Successful MLOps not only requires strong collaboration between the AI data team, AI model team, and DevOps- it’s the ability to effectively manage and mitigate risk across the deployment, integration, scale, monitoring, and compliance stages of an AI project. Learn more about ML Ops and implementations in this session.
MLOps Done Right: Best Practices to Deploy, Integrate, Scale, Monitor, and Comply
Tue. September 15, 2020 @ 16:15 ET
Featured Presenters
Kfir Yeshayahu
Kfir Yeshayahu currently serves as Senior Vice President of Products at Veritone, Inc. Kfir is a passionate, customer-focused product leader, with a demonstrated experience in
Event Sponors
Amazon Web Services (AWS)
Government, education, and nonprofit organizations face unique challenges to accomplish complex missions with limited resources. Tens of thousands of public sector organizations around the world
CloudFactory
CloudFactory combines people and technology to provide cloud workforce solutions. Our professionally managed and trained teams can process data for machine learning with high accuracy
Cognilytica
Cognilytica is an AI, ML, and data focused advisory and education firm. Cognilytica serves Public Sector and industry clients with AI, ML, and Cognitive technology
Databricks
Databricks is the data and AI company. Thousands of organizations worldwide — including Comcast, Condé Nast, Nationwide and H&M — rely on Databricks’ open and
Maverick Quantum Inc (mavQ)
mavQ provides an AI driven Low Code platform that allows enterprises to imagine, innovate, and transform their digital journey. Convert your ideas into production scale
