Metaflow Tags: Tags in CI/CD

Outerbounds · Beginner ·☁️ DevOps & Cloud ·4y ago

Key Takeaways

Metaflow tags are used in conjunction with CI/CD systems like GitHub actions to streamline MLOps workflows

Full Transcript

let's turn into more production oriented use cases for tagging so now imagine that you're a data scientist who has been iterating on a workflow locally let's say we execute this workflow we keep making changes um whatever it might be and and at some point like we are pretty confident that the workflow does what we wanted it to do so uh of course it is it is definitely considered a good practice that you used git for version control and not only that but maybe you have some kind of a ci cd setup you used to create pull request and so forth so we can do the usual thing that we do when developing software so let's say that we have been working on on some feature brands so this is new awesome model branch and now we are really happy with our results um so we uh of course like first add our changes there and like we commit uh add new awesome model right like this right and um now we push our branch to github and um as usual what we want to do here by the way i can show you that we have all these past executions that have been run locally first but now when we go to github we are ready to create a pull request and we can say that okay we are adding new awesome model here we create the pull request and what i did here is that i we have also uh github actions set up and what the github actions do i can show you here is that they first execute the workflow here they execute a test run like making sure that the flow executes correctly of course this is your opportunity to set up any kind of a test you want to have in place oftentimes we have unit tests in place the test individual modules um that are that are like kind of the part of your workflow maybe you have an end to end integration test like for the whole workflow like what we are doing here but the key part here is that like after the test is successful we will tag the or the github actions will tag the the successful run so you are able to see uh like when when the kind of the the ci cd tests have passed successfully and then you can take further action based on that information for instance you could deploy the model automatically you could deploy the workflow automatically whenever that happens so now we can see that the tests executed successfully and after the test executed successfully we have an action to tag the corresponding executions so now if we go back to the to the meta flow ui here we see that there is a one more execution of the same workflow but in this case like we know that it was executed by github actions we just tag it accordingly and then like we have the tag mentioning that okay so we know that the tests were okay and now this is quite convenient because now in this case like you see all your local executions and then the github executions mixed up but i mean for instance you could imagine like having this view like where you only see like all the all the successful uh cicd execution so you can keep track of all the pushes all the model versions that uh resulted due to kind of a positive tests like successful tests being executed by github actions

Original Description

Learn how to use Metaflow tags in conjunction with CI/CD systems like GitHub actions Find out more about how we think about MLOps, OSS, and human-centric data science tools here: https://outerbounds.com/
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Playlist

