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
Stepan Pushkarev
Chief Technology Officer at Provectus
