* Design and develop the ML infrastructure, including data ingestion, storage, and management systems.
* Implement and maintain version control systems for ML models and datasets.
* Develop and deploy automated ML workflows using tools such as TensorFlow, PyTorch, or Scikit-learn.
2. Model Deployment:
* Collaborate with data scientists to develop and deploy ML models into production environments.
* Ensure the smooth operation of deployed models by monitoring performance and identifying potential issues.
* Implement model serving infrastructure using tools such as TensorFlow Serving or Hugging Face Transformers.
3. Data Management:
* Develop and maintain data ingestion, storage, and management systems for ML workflows.
* Ensure the quality and integrity of data used in ML models by implementing data validation and cleaning processes.
* Implement data security measures to protect sensitive data.
4. Monitoring and Optimization:
* Develop and maintain monitoring and logging systems for ML workflows.
* Identify potential issues with deployed ML models and work with data scientists to resolve them.
* Implement optimization techniques to improve the performance of ML models in production.