Is Open Source Software Actually Secure?

MLOps.community · Beginner ·🏭 MLOps & LLMOps ·10mo ago
Skills: AI Security80%

Key Takeaways

Discusses the security of open source software for large language models

Original Description

Trust at Scale: Security and Governance for Open Source Models // MLOps Podcast #338 with Hudson Buzby, Solutions Architect at JFrog. Appreciate @JFrogInc for their support in bringing this podcast to life. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Abstract For better or for worse, machine learning has traditionally escaped the gaze of security and infrastructure teams, operating outside traditional DevOps practices and not always adhering to organizations' development or security standards. With the introduction of open source catalogs like HuggingFace and Ollama, a new standard has been established for locating, identifying, and deploying machine learning and AI models. But with this new standard comes a plethora of security, governance, and legal challenges that organizations need to address before they can comfortably allow developers to freely build and deploy ML/AI applications. In this conversation, we will discuss ways that enterprise-scale organizations are addressing these challenges to safely and securely build these development environments. // Bio Hudson Buzby is a solution engineer with an emphasis on MLOps, LLMOps, Big Data, and Distributed Systems, leveraging his expertise to help organizations optimize their machine learning operations and large language model deployments. His role involves providing technical solutions and guidance to enhance the efficiency and effectiveness of AI-driven projects. // Related Links https://www.youtube.com/channel/UCh2hNg76zo3d1qQqTWIQxDg ~~~~~~~~ ✌️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://
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Playlist

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1 Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
MLOps.community
 2 Remote Collaboration as a Data Scientist
Remote Collaboration as a Data Scientist
MLOps.community
 3 MLOps Manifesto with Luke Marsden from Dotscience
MLOps Manifesto with Luke Marsden from Dotscience
MLOps.community
 4 MLOps lifecycle description
MLOps lifecycle description
MLOps.community
 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
MLOps.community
 6 Life purpose and too many spreadsheets
Life purpose and too many spreadsheets
MLOps.community
 7 Explainability, Black boxes and EU white paper on reproducibility
Explainability, Black boxes and EU white paper on reproducibility
MLOps.community
 8 Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
MLOps.community
 9 Automatically Retrain Machine Learning Models? Are best practices worth it?
Automatically Retrain Machine Learning Models? Are best practices worth it?
MLOps.community
 10 Building an MLOps Team? Key ideas to keep in mind
Building an MLOps Team? Key ideas to keep in mind
MLOps.community
 11 Hierarchy of MLOps Needs
Hierarchy of MLOps Needs
MLOps.community
 12 Bare necessities for getting an ML model into production
Bare necessities for getting an ML model into production
MLOps.community
 13 MLOps and Monitoring
MLOps and Monitoring
MLOps.community
 14 How Phil Winder got into Data Science and Software Engineering
How Phil Winder got into Data Science and Software Engineering
MLOps.community
 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?
MLOps.community
 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
MLOps.community
 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
MLOps.community
 27 Developing a Machine Learning Feature Store
Developing a Machine Learning Feature Store
MLOps.community
 28 Auto retrain ML models is not the question
Auto retrain ML models is not the question
MLOps.community
 29 3 key parts to Machine Learning monitoring
3 key parts to Machine Learning monitoring
MLOps.community
 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
MLOps.community
 33 Specific challenges in Machine Learning
Specific challenges in Machine Learning
MLOps.community
 34 Current State Of Machine Learning
Current State Of Machine Learning
MLOps.community
 35 Humans in the Loop are a defining factor in Machine Learning
Humans in the Loop are a defining factor in Machine Learning
MLOps.community
 36 Learning from real life Machine Learning failures
Learning from real life Machine Learning failures
MLOps.community
 37 Survivorship Bias in machine learning tutorials
Survivorship Bias in machine learning tutorials
MLOps.community
 38 Swiss Cheese model in Machine Learning
Swiss Cheese model in Machine Learning
MLOps.community
 39 Resume driven development in Machine learning & software engineering
Resume driven development in Machine learning & software engineering
MLOps.community
 40 Who has the highest standards in ML?
Who has the highest standards in ML?
MLOps.community
 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
MLOps.community
 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
MLOps.community
 43 Speed, Trust, Evolution and Scale in MLOps
Speed, Trust, Evolution and Scale in MLOps
MLOps.community
 44 More difficult transition for data scientists to become ML engineers
More difficult transition for data scientists to become ML engineers
MLOps.community
 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?
MLOps.community
 46 Deeper thinking from data scientists around platform blackholes
Deeper thinking from data scientists around platform blackholes
MLOps.community
 47 Checkpointing, metadata, and confidence in your data
Checkpointing, metadata, and confidence in your data
MLOps.community
 48 Adjacent usecases and multistep feature engineering
Adjacent usecases and multistep feature engineering
MLOps.community
 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
Who decides the dirrection of Kubeflow
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 57 What do Kubeflow and Arrikto do and how do they work together?
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
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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