MLOPS Tools available in Market

1. Version Control and Experiment Tracking:

- DVC (Data Version Control): Manages datasets and models using version control, similar to how Git handles code.

- MLflow: An open-source platform to manage the ML lifecycle, including experiment tracking, model versioning, and deployment.

- Weights & Biases: Offers experiment tracking, model management, and visualization tools.

2. Model Deployment:

- Kubeflow: An open-source toolkit that runs on Kubernetes, designed to make deployments scalable and portable.

- AWS SageMaker: Amazon’s fully managed service that provides tools for building, training, and deploying machine learning models at scale

- TensorFlow Serving: A flexible, high-performance serving system for machine learning models, designed for production environments.

3. CI/CD for Machine Learning:

- GitHub Actions: Automates CI/CD pipelines for machine learning projects, integrating with other MLOps tools.

- Jenkins: An automation server that can be customized to manage CI/CD pipelines for machine learning.

4. Model Monitoring and Management:

- Prometheus & Grafana: Combined, they provide powerful monitoring and alerting solutions, often used for ML model monitoring.

- Seldon Core: An open-source platform for deploying, scaling, and managing thousands of machine learning models on Kubernetes.

5. Data Pipeline Management:

- Apache Airflow: An open-source platform to programmatically author, schedule, and monitor workflows.

- Prefect: A modern workflow orchestration tool that handles complex data pipelines, including those involving ML models.