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.
Francesca Lazzeri, Ph.D. is a data and machine learning scientist with over 15 years of experience in academic research and tech industry. She is author of the book “Machine Learning for Time Series Forecasting with Python” (Wiley) and many other publications, including technology journals and conferences. Francesca is currently Adjunct Professor of machine learning at Columbia University and Principal Data Scientist Manager at Microsoft, where she leads a team of data scientists and engineers focusing on building scalable machine learning applications for customer retention, fraud and risk management, payments, revenue forecasting and experimentation use cases.
Before joining Microsoft, she was a research fellow at Harvard University in the Technology and Operations Management Unit. She is currently Advisory Board member of the European Union, the Women in Data Science (WiDS) initiative, and the Massachusetts Institute of Technology. You can find her on LinkedIn, Twitter @frlazzeri and Medium.