How to Build a Full-Stack Recommender System

Outerbounds · Beginner ·📐 ML Fundamentals ·3y ago
Jacopo Tagliabue, RecSys expert and former Director of AI at Coveo, will join Hugo Bowne-Anderson, Outerbounds’ Head of DevRel, in our first live code along session to dive into how to build a production-grade RecSys, the goal being to develop a relatively simple, effective, and general pipeline for sequential recommendations. We’ll show how you can use popular open-source libraries and tools including DuckDB, Gensim, Metaflow, and Keras to build a fully working cloud endpoint that serves predictions in real time, starting from raw data. We’ll be using the Metaflow sandboxes so you can easily …
Watch on YouTube ↗ (saves to browser)

Chapters (10)

Prelude
4:55 How to code along: Metaflow sandbox (Colab for MLOps)!
12:56 Intro to RecSys
27:43 Building a neural network sequential recommender system live
28:50 Understanding the data: Spotify playlists
37:48 Query faster with DuckDB
47:40 Build a recommender model
51:20 Tuning hyperparameters for model performance
59:30 Analyze results and iterate
1:03:11 Deploy an endpoint to make real time predictions

Playlist

Playlist UU5h8Ji6Lm1RyAZopnCpDq7Q · Outerbounds · 28 of 60

← Previous Next →
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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 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 Outerbounds Live Stream
Outerbounds Live Stream
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 ▶ How to Build a Full-Stack Recommender System
How to Build a Full-Stack Recommender System
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 29 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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 30 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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 31 Fireside chat #9: Language Processing: From Prototype to Production
Fireside chat #9: Language Processing: From Prototype to Production
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 32 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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 33 Full-Stack Machine Learning with Metaflow on CoRise
Full-Stack Machine Learning with Metaflow on CoRise
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 34 Natural Language Processing meets MLOps
Natural Language Processing meets MLOps
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 35 Fireside Chat #10: Large Language Models: Beyond Proofs of Concept
Fireside Chat #10: Large Language Models: Beyond Proofs of Concept
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 36 What even are Large Language Models?
What even are Large Language Models?
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 37 How to get started with LLMs today
How to get started with LLMs today
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 38 LLMs in production
LLMs in production
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 39 Accessing secrets securely in Metaflow [no audio]
Accessing secrets securely in Metaflow [no audio]
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 40 Fireside Chat #11: The Open-Source Modern Data Stack
Fireside Chat #11: The Open-Source Modern Data Stack
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 41 Fireside chat #12: Kubernetes for Data Scientists
Fireside chat #12: Kubernetes for Data Scientists
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 42 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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 43 Fireside chat #13: Supply Chain Security in Machine Learning
Fireside chat #13: Supply Chain Security in Machine Learning
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 44 Quick Delivery, Quicker ML: DeliveryHero's Metaflow Story
Quick Delivery, Quicker ML: DeliveryHero's Metaflow Story
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 45 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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 46 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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 47 From Kitchen to Doorstep: Optimizing Data Science Velocity at Deliveroo
From Kitchen to Doorstep: Optimizing Data Science Velocity at Deliveroo
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 48 Building a GenAI Ready ML Platform with Metaflow at Autodesk
Building a GenAI Ready ML Platform with Metaflow at Autodesk
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 49 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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 50 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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 51 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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 52 The Past, Present, and Future of Generative AI
The Past, Present, and Future of Generative AI
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 53 Building Production Systems with Generative AI, Machine Learning, and Data
Building Production Systems with Generative AI, Machine Learning, and Data
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 54 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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 55 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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 56 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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 57 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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 58 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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 59 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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 60 LLMs in Practice: A Guide to Recent Trends and Techniques
LLMs in Practice: A Guide to Recent Trends and Techniques
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The NEW wave of engineering 🤔
Next Up
The NEW wave of engineering 🤔
Sajjaad Khader