• Data Preprocessing: Cleaning and preparing the data.
• Model Development: Building, training, and evaluating a robust ML model.
• Deployment: Using MLOps best practices to deploy the model as a microservice or an API.
• Monitoring: Implementing monitoring to track the model's performance and data drift in production.

Required Experience:

At least 6-8 years of experience in Machine Learning Engineering or a related field. The freelancer must have a proven track record of deploying ML models in a live production environment.

Required Expertise:

• Expertise in Python and machine learning libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
• Experience with cloud platforms (AWS, GCP, or Azure) for ML.
• Strong knowledge of MLOps principles and tools (e.g., Kubeflow, SageMaker, MLflow).
• Ability to work with large datasets and distributed systems.

Sample Work Required:

Please provide a case study or documentation for a previous MLOps project you executed, detailing the model, the deployment pipeline, and the performance metrics in production.

Freelancer Proposal:

The freelancer should submit a detailed technical proposal outlining their approach to model development and a robust MLOps plan for deployment and monitoring. The proposal must also include a risk assessment.