LIVE ONLINE EVENT | March 31 @ 12:00 PM ET

MLOps Salon:

Monitoring Edition


Moody Hadi

Group Manager, New Product Development & Financial Engineering

S&P Global


Moody is a Group Manager of New Product Development at S&P Global within Market Intelligence, he leads a team focusing on applying modelling techniques, such as machine learning and data sciences to distill information value for risk management based signals. Previously, he was Co-Head of Research and Development at Credit Market Analysis (CMA), where he lead the model development and research on Credit Default Swaps pricing and risk management. Prior to CMA, Moody was a Senior Quantitative Analyst at the Chicago Mercantile Exchange (CME) Group, where we worked on Over-The-Counter (OTC) Clearing of Interest Rate and Credit Derivatives and the SPAN Margining Algorithm. Prior to that he had several senior roles in analytical & technical consulting, spanning diverse areas from Asset-Liability Management (ALM) to Business Intelligence (BI).

Moody holds a Bachelors of Science in Computer Science from Georgia Institute of Technology, Masters of Science in Operations Research from Columbia University and MBA from the University of Chicago – Booth School of Business.



MLOps Drivers For an Analytics Platform

Watch live @ 12:05 pm ET

The delivery of Machine learning solutions should reconcile various objectives. Deploying Machine Learning to Production requires integrated discipline and practice across data science & development teams. MLOps provides guidelines and best practices to be followed by data scientists, software engineers & project managers and fosters a culture in which ML technologies can generate business benefits by rapidly building, testing and releasing ML models in to production. The talk succinctly summarizes the challenges in implementing MLOps and introduces the core MLOps drivers that eventually lays the foundation blocks of a Analytics Platform. The talk highlights the steps needed to build a generic purpose Analytics Platform conforming to the stages in Machine Learning development projects and finally touches upon a mini case study carried out by S&P Global’s Market Intelligence Product and Data Science Team.