Scaling Enterprise Machine Learning with Seldon

Presented by

Alejandro Saucedo, Director of Machine Learning, Seldon and Lee Baker, Commercial Director, Seldon

About this talk

Join this webinar to understand the challenges of scaling machine learning across an enterprise and the solutions provided through MLOps. In this presentation, we will: - Present an overview of the platform’s functionality - Demonstrate the powerful features of the Seldon MLOps solution - Explain how to overcome common hurdles from compliance obligations to model governance Additionally, we will share insights to some of our key client successes: - A global manufacturer has realized 85% efficiency savings - Capital One has established a fully functional MaaS program About the speakers: Alejandro Saucedo leads large scale projects implementing open source and enterprise infrastructure for Machine Learning Orchestration and Explainability. As the Chief Scientist at the Institute for Ethical AI & Machine Learning, he leads the development of industry standards on machine learning bias, adversarial attacks and differential privacy. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has delivered multinational projects with top tier investment banks, magic circle law-firms and global insurance companies. Lee Baker helps people predict and shape their future using machine intelligence. He is responsible for global partnerships, client development and go-to-market strategy. Passionate about the confluence of business and technology, Lee enables enterprise customers, like Barclays, realize success through the industrialization of their machine learning models. This webinar is co-hosted in partnership with Seldon and Red Hat. As a result, both Red Hat and Seldon are collecting your personal data when you submit such information as part of the registration process above. For more information on each party’s privacy practices, see: Red Hat’s policy: Seldon privacy policy:
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