👨‍🔧
The Ops Compendium
  • The Ops Compendium
  • Definitions
    • Ops Definition Comparisons
  • ML & DL Compendium
  • MLOps
    • MLOps Intro
    • MLOps Teams
    • MLOps Literature
    • MLOps Course
    • MLOps Patterns
    • ML Experiment Management
    • ML Model Monitoring & Alerts
    • MLOps Tools
    • MLOps Deployment
    • Feature Stores & Feature Pipelines
    • Model Formats
    • AI As Data
    • MLOps Interview Questions
    • ML Architecture
  • DataOps
    • SQL
    • Tools
    • Databases
    • Database Modeling
    • Data Analytics
    • Data Engineering
    • Data Pipelines
    • Data Strategy
    • Data Vision
    • Data Teams
    • Data Catalogs
    • Data Governance
    • Data Quality
    • Data Observability
    • Data Program Management
    • Data KPIs
    • Data Mesh
    • Data Contract
    • Data Product
    • Data Engineering Questions & Training
    • Data Patterns
    • Data Architecture
    • Data Platforms
    • Data Lineage
  • DevOps
    • DevOps Strategy
    • DevOps Tools
      • Tutorials
      • Continuous Integration
      • Docker
      • Kubernetes
      • Cloud Objects
      • Key Value DB
      • API Gateway
      • Infrastructure As code
      • Logs
      • ELK
      • SLO
    • DevOps Courses
  • DevSecOps
    • Definitions
    • Tools
    • Concepts
  • Architecture
    • Problems
    • Development Concepts
    • System Design
Powered by GitBook
On this page
  • General Patterns
  • Patterns in Practice

Was this helpful?

Edit on GitHub
  1. MLOps

MLOps Patterns

PreviousMLOps CourseNextML Experiment Management

Last updated 1 year ago

Was this helpful?

General Patterns

  1. ML product lifecycle patterns

  2. ML design patterns book repo

  3. MLOPS Design Patterns

  4. Awesome MLOps

  1. MLOps Roadmap

  2. Google’s Practitioners Guide to MLOps: A framework for continuous delivery and automation of machine learning

  3. State of MLOps

  4. Easy mlops with pycaret and mlflow

  5. Challenges and solutions by iguazio

  1. Stanford CS329 - CS 329S: Machine Learning Systems Design - the course goes in-depth about how ML systems are built, and how to debug, root cause, monitor, etc.

  2. Metaflow, medium 1 (high level review), 2 (schema), 3, **4 (amazing), 5 (extra), 6 (loading and storing data docs!)

  3. HyperparameterHunter, Hyperopt, mlflow, unit test, concept drifts, using python and kafka

Patterns in Practice

  1. An MLOps End-to-End system, i.e., "You dont need a bigger boat", using MetaFlow, Snowflake, DBT, Prefect, Great Expectations, Weights & Biases, Sagemaker, Lambda

  2. A simplistic E2E system - Snowflake, DBT, S3, CometML, Reclist, SageMaker.

Visenger
Iguazio

TOC
Template