Playlist UU5h8Ji6Lm1RyAZopnCpDq7Q · Outerbounds · 9 of 60

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]
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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
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4 Fireside chat #2: MadeWithML.com -- Teaching Practical Machine Learning
Fireside chat #2: MadeWithML.com -- Teaching Practical Machine Learning
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5 Metaflow on Kubernetes and Argo Workflows [no sound]
Metaflow on Kubernetes and Argo Workflows [no sound]
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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
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7 Metaflow Tags: Programmatic Tagging
Metaflow Tags: Programmatic Tagging
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8 Metaflow Tags: Basic Tagging
Metaflow Tags: Basic Tagging
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Metaflow Tags: Tags in CI/CD
Metaflow Tags: Tags in CI/CD
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10 Metaflow Tags: Tags and Namespaces
Metaflow Tags: Tags and Namespaces
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11 Metaflow Tags: Tags and Continuous Training
Metaflow Tags: Tags and Continuous Training
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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
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13 Fireside Chat #5: Machine Learning + Infrastructure for Humans
Fireside Chat #5: Machine Learning + Infrastructure for Humans
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14 Metaflow Sandbox Demo: Free Data Science Infrastructure In the Browser
Metaflow Sandbox Demo: Free Data Science Infrastructure In the Browser
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15 Metaflow on Azure
Metaflow on Azure
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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
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17 ML engineering vs traditional software engineering: similarities and differences
ML engineering vs traditional software engineering: similarities and differences
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18 Why data scientists love and hate notebooks: velocity and validation
Why data scientists love and hate notebooks: velocity and validation
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19 What even is a 10x ML engineer?
What even is a 10x ML engineer?
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20 The 4 main tasks in the production ML lifecycle
The 4 main tasks in the production ML lifecycle
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21 Is the premise of data-centric AI flawed?
Is the premise of data-centric AI flawed?
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22 The 3 factors that Determine the success of ML projects
The 3 factors that Determine the success of ML projects
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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]
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25 Metaflow on GCP
Metaflow on GCP
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26 Fireside Chat #8: Navigating the Full Stack of Machine Learning
Fireside Chat #8: Navigating the Full Stack of Machine Learning
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27 How to Build a Full-Stack Recommender System
How to Build a Full-Stack Recommender System
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28 Modernize your Airflow deployments with Metaflow - zero-cost migration [no sound]
Modernize your Airflow deployments with Metaflow - zero-cost migration [no sound]
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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]
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30 Fireside chat #9:  Language Processing: From Prototype to Production
Fireside chat #9: Language Processing: From Prototype to Production
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31 How to build end-to-end recommender systems at reasonable scale
How to build end-to-end recommender systems at reasonable scale
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32 Full-Stack Machine Learning with Metaflow on CoRise
Full-Stack Machine Learning with Metaflow on CoRise
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33 Natural Language Processing meets MLOps
Natural Language Processing meets MLOps
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34 Fireside Chat #10: Large Language Models: Beyond Proofs of Concept
Fireside Chat #10: Large Language Models: Beyond Proofs of Concept
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35 What even are Large Language Models?
What even are Large Language Models?
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36 How to get started with LLMs today
How to get started with LLMs today
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37 LLMs in production
LLMs in production
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38 Accessing secrets securely in Metaflow [no audio]
Accessing secrets securely in Metaflow [no audio]
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39 Fireside Chat #11: The Open-Source Modern Data Stack
Fireside Chat #11: The Open-Source Modern Data Stack
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40 Fireside chat #12: Kubernetes for Data Scientists
Fireside chat #12: Kubernetes for Data Scientists
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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
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42 Fireside chat #13: Supply Chain Security in Machine Learning
Fireside chat #13: Supply Chain Security in Machine Learning
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43 Quick Delivery, Quicker ML: DeliveryHero's Metaflow Story
Quick Delivery, Quicker ML: DeliveryHero's Metaflow Story
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44 Crafting General Intelligence: LLM Fine-tuning with Metaflow at Adept.ai
Crafting General Intelligence: LLM Fine-tuning with Metaflow at Adept.ai
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45 Fuelling Decisions: How DTN Powers Gas Pricing and Data Science Collaboration
Fuelling Decisions: How DTN Powers Gas Pricing and Data Science Collaboration
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46 From Kitchen to Doorstep: Optimizing Data Science Velocity at Deliveroo
From Kitchen to Doorstep: Optimizing Data Science Velocity at Deliveroo
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47 Building a GenAI Ready ML Platform with Metaflow at Autodesk
Building a GenAI Ready ML Platform with Metaflow at Autodesk
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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
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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
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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
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51 The Past, Present, and Future of Generative AI
The Past, Present, and Future of Generative AI
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52 Building Production Systems with Generative AI, Machine Learning, and Data
Building Production Systems with Generative AI, Machine Learning, and Data
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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)
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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)
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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)
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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)
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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)
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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)
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59 LLMs in Practice: A Guide to Recent Trends and Techniques
LLMs in Practice: A Guide to Recent Trends and Techniques
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60 Metaflow for distributed high-performance computing and large-scale AI training
Metaflow for distributed high-performance computing and large-scale AI training
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Learn how to use Metaflow tags with CI/CD systems like GitHub actions to improve MLOps workflows. This video covers the basics of Metaflow tags and how to integrate them with GitHub actions for automated workflows.

Key Takeaways
  1. Create a Metaflow project
  2. Configure Metaflow tags
  3. Integrate Metaflow with GitHub actions
  4. Automate workflows using GitHub actions
  5. Test and deploy Metaflow workflows
💡 Metaflow tags can be used to streamline MLOps workflows by integrating with CI/CD systems like GitHub actions

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