Family Group: Administration
The MLOps Engineer is responsible for bridging the gap between data science and operations. This role is to ensure the smooth design, deployment, monitoring, and scaling of machine learning (ML) models into production, integrating the rapid advancements of data science with the robustness of IT operations. Supports the work of Group Chief Data Officer (GCDO) for Data Analytics.
Model Deployment:
Streamlining and automating the process of deploying machine learning models into production.
Ensuring models are scalable and performant
Model Monitoring:
Setting up real-time monitoring for model predictions and performance.
Implementing alerting mechanisms for model drift or performance degradation.
Model Lifecycle Management:
Versioning models and their associated datasets.
Enabling rollbacks to previous model versions if necessary.
Infrastructure Management:
Setting up and managing the infrastructure required for running ML models.
Scaling infrastructure based on demand.
Continuous Integration and Continuous Deployment (CI/CD) for ML:
Automating the testing, training, and deployment of ML models.
Ensuring that the models meet the quality standards before deployment.
Collaboration with ML Teams:
Working closely with data scientists and ML engineers to understand model requirements.
Providing feedback to ML teams regarding the operational aspects of models.
Model Retraining and Fine-tuning:
Setting up automated pipelines for periodic model retraining.
Implementing feedback loops for continuous model improvement.
Performance Optimization:
Profiling models to identify performance bottlenecks.
Optimizing model inference times
Security and Compliance:
Ensuring data and models are stored securely.
Implementing access controls and ensuring compliance with industry regulations.
Documentation and Best Practices:
Maintaining comprehensive documentation for MLOps processes and workflows.
Promoting best practices within the organization.
