Description:

Struggle with slow Python computations bottlenecked by a single core? Parallel processing offers a solution, but existing frameworks like Dask and Spark can be complex.

This talk introduces Ray, a revolutionary open-source framework for effortless parallel and distributed computing in Python. We'll explore scenarios where parallelism shines, unveil the limitations of traditional frameworks, and dive into Ray's unique approach of actor-based and task-based parallelism. Companies like Uber and Amazon have already moved to adopt Ray as their central distributed learning framework.

Why Ray?

• Simplicity: Ray uses a familiar Pythonic API, minimizing the learning curve for developers of all levels.
• Flexibility: Run tasks locally, on a cluster, or in the cloud – Ray adapts to your needs.
• Scalability: Seamlessly scale your Python workloads from a single machine to massive clusters.
• Performance: Ray boasts impressive performance gains compared to traditional frameworks.

Why attend?

• Learn how to write parallel Python code with minimal effort using Ray.
• Understand the limitations of existing frameworks and discover Ray's advantages.
• Gain practical insights into accelerating Python workflows with real-world use cases.
• Explore how Ray empowers you to tackle large-scale data analysis, machine learning, and scientific computing problems.

Outline

• The Power of Parallelism: Bottlenecks and Solutions (5 minutes)
• Limitations of Traditional Frameworks (5 minutes)
• Introducing Ray: A New Era of Parallel Python (5 minutes)
• Live Ray Demo (10 min)
• Q&A (5 minutes)

Prerequisites:

Python and familiarity with distributed computing.

Video URL:

https://drive.google.com/drive/folders/1CXHJLazxbqsdeeH8yXesQB-518FnpKkd?usp=drive_link

Content URLs:

We will be presenting using a collab notebook with Python code.

https://colab.research.google.com/drive/1iKlI185WWDlTe8lkEGnNsjwCssr6s2vS?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.

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.