MLOps for Machine Learning Sustainability, Scalability and Governance

Is it challenging for your team to maintain the various tools, languages, and frameworks you are using for data science? Do you have multiple models in your catalog, but can’t easily keep track of them all? Is your model version control either nonexistent or manual? No worries, you are NOT alone!

87% of organizations struggle when they need to maintain a sustainable machine learning model lifecycle and, for 64% of organizations, it takes a month or longer to deploy a single model (Algorithmia Survey Report, 2021) and build consumable and scalable AI applications. In this session, Francesca will share a set of challenges, tools and best practices that data scientists and engineers should keep in mind before deploying their open-loop machine learning systems: these tools and practices are necessary not only to ensure maximum performance, but also to guarantee that your solution once deployed will meet the required governance and security standards.