MLOps and Data Quality: Deploying Reliable ML Models in Production

Learn to build a robust ML Infrastructure on AWS, to manage data quality and metadata, and ensure MLOps success

On-Demand Webinar

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

Speakers:

Stepan Pushkarev, Chief Technology Officer, Provectus
Rinat Gareev, ML Solutions Architect, Provectus

Who should attend:

Data Scientists & Analysts
ML Practitioners & ML Engineers
Data Engineers
QA Specialists
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!

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On-Demand Webinar

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

Speakers:

Stepan Pushkarev, Chief Technology Officer, Provectus
Rinat Gareev, ML Solutions Architect, Provectus

Who should attend:

Data Scientists & Analysts
ML Practitioners & ML Engineers
Data Engineers
QA Specialists
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!