What's included

• Model Development & Training

  I will build and train machine learning models based on your business requirements, ensuring high performance and scalability. This includes data preprocessing, feature engineering, and hyperparameter tuning.

• CI/CD Pipeline Setup for ML Models

  I will create and implement continuous integration and deployment pipelines using tools like Docker, MLFlow, and cloud platforms (e.g., Azure, AWS, GCP) to automate the release of new models and updates, ensuring seamless deployment.

• Model Monitoring & Management

  I will set up tools to track model performance post-deployment, monitoring key metrics such as accuracy, latency, and data drift. This will ensure that your models remain reliable and relevant over time.

• MLOps Infrastructure Setup

  I will configure a scalable cloud infrastructure for hosting ML models, ensuring that it is optimized for real-time inference and batch processing. This includes setting up containerization (Docker) and orchestration (Kubernetes) where necessary.

• End-to-End ML Pipeline Documentation

  I will provide detailed documentation for every component of the ML pipeline, including model architecture, data flow, and the CI/CD processes. This ensures that your team has clear visibility into the system and can maintain it effectively.