- mlops
- Machine Learning Toolkit
- The State of MLOps 2021
- AI Infrastructure Alliance Mission Statement
- Open MLOps: Open Source Production Machine Learning
- 10 ML Project and AI Project Ideas for 2021
- ML Platform User Experience: Build for Data Scientists
ML platform principle - Towards MLOps: Technical capabilities of a Machine Learning platform
mlops tutorial
- MLOps Automation
- What I Learned From Attending MLOps World 2021 🎉
recommender mlops - A Complete MLOps Toolbox
- ML Ops: Machine Learning as an Engineering Discipline
- Why is DevOps for Machine Learning so Different?
- A MLOps Tale about operationalising MLFlow and PyTorch
- The role of MLOps on effective AI 🧑💻
make sense of architecture histogram and mlops level describe - Introduction & Comparison of MLOps platforms: AWS Sagemaker, Azure Machine Learning, GCP Vertex AI 🌻
mlops on cloud - End-to-End AutoML Pipeline with H2O AutoML, MLflow, FastAPI, and Streamlit
end to end ml - MLflow for managing the end-to-end machine learning lifecycle
mlflow mlops - Top Github repo trends in 2021
mention ML/AI
- Google Vertex AI: The Easiest Way to Run ML Pipelines
- What Uber Learned from Building Its Own Machine Learning Platform? 🌏
mention the pain points of machine learning
- Feature Store vs Data Warehouse
- MLOps: Building a Feature Store? Here are the top things to keep in mind
- Feature Store Milestones
- What is a Feature Store?
- What are Feature Stores and Why Are They Critical for Scaling Data Science?
- Building Real-Time ML Pipelines with a Feature Store
- Choosing a Feature Store: Feast vs Hopsworks
- Feast is a Simple, Open Source Feature Store that Every Data Scientist Should Know About
- Feast: Setup your own ML Feature store on Kubernetes
- Top MLOps Feature Stores — 2021
- Introduction to Feature Store for Machine Learning
feature pipeline - Feature Engineering on the Modern Data Stack
feature
- Top 15 best Open-Source MLOps tools in 2023
- Level Up Your MLOps Journey with Kedro
- Building an Open Source ML Pipeline: Part 1
open source
- How to guide: Set up, Manage & Monitor Spark on Kubernetes
- Apache Spark on Kubernetes — On-Premise (Ceph) and AWS (S3)
- Stop using Spark for ML!
- What is the difference between Dask and RAPIDS? 🌕
Two mainstream frameworks
- The Perfect Text Editor for Jupyter: A Complete Python IDE
- Navigating ML Deployment
- A Brief Introduction to Azure Machine Learning Studio
+Best OpenSource AutoML frameworks in 2021 📟 automl open source