MLOps Platform & Tools
Deploy, monitor, and manage ML models at scale
MLflow
Open-source platform for managing ML lifecycle including experimentation and deployment
Kubeflow
Machine learning toolkit for Kubernetes-based ML workflows
Apache Airflow
Platform for developing, scheduling, and monitoring ML workflows
DVC (Data Version Control)
Git for data science - version control for ML projects
Weights & Biases
Experiment tracking, dataset versioning, and model management
Neptune.ai
MLOps platform for experiment management and model registry
Seldon Core
Open-source platform for deploying ML models on Kubernetes
TensorFlow Serving
Flexible, high-performance serving system for ML models
PyTorch Serve
Model serving framework for PyTorch models
AWS SageMaker
Fully managed service for building, training, and deploying ML models
Azure ML
Cloud-based environment for training, deploying, and managing ML models
Vertex AI
Google Cloud's unified ML platform for building and deploying AI solutions
Evidently AI
Open-source tool for ML model monitoring and data drift detection
Alibi Detect
Python library focused on outlier, adversarial, and drift detection
Feast
Open-source feature store for ML feature management
Pachyderm
Data versioning and data pipelines for ML