Description:

In the fast-paced world of machine learning, building and deploying models is only half the battle. The real challenge begins when these models are used in real-time applications, where every millisecond counts, and the stakes are high. This talk will dive deep into the unique challenges faced when implementing real-time machine learning systems and offer practical solutions to overcome them.

We'll explore common pitfalls such as data latency, model drift, and scaling issues, and discuss how to maintain model performance and accuracy in production environments. Using Python and its powerful ecosystem of libraries, we'll demonstrate how to design robust pipelines, implement continuous monitoring, and ensure that your models remain reliable and efficient in real-time applications.

Takeaways for the Audience:

• Understand the specific challenges of real-time machine learning and why they matter.
• Learn practical strategies and tools to address these challenges using Python.
• Gain insights from real-world examples and case studies.
• Walk away with actionable knowledge to improve your own machine learning systems.

Prerequisites:

This talk is aimed at data scientists, machine learning engineers, and developers who are involved in building and maintaining machine learning models. Attendees should have a basic understanding of machine learning concepts and some experience with Python.

Content URLs:

https://docs.google.com/presentation/d/1ewljYN1ftyQBdrHwtxXJ6gukkMoTvjZpVjAjossP2Qw/edit?usp=sharing

Speaker Info:

Dhruv Nigam

Dhruv is a machine learning engineer who loves to build and deploy models at scale using Python. At Dream11, he leverage uplift modeling, reinforcement learning, and supervised learning to create action systems that enhance the user experience for over 100 million users. Before Dream11, Dhruv was a Director and founding Data scientist at Protium. He was key in scaling data science infrastructure from scratch to serve over 500k customers at Protium. He established core data engineering pipelines, data models, and deployment frameworks (GitLab CI/CD, Fast API, EC2, MlFlow) for machine learning models. He has spoken at various prestigious venues including a sponsor talk at CODS COMAD 2024. He has a bachelors and Masters in Electrical Engineering from IIT Bombay.

Ved Prakash

Ved is a skilled ML engineer with 9+ years of experience in conceptualizing and deploying large-scale machine learning and deep learning solutions. At Dream11, he has been a key player in reengineering the core contest generation engine. He is currently engaged in building state-of-the-art deep learning models tailored for tabular data domains. Before joining Dream11, Ved led the search and personalization initiatives at Paytm, where he built and deployed cutting-edge real-time machine learning solutions for 350 million users.

Speaker Links:

Dhruv

Linkedin - www.linkedin.com/in/dhruv-nigam-52531176.

Github - https://github.com/dhruvnigam93.

Twitter - https://twitter.com/druubeey.

Talk on credit risk modeling organized by Databuzz and DPhi - https://www.youtube.com/live/4acAw17khkY?si=vD-83gcY99CehXis.

Ved

https://github.com/ved93.

https://www.linkedin.com/in/vedthedataguy/.

Talk on real time ML- challenges and solutions - https://www.youtube.com/watch?v=DD5f-Gz1890.