For most organizations, the development, deployment, and maintenance of multiple ML models in production are new tasks. Though manageable at a small scale, they become harder to handle as dependencies such as changes in data, model management on the deployment or operational side grow in number and complexity. Join Provectus as we explain how to build a robust ML infrastructure, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
You will learn about:
Data Quality (and quality data) and why it matters
Challenges and solutions of data testing and model testing
Best practices of building and deploying ML models in production
Reference architectures for implementation of Data Quality components as part of MLOps pipelines
Stepan Pushkarev, Chief Technology Officer, Provectus
Rinat Gareev, ML Solutions Architect, Provectus
Who should attend:
Data Scientists & Analysts
ML Practitioners & ML Engineers
Technology Executives & Decision Makers
Let’s explore how to design and implement Data QA components in your ML infrastructure, to build a robust foundation for MLOps!