AI

Join us for a session on MLOps, where we delve into the transformative practices and tools that bridge the gap between machine learning development and production deployment. Discover how MLOps enhances collaboration, reproducibility, and scalability in machine learning projects, ensuring seamless transitions from data engineering to model monitoring. Learn about the latest technologies, including Docker, Kubernetes, and MLflow, and explore real-world case studies highlighting best practices and common challenges. Whether you’re a data scientist, engineer, or manager, this session will equip you with the knowledge to streamline your ML workflows and drive impactful business outcomes.

This presentation will assume that the attendees have little to no knowledge of creating and operationalizing ML Models.

In this presentation, we will introduce neural networks slowly. First, we will describe the process of learning machine learning. Then, we will discuss the tools typically involved with machine learning and neural networks. The core of this presentation is taking small steps to achieve a big goal: understanding a neural network. This presentation assumes that the audience knows nothing about the internals of machine learning.

This session will focus on data governance and making data available within your enterprise. Who owns the data, how do we obtain the data, and what does governance look like?