Optimizing Machine Learning (ML Ops) in Financial Services with Kubernetes and Cloud Technologies.

Topic:
Optimizing Machine Learning Operations (ML Ops) in Financial Services with Kubernetes and Cloud Technologies.
Abstract:
This presentation will focus on how financial institutions can leverage Kubernetes, Terraform, and machine learning frameworks to optimize operations, streamline deployments, and enhance security and scalability in machine learning workflows. The session will dive into key strategies for integrating machine learning models with cloud infrastructure and automating processes through CI/CD pipelines.
Who is this presentation for?
This presentation is intended for IT leaders, system architects, machine learning engineers, and DevOps professionals working in industries such as finance, healthcare, and technology, who are focused on optimizing machine learning workflows and cloud infrastructure.
Prerequisite knowledge:
Basic understanding of machine learning, cloud platforms (AWS, Azure), Kubernetes, and DevOps principles (CI/CD pipelines).
What you'll learn?
1.Best practices for implementing Kubernetes in cloud-native machine learning operations. 2.How to automate ML workflows and deployments with CI/CD tools. 3.Techniques to integrate machine learning models with cloud infrastructure and containerized environments. 4. Strategies for enhancing system scalability, performance, and security using Kubernetes. 5. The role of Terraform and infrastructure as code in optimizing cloud environments.
Speaker Bio:
As Vice President of ML Ops at JP Morgan Chase, Jayaram likely plays a significant role in making purchasing decisions, particularly for technologies related to machine learning workflows, cloud infrastructure, container orchestration (like Kubernetes), and DevOps tools that optimize performance, scalability, and cost efficiency in machine learning and infrastructure operations.
Event Agenda: www.globalbigd...
Speaker: www.globalbigd...
More Info: www.globalbigd...