On-demand

MLOps and Reproducible ML on AWS with Kubeflow and SageMaker

Learn how to build a secure and compliant Machine Learning Infrastructure to manage the full ML lifecycle on AWS

On-demand

Businesses recognize that machine learning is crucial to ensuring future competitiveness. Yet, a successful ML project is more than just building and deploying models. It is about the complete lifecycle involving ML, DevOps, and data engineering that is built on top of and supported by ML infrastructure. 

Secure and compliant ML infrastructure requires 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

AWS and Amazon SageMaker provide a foundation for building infrastructure for machine learning while Kubeflow is a great open source project, which is not given enough credit in the AWS community.

Join Provectus and AWS as we take you through the best of two worlds and demonstrate how to design and build an end-to-end ML infrastructure on AWS for agile enterprises, as well as for regulated industries, such as HCLS, Financial Services, and Insurance.

Request Webinar

  • This field is for validation purposes and should be left unchanged.

See the Provectus privacy policy for details on how we collect, use, and share information you provided.

On-demand

Businesses recognize that machine learning is crucial to ensuring future competitiveness. Yet, a successful ML project is more than just building and deploying models. It is about the complete lifecycle involving ML, DevOps, and data engineering that is built on top of and supported by ML infrastructure. 

Secure and compliant ML infrastructure requires 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

AWS and Amazon SageMaker provide a foundation for building infrastructure for machine learning while Kubeflow is a great open source project, which is not given enough credit in the AWS community.

Join Provectus and AWS as we take you through the best of two worlds and demonstrate how to design and build an end-to-end ML infrastructure on AWS for agile enterprises, as well as for regulated industries, such as HCLS, Financial Services, and Insurance.

Customer Success Story: GoCheck Kids, Machine Learning infrastructure for disease screening

GoCheck Kids received a secure, FDA compliant ML infrastructure on AWS for more efficient data preparation and faster experimentation. It enabled the team to run 100 large scale experiments by three ML engineers in three weeks and increase the recall of their eye screening model by 3x while preserving high precision.

Who Should Attend

The webinar content is geared toward technology executives, manager-level tech roles, and data engineers.

Presented by

photo

Stepan Pushkarev

Chief Technology Officer at Provectus

photo

Alex Chung

Senior Product Manager in Deep Learning at AWS

photo

Christopher Burns

Senior AI/ML Solution Architect at AWS

Ready to learn about MLOps and reproducible ML on AWS?