From Kitchen to Doorstep: Optimizing Data Science Velocity at Deliveroo

Outerbounds · Beginner ·📐 ML Fundamentals ·2y ago

Key Takeaways

Deliveroo's Staff Machine Learning Engineer Thomas Furmston discusses optimizing data science velocity using Metaflow with Argo Workflows and GitOps deployment process

Original Description

Deliveroo is a British online food delivery company founded in 2013 and operating in many European and Asian countries. Its product is complex three-sided marketplace, involving consumers, riders and merchants. Thomas Furmston is a Staff Machine Learning Engineer at Deliveroo. In this talk he discusses how Deliveroo uses Metaflow with Argo Workflows and the process involved in it. He discusses how the existing GitOps deployment process leverages Metaflow to enable seamless deployments to staging & production.There is a conversation about using Metaflow-extensions and about Argo workflows: what works, what doesn't, and what could be built. Discover more such stories at slack.outerbounds.co
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Playlist

Playlist UU5h8Ji6Lm1RyAZopnCpDq7Q · Outerbounds · 46 of 60

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1 Metaflow GUI for monitoring machine learning workflows
Metaflow GUI for monitoring machine learning workflows
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 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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 9 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
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 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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 ▶ 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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Deliveroo's Thomas Furmston shares how to optimize data science velocity using Metaflow with Argo Workflows and GitOps deployment process, enabling seamless deployments to staging and production. This talk provides insights into the challenges and solutions of building efficient ML pipelines in a complex three-sided marketplace.

Key Takeaways
  1. Implement Metaflow with Argo Workflows
  2. Leverage GitOps deployment process
  3. Enable seamless deployments to staging and production
  4. Monitor and optimize ML pipelines
  5. Apply mathematical concepts to real-world problems
💡 Using Metaflow with Argo Workflows and GitOps deployment process can significantly optimize data science velocity in complex marketplaces

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