Webinar: Feb 24 | 11 AM PT | 2 PM ET

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

WEDNESDAY, FEBRUARY 24, 11 AM PT | 2 PM ET

Every step in the machine learning lifecycle, from raw data to final predictions, requires quality datasets. Historically, a reliable ML Infrastructure has consisted of four fundamental components:

  • Reusable Feature Store with reproducible data preparation pipelines
  • Reproducible experimentation & model training pipelines
  • Continuous Integration and Delivery for ML (MLOps)
  • Production monitoring and model re-training

Now, as businesses embrace machine learning on a larger scale, they have to ensure model quality and continuity by accounting for changes in data. Data Quality checks and monitoring have become the fifth component of ML Infrastructure that plays an essential role in delivering high-quality AI services.

Webinar Registration

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WEDNESDAY, FEBRUARY 24, 11 AM PT | 2 PM ET

Every step in the machine learning lifecycle, from raw data to final predictions, requires quality datasets. Historically, a reliable ML Infrastructure has consisted of four fundamental components:

  • Reusable Feature Store with reproducible data preparation pipelines
  • Reproducible experimentation & model training pipelines
  • Continuous Integration and Delivery for ML (MLOps)
  • Production monitoring and model re-training

Now, as businesses embrace machine learning on a larger scale, they have to ensure model quality and continuity by accounting for changes in data. Data Quality checks and monitoring have become the fifth component of ML Infrastructure that plays an essential role in delivering high-quality AI services.

Who Should Attend

The webinar content is geared toward technology leaders, data scientists, ML engineers, and DevOps engineers.

Presented by

photo

Stepan Pushkarev

Chief Technology Officer at Provectus

Ready to learn about MLOps and Data Quality?