at SurveyMonkey
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 21 Message buses, Async and sync architecture
Message buses, Async and sync architecture
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 22 MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
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 23 Hybrid Data Science Teams @SurveyMonkey
Hybrid Data Science Teams @SurveyMonkey
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 24 How do you handle ML version control at SurveyMonkey
How do you handle ML version control at SurveyMonkey
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 25 Doing ML with Personal Information
Doing ML with Personal Information
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 26 Evolution of the ML feature store @SurveyMonkey
Evolution of the ML feature store @SurveyMonkey
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 27 Developing a Machine Learning Feature Store
Developing a Machine Learning Feature Store
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 28 Auto retrain ML models is not the question
Auto retrain ML models is not the question
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 29 3 key parts to Machine Learning monitoring
3 key parts to Machine Learning monitoring
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 30 MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
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 31 MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
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 32 MLOps: Airflow Pros and Cons
MLOps: Airflow Pros and Cons
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 33 Specific challenges in Machine Learning
Specific challenges in Machine Learning
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 34 Current State Of Machine Learning
Current State Of Machine Learning
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 35 Humans in the Loop are a defining factor in Machine Learning
Humans in the Loop are a defining factor in Machine Learning
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 36 Learning from real life Machine Learning failures
Learning from real life Machine Learning failures
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 37 Survivorship Bias in machine learning tutorials
Survivorship Bias in machine learning tutorials
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 38 Swiss Cheese model in Machine Learning
Swiss Cheese model in Machine Learning
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 39 Resume driven development in Machine learning & software engineering
Resume driven development in Machine learning & software engineering
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 40 Who has the highest standards in ML?
Who has the highest standards in ML?
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 41 Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
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 42 Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
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 43 Speed, Trust, Evolution and Scale in MLOps
Speed, Trust, Evolution and Scale in MLOps
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 44 More difficult transition for data scientists to become ML engineers
More difficult transition for data scientists to become ML engineers
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 45 How many models in prod til I need a dedicated ML platform?
How many models in prod til I need a dedicated ML platform?
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 46 Deeper thinking from data scientists around platform blackholes
Deeper thinking from data scientists around platform blackholes
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 47 Checkpointing, metadata, and confidence in your data
Checkpointing, metadata, and confidence in your data
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 48 Adjacent usecases and multistep feature engineering
Adjacent usecases and multistep feature engineering
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 49 Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
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 50 Reproducability flaws in end to end Machine Learning debugging
Reproducability flaws in end to end Machine Learning debugging
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 51 3rd wave of data scientists
3rd wave of data scientists
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 52 MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
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 53 MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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 54 Are Kubeflow and Airflow complementary?
Are Kubeflow and Airflow complementary?
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 55 Why Kubeflow gained so much traction=open community
Why Kubeflow gained so much traction=open community
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 56 Who decides the dirrection of Kubeflow
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 57 What do Kubeflow and Arrikto do and how do they work together?
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 58 Versioning your ML steps with Kubeflow
Versioning your ML steps with Kubeflow
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 59 Machine Learning Lifecycles//Perception vs Reality
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 60 Kubeflow vs SageMaker in Machine Learning
Kubeflow vs SageMaker in Machine Learning
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