MLOps
ML Pipeline Orchestration
Open-source ML platform on Kubernetes providing end-to-end workflows for training, tuning, and serving models at scale.
- Kubeflow Pipelines for workflow orchestration
- Katib for hyperparameter tuning
- KFServing for model deployment
- Jupyter notebooks integration
- Multi-framework support (TF, PyTorch, XGBoost)
- Enterprise ML platforms
- Multi-team ML workflows
- Kubernetes-native deployments
- Distributed training at scale
Workflow orchestration platform for authoring, scheduling, and monitoring ML pipelines as directed acyclic graphs (DAGs).
- Python-based DAG definition
- Rich UI for monitoring
- Extensible with custom operators
- Dynamic pipeline generation
- Integration with major cloud providers
- Batch ML workflows
- Data preprocessing pipelines
- Scheduled model retraining
- ETL + ML combined workflows
Netflix-developed framework for building and managing real-life data science projects with versioning and scaling built-in.
- Easy transition from prototype to production
- Automatic versioning of data and code
- Cloud scalability (AWS Batch, Kubernetes)
- Experiment tracking and visualization
- Python-first design
- Data science team workflows
- Research to production pipelines
- Experimentation workflows
- ML pipeline development
Google Cloud's managed ML pipeline service built on Kubeflow Pipelines with serverless execution and integrated monitoring.
- Serverless pipeline execution
- Pre-built components library
- Integration with Vertex AI services
- Pipeline versioning and lineage
- Automated hyperparameter tuning
- GCP-native ML workflows
- Serverless ML pipelines
- AutoML integration
- Enterprise ML on Google Cloud
Model Lifecycle Management
Open-source platform for the complete ML lifecycle including experimentation, reproducibility, deployment, and model registry.
- Experiment tracking with metrics and artifacts
- Model registry with versioning
- Model deployment to multiple targets
- Project packaging and reproducibility
- Multi-framework support
- Experiment management
- Model versioning and registry
- Multi-framework deployments
- Team collaboration
ML development platform for experiment tracking, dataset versioning, and model management with collaborative features.
- Real-time experiment tracking
- Hyperparameter optimization (Sweeps)
- Dataset and artifact versioning
- Model registry and deployment
- Team collaboration and reports
- Deep learning experiments
- Team-based ML projects
- Research reproducibility
- Model performance comparison
Git-like version control system for ML projects handling large datasets and models with pipeline management.
- Data and model versioning
- Pipeline definition and tracking
- Experiment management
- Cloud storage integration
- Reproducible ML workflows
- Dataset versioning
- Model artifact tracking
- Reproducible experiments
- Team collaboration on data
Framework for packaging, deploying, and scaling ML models as production-ready API services with containerization.
- Model packaging and versioning
- REST and gRPC APIs
- Auto-scaling and batching
- Multi-framework support
- Cloud-native deployment
- Model serving APIs
- Production deployments
- Model packaging
- Microservices architecture
Training Infrastructure
Distributed computing framework for scaling Python applications and ML workloads with built-in libraries for training and tuning.
- Distributed training (Ray Train)
- Hyperparameter tuning (Ray Tune)
- Reinforcement learning (Ray RLlib)
- Model serving (Ray Serve)
- Scalable compute primitives
- Large-scale ML training
- Distributed hyperparameter search
- Multi-node workloads
- Production model serving
Distributed deep learning training framework from Uber optimized for TensorFlow, Keras, PyTorch, and MXNet.
- Data-parallel training
- Multi-GPU and multi-node support
- MPI-based communication
- Auto-tuning for optimal performance
- Framework-agnostic API
- Distributed deep learning
- Multi-GPU training
- Large model training
- Computer vision models
Automatic hyperparameter optimization framework with efficient sampling algorithms and pruning strategies.
- Define-by-run API
- Efficient sampling (TPE, CMA-ES)
- Pruning of unpromising trials
- Parallel distributed optimization
- Visualization tools
- Hyperparameter tuning
- Neural architecture search
- Model optimization
- Automated ML tuning
End-to-end MLOps platform providing experiment management, orchestration, and deployment with auto-magical tracking.
- Auto-logging of experiments
- Remote execution and orchestration
- Dataset versioning
- Model registry
- Resource scheduling
- Full MLOps pipeline
- Team collaboration
- Experiment tracking
- Remote job execution
MLOps Maturity Levels
| Level | Characteristics | Automation | Deployment Frequency |
|---|---|---|---|
| Level 0: Manual | Notebook-driven, manual deployment, no tracking | None | Weeks to months |
| Level 1: DevOps | Automated training, manual deployment, basic tracking | Partial | Weeks |
| Level 2: Automated Training | CI/CD for training, experiment tracking, model registry | Training | Days to weeks |
| Level 3: Automated Deployment | Full CI/CD pipeline, automated validation, monitoring | Training + Deployment | Hours to days |
| Level 4: Full MLOps | End-to-end automation, drift detection, auto-retraining | Complete | Continuous |
Core MLOps Components
Data Management
- Data versioning (DVC, Git LFS)
- Feature stores (Feast, Tecton)
- Data validation (Great Expectations)
- Data lineage tracking
Model Training
- Experiment tracking (MLflow, W&B)
- Hyperparameter tuning (Optuna, Ray Tune)
- Distributed training (Horovod, PyTorch DDP)
- Resource scheduling
Model Deployment
- Model registry (MLflow, BentoML)
- Serving infrastructure (Seldon, KServe)
- A/B testing frameworks
- Canary deployments
Model Monitoring
- Drift detection (Evidently, WhyLabs)
- Performance monitoring
- Explainability tools (SHAP, LIME)
- Alerting and incident response