Building Efficient, Robust, Reproducible Bioinformatics Systems Using Metaflow

Outerbounds · Advanced ·🧠 Large Language Models ·2y ago

Key Takeaways

Building efficient bioinformatics systems using Metaflow and transformer models, specifically the HuggingFace Geneformer model, for reproducible and scalable computational biology.

Original Description

Eddie Matia from Outerbounds has a background in scientific computing, machine learning developer tools and product management. In this video Eddie explores the integration of AI, specifically transformer models, into computational biology for building reproducible and scalable computational biology systems. He discusses the amazing research happening at the intersection of Machine Learning and BioInformatics and how foundation models have been playing a significant role in it. He highlights the use of Metaflow for exploring the HuggingFace Geneformer model as well as the dataset powering it (Genecorpus). He walks through a flow that does a cell-classification fine-tuning task for different organs and how Metaflow enables this easily. Discover more such stories at slack.outerbounds.co
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Playlist

Playlist UU5h8Ji6Lm1RyAZopnCpDq7Q · Outerbounds · 0 of 60

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1 Metaflow GUI for monitoring machine learning workflows
Metaflow GUI for monitoring machine learning workflows
Outerbounds
 2 Metaflow Cards [no sound]
Metaflow Cards [no sound]
Outerbounds
 3 Fireside chat #1: How to Produce Sustainable Business Value with Machine Learning
Fireside chat #1: How to Produce Sustainable Business Value with Machine Learning
Outerbounds
 4 Fireside chat #2: MadeWithML.com -- Teaching Practical Machine Learning
Fireside chat #2: MadeWithML.com -- Teaching Practical Machine Learning
Outerbounds
 5 Metaflow on Kubernetes and Argo Workflows [no sound]
Metaflow on Kubernetes and Argo Workflows [no sound]
Outerbounds
 6 Fireside chat #3: Reasonable Scale Machine Learning -- You're not Google and it's totally OK
Fireside chat #3: Reasonable Scale Machine Learning -- You're not Google and it's totally OK
Outerbounds
 7 Metaflow Tags: Programmatic Tagging
Metaflow Tags: Programmatic Tagging
Outerbounds
 8 Metaflow Tags: Basic Tagging
Metaflow Tags: Basic Tagging
Outerbounds
 9 Metaflow Tags: Tags in CI/CD
Metaflow Tags: Tags in CI/CD
Outerbounds
 10 Metaflow Tags: Tags and Namespaces
Metaflow Tags: Tags and Namespaces
Outerbounds
 11 Metaflow Tags: Tags and Continuous Training
Metaflow Tags: Tags and Continuous Training
Outerbounds
 12 Fireside chat #4: Machine Learning and User Experience -- Building ML Products for People
Fireside chat #4: Machine Learning and User Experience -- Building ML Products for People
Outerbounds
 13 Fireside Chat #5: Machine Learning + Infrastructure for Humans
Fireside Chat #5: Machine Learning + Infrastructure for Humans
Outerbounds
 14 Metaflow Sandbox Demo: Free Data Science Infrastructure In the Browser
Metaflow Sandbox Demo: Free Data Science Infrastructure In the Browser
Outerbounds
 15 Metaflow on Azure
Metaflow on Azure
Outerbounds
 16 Fireside Chat #6: Operationalizing ML -- Patterns and Pain Points from MLOps Practitioners
Fireside Chat #6: Operationalizing ML -- Patterns and Pain Points from MLOps Practitioners
Outerbounds
 17 ML engineering vs traditional software engineering: similarities and differences
ML engineering vs traditional software engineering: similarities and differences
Outerbounds
 18 Why data scientists love and hate notebooks: velocity and validation
Why data scientists love and hate notebooks: velocity and validation
Outerbounds
 19 What even is a 10x ML engineer?
What even is a 10x ML engineer?
Outerbounds
 20 The 4 main tasks in the production ML lifecycle
The 4 main tasks in the production ML lifecycle
Outerbounds
 21 Is the premise of data-centric AI flawed?
Is the premise of data-centric AI flawed?
Outerbounds
 22 The 3 factors that Determine the success of ML projects
The 3 factors that Determine the success of ML projects
Outerbounds
 23 Fireside Chat #7: How to Build an Enterprise Machine Learning Platform from Scratch
Fireside Chat #7: How to Build an Enterprise Machine Learning Platform from Scratch
Outerbounds
 24 Run Metaflow on any cloud: Google Cloud, Azure, or AWS [no sound]
Run Metaflow on any cloud: Google Cloud, Azure, or AWS [no sound]
Outerbounds
 25 Metaflow on GCP
Metaflow on GCP
Outerbounds
 26 Fireside Chat #8: Navigating the Full Stack of Machine Learning
Fireside Chat #8: Navigating the Full Stack of Machine Learning
Outerbounds
 27 How to Build a Full-Stack Recommender System
How to Build a Full-Stack Recommender System
Outerbounds
 28 Modernize your Airflow deployments with Metaflow - zero-cost migration [no sound]
Modernize your Airflow deployments with Metaflow - zero-cost migration [no sound]
Outerbounds
 29 Easy Airflow DAGs for ML and data science with Metaflow [no sound]
Easy Airflow DAGs for ML and data science with Metaflow [no sound]
Outerbounds
 30 Fireside chat #9: Language Processing: From Prototype to Production
Fireside chat #9: Language Processing: From Prototype to Production
Outerbounds
 31 How to build end-to-end recommender systems at reasonable scale
