How to productionize ML models at Scale
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  July 16th, 2020

11 AM PST (2PM EST)

Artificial intelligence (AI) and machine learning (ML) are quickly becoming the de-facto tool to affect the bottom line of the organization. Despite the industry being in exponential growth in recent years, 85% of trained machine learning models are never deployed in the real world. (Source: NewVantage Partners)

The widest gap is between data scientists and IT. Organizations often cite a wide variety of complexities when they want to use AI / ML models in operation with production scale data in the real world – from data management, integration, security and real-time analytics.

Join experts at Sigmoid, a fast-growing data and analytics company which has helped many Fortune 500 companies productionize their AI / ML models in the real world.

In this webinar you will learn:

Implications of large scale models in operations
Challenges in taking models to production
Best practices of ML to production
Data Engineering Maturity
Operationalizing in the cloud
Sigmoid’s cutting edge approach

Speakers

Scott Kasper
Director Data Engineering- Yum Brands

Scott Kasper is currently serving as a Director of Data Engineering for Yum! Digital. He is focused on developing a centralized infrastructure to enable teams to develop AI/ML capabilities, for the next wave of growth and strategic possibilities. Scott’s passion for using data and technology to elevate businesses and delight customers stretches back several years, beginning with his involvement in the development of the Magic Band program at Walt Disney World. In the past, Scott has spearheaded Taco Bell’s cloud journey by leading a data warehousing transformation from a decade-old legacy on-premise system to a cloud-based data solution built on Amazon Web Services

Mayur Rustagi
CTO & Cofounder Sigmoid

Mayur has an experience of 11+ years in designing end to end architectures and building data pipelines for big data applications. He has led the development of hundreds of such data pipelines in production systems for leading organizations and has put complex ML models into production

Rahul Singh
Chief Analytics Officer & Cofounder Sigmoid

Rahul has more than 11 years of experience in building intelligent self-learning systems. Rahul and his team bring in a core problem-solving mindset to help businesses with their AI architectures and ML models which transform into continuous actionable insights

Moderator

Tenzin Namdak
Director of Big Data and advanced analytics Sigmoid

With over 10 years of experience in crafting technology solutions, Tenzin works with enterprises that are on a transformation journey to streamline their technology infrastructure across domains, through a data first approach