Enterprise ML Platform Double-Header!

Not one but two great main talks on everything related to enterprise machine learning platforms: use cases, workflows, monitoring, challenges, opportunities, relevant technologies, and lessons learned!

MAIN TALK 1: Macy’s Technology - Enterprise Machine Learning Platform (EMLP) & Uses

Aravind Chamakura – Staff Software Engineer, Selection (Macy's)
Keyur Gabani – Lead Data Scientist, Marketing Analytics (Macy's)
Mahmood Mohammed – Sr. Manager, Engineering, Platforms (Macy's)

Machine learning can drive tangible business value but only if it is actually put to use. Despite the many machine learning discoveries companies are struggling to deploy machine learning to solve real business problems. The gap isn’t that machine learning doesn’t work, but companies struggle to actually use it. With our Enterprise Machine Learning Platform we are addressing this challenge by making it easy for engineers and various teams at Macys to employ Machine Learning to enhance their applications and delight our customers.

By building one common EMLP we are eliminating tedious manual processes and providing our ML Engineers and Data Scientists a one-stop shop to build and run their machining learning end-to-end at scale with automated ML Pipelines. To start with EMLP will support the languages (Python, R, Java, Scala), Workflow, Docker, Spark & Kubernetes. EMLP can run ML-Pipelines on any cloud or on-prem compute resources. EMLP will build and promote "best of breed” tools, process, practices and governance. It will have a feature-repository and a model-repository and it integrates with our Enterprise Experimentation and Telemetry Platforms. We have a very talented group of ML engineers and our EMLP group is led by Haamid Ali, Director of Machine Learning Platform. He has led rollout of many successful machine learning applications at Macy’s and outside.

MAIN TALK 2: Lessons learned deploying machine learning and deep learning models in production at major tech companies

Harish Doddi - Co-founder / CEO (Datatron Technologies)

Deploying machine learning models and deep learning models in production is hard. Harish Doddi outlines the enterprise data science lifecycle, covering how production model deployment flow works, challenges, best practices, and lessons learned. Along the way, they explain why monitoring models in the production should be mandatory.

SCHEDULE

· 6:00-6:15: Check-in & networking

· 6:15-7:00: Main Talk 1: Macy’s Technology - Enterprise Machine Learning Platform (EMLP) & Uses (Aravind Chamakura, Keyur Gabani, Mahmood Mohammed )

· 7:00-7:15: Main Talk 1 Q&A

· 7:15-8:00: Main Talk 2: Datatron ML Platform (Harish Doddi)

· 8:00-8:15: Main talk 2 Q&A

· 8:15-8:30: Wrap-up

ACKNOWLEDGMENTS
Special thanks to Macy's for hosting, food, and drinks!