ON-DEMAND WEBINAR

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 WEBINAR

Businesses know 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.

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ON-DEMAND WEBINAR

Businesses know 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: FireworkTV, ML infrastructure for personalized video recommendation

FireworkTV’s ML team received a machine learning infrastructure on AWS, built in just four weeks. By using new inference and training pipelines, FireworkTV cut its infrastructure costs by 2x and sped up inferences by 10x, increasing ML team’s productivity and driving improvements in real-time video recommendations.

Who Should Attend

The webinar content is geared toward technology executives, manager-level tech roles, and data engineers. Do not hesitate to invite your boss.

Presented by

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Stepan Pushkarev

Chief Technology Officer at Provectus

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Alex Chung

Senior Product Manager in Deep Learning at AWS

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Christopher Burns

Senior AI/ML Solution Architect at AWS

Ready to learn about MLOps and reproducible ML on AWS?