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.
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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
Stepan Pushkarev
Chief Technology Officer at Provectus
Alex Chung
Senior Product Manager in Deep Learning at AWS
Christopher Burns
Senior AI/ML Solution Architect at AWS
