Address- Conduct reviews for compliance of the ML models in accordance with overall platform governance principles such as versioning, data / model lineage, code best practices and provide feedback to data scientists for potential improvements
- Develop pipelines for continuous operation, feedback and monitoring of ML models leveraging best practices from the CI/CD vertical within the MLOps domain. This can include monitoring for data drift, triggering model retraining and setting up rollbacks.
- Optimize AI development environments (development, testing, production) for usability, reliability and performance.
- Have a strong relationship with the infrastructure and application development team in order to understand the best method of integrating the ML model into enterprise applications (e.g., transforming resulting models into APIs).
- Work with data engineers to ensure data storage (data warehouses or data lakes) and data pipelines feeding these repositories and the ML feature or data stores are working as intended.
- Evaluate open-source and AI/ML platforms and tools for feasibility of usage and integration from an infrastructure perspective. This also involves staying updated about the newest developments, patches and upgrades to the ML platforms in use by the data science teams.
Requirements
- Develop and deploy effective Machine Learning models, analyze data, and enhance algorithms.
- Cleaning and preparing datasets, training models, and ensuring performance meets defined criteria.
- Deliverables include accurate and scalable models, clear documentation, and effective communication of insights derived from data.
- Continuous improvement and optimization of models