How to build end-to-end recommender systems at reasonable scale
Outerbounds
 32 Full-Stack Machine Learning with Metaflow on CoRise
Full-Stack Machine Learning with Metaflow on CoRise
Outerbounds
 33 Natural Language Processing meets MLOps
Natural Language Processing meets MLOps
Outerbounds
 34 Fireside Chat #10: Large Language Models: Beyond Proofs of Concept
Fireside Chat #10: Large Language Models: Beyond Proofs of Concept
Outerbounds
 35 What even are Large Language Models?
What even are Large Language Models?
Outerbounds
 36 How to get started with LLMs today
How to get started with LLMs today
Outerbounds
 37 LLMs in production
LLMs in production
Outerbounds
 38 Accessing secrets securely in Metaflow [no audio]
Accessing secrets securely in Metaflow [no audio]
Outerbounds
 39 Fireside Chat #11: The Open-Source Modern Data Stack
Fireside Chat #11: The Open-Source Modern Data Stack
Outerbounds
 40 Fireside chat #12: Kubernetes for Data Scientists
Fireside chat #12: Kubernetes for Data Scientists
Outerbounds
 41 Behind the Screen: How Amazon Prime Video ships RecSys models 4x faster
Behind the Screen: How Amazon Prime Video ships RecSys models 4x faster
Outerbounds
 42 Fireside chat #13: Supply Chain Security in Machine Learning
Fireside chat #13: Supply Chain Security in Machine Learning
Outerbounds
 43 Quick Delivery, Quicker ML: DeliveryHero's Metaflow Story
Quick Delivery, Quicker ML: DeliveryHero's Metaflow Story
Outerbounds
 44 Crafting General Intelligence: LLM Fine-tuning with Metaflow at Adept.ai
Crafting General Intelligence: LLM Fine-tuning with Metaflow at Adept.ai
Outerbounds
 45 Fuelling Decisions: How DTN Powers Gas Pricing and Data Science Collaboration
Fuelling Decisions: How DTN Powers Gas Pricing and Data Science Collaboration
Outerbounds
 46 From Kitchen to Doorstep: Optimizing Data Science Velocity at Deliveroo
From Kitchen to Doorstep: Optimizing Data Science Velocity at Deliveroo
Outerbounds
 47 Building a GenAI Ready ML Platform with Metaflow at Autodesk
Building a GenAI Ready ML Platform with Metaflow at Autodesk
Outerbounds
 48 Media Transcoding for 10 Million users and beyond with Metaflow at Epignosis
Media Transcoding for 10 Million users and beyond with Metaflow at Epignosis
Outerbounds
 49 Telematics with Metaflow: How Nirvana Insurance built a large-scale Risk Estimation platform
Telematics with Metaflow: How Nirvana Insurance built a large-scale Risk Estimation platform
Outerbounds
 50 Fireside chat #14: Generative AI and Machine Learning for Film, TV, and Gaming
Fireside chat #14: Generative AI and Machine Learning for Film, TV, and Gaming
Outerbounds
 51 The Past, Present, and Future of Generative AI
The Past, Present, and Future of Generative AI
Outerbounds
 52 Building Production Systems with Generative AI, Machine Learning, and Data
Building Production Systems with Generative AI, Machine Learning, and Data
Outerbounds
 53 A Custom Fine-Tuned LLM in Action (LLMs, RAG, and Fine-Tuning: An Interactive Guided Tour Part 5)
A Custom Fine-Tuned LLM in Action (LLMs, RAG, and Fine-Tuning: An Interactive Guided Tour Part 5)
Outerbounds
 54 Building Live Production Systems with RAG (LLMs & RAG: An Interactive Guided Tour Part 4)
Building Live Production Systems with RAG (LLMs & RAG: An Interactive Guided Tour Part 4)
Outerbounds
 55 Better Relevancy with RAG (LLMs, RAG, and Fine-Tuning: An Interactive Guided Tour Part 3)
Better Relevancy with RAG (LLMs, RAG, and Fine-Tuning: An Interactive Guided Tour Part 3)
Outerbounds
 56 Working with OSS LLMs (LLMs, RAG, and Fine-Tuning: An Interactive Guided Tour Part 2)
Working with OSS LLMs (LLMs, RAG, and Fine-Tuning: An Interactive Guided Tour Part 2)
Outerbounds
 57 Hitting OpenAI and Other Vendor APIs (LLMs, RAG, and Fine-Tuning: An Interactive Guided Tour Part 1)
Hitting OpenAI and Other Vendor APIs (LLMs, RAG, and Fine-Tuning: An Interactive Guided Tour Part 1)
Outerbounds
 58 Production Systems with Generative AI (LLMs, RAG, & Fine-Tuning: An Interactive Guided Tour Part 0)
Production Systems with Generative AI (LLMs, RAG, & Fine-Tuning: An Interactive Guided Tour Part 0)
Outerbounds
 59 LLMs in Practice: A Guide to Recent Trends and Techniques
LLMs in Practice: A Guide to Recent Trends and Techniques
Outerbounds
 60 Metaflow for distributed high-performance computing and large-scale AI training
Metaflow for distributed high-performance computing and large-scale AI training
Outerbounds

This video explores the integration of AI, specifically transformer models, into computational biology for building reproducible and scalable systems. It highlights the use of Metaflow for exploring the HuggingFace Geneformer model and fine-tuning it for cell-classification tasks.

Key Takeaways
  1. Explore the HuggingFace Geneformer model
  2. Use Metaflow to fine-tune the model for cell-classification tasks
  3. Utilize the Genecorpus dataset for training and testing
  4. Optimize model performance using Metaflow
  5. Deploy the model for scalable and reproducible computational biology systems
💡 The use of Metaflow enables easy fine-tuning of transformer models for specific tasks, making it a valuable tool for building efficient and reproducible bioinformatics systems.

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