The Data + AI Summit happening this week had its origins as the Spark + AI Summit, but we thought it was important to expand the conference due to the convergence of data topics we’ve been seeing in the community. The Summit now covers all things data — from data science and data engineering to data analytics and machine learning.
This year, we’re lucky to be joined by some of the leading innovators in the field of machine learning, such as Rajat Monga (co-creator of TensorFlow), Soumith Chintala (co-creator of PyTorch), Clément Delangue (co-creator of Hugging Face Transformers NLP), Matei Zaharia (co-creator of MLflow), Manasi Vartak (creator of ModelDB) and more.
In addition to these well-known creators, there are many other amazing practitioners joining Data + AI Summit to share their knowledge. Here’s some of the 200+ sessions at Data + AI Summit.
Machine learning at large
Keynote: AI is Eating Software by Rajat Monga, co-creator of TensorFlow
Keynote: Using Mathematics to Address the Growing Distrust in Algorithms by Turing Award winner Shafi Goldwasser, CS Professor at MIT, UC Berkeley and Weizmann
A Vision for the Future of ML Frameworks by Soumith Chintala, co-creator of PyTorch and lead of PyTorch at Facebook AI
Meetup: Machine Learning Frameworks, Model Management and Ops by Clément Delangue (Hugging Face), Max Fisher (Databricks), Zhihao Jia (Facebook)
Keynote: AI for Intelligent Financial Services by Dr. Manuela Veloso (JP Morgan, Carnegie Mellon University)
Drug Repurposing using Deep Learning on Knowledge Graphs by Alexander Thomas (Wisecube) and Vishnu Vettrivel (Wisecube)
Object Detection with Transformers by Dr Liam Li (Determined AI)
Automated Background Removal Using PyTorch by Oleksander Miroshnychenko (GlobalLogic), Simona Stolnicu (Levi9)
ML operations (MLOps)
CI/CD in MLOps: Implementing a Framework for Self-Service Everything by Cara Phillips (Artis Consulting) and Wesly Clark (J.B. Hunt)
Why APM is Not the Same as ML Monitoring by Cory Johannsen (Verta)
The Function, the Context, and the Data – Enabling MLOps at Stitch Fix by Elijah Ben Izzy
Consolidating MLOps at One of Europe’s Biggest Airports by Floris Hoogenboom (Schipol) and Sebastiaan Grasdijk (Schipol)
Catch Me If You Can: Keeping Up With ML Models in Production by Shreya Shankar (former Viaduct, Google Brain, Stanford)
Machine Learning CI/CD for Email Attack Detection by Jeshua Bratman (Abnormal Security) and Justin Young (Abnormal Security)
Anomaly Detection at Scale! By Opher Dubrovsky (Nielsen) and Max Peres (Nielsen)
Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue by Han Wang (Lyft)
The Rise of Vector Data by Edo Liberty (Pinecone)
Model Monitoring at Scale with Apache Spark and Verta by Manasi Vartak (Verta)
Natural Language Processing (NLP)
Efficient Large-Scale Language Model Training on GPU Clusters by Deepak Narayanan (PhD Student @ Stanford)
Automatic ICD-10 Code Assignment to Consultations in healthcare by Joinal Ahmed (Halodoc) and Nirav Kumar (Halodoc)
Advanced Natural Language Processing with Apache Spark NLP by David Talby (John Snow Labs)
Conversational AI with Transformer Models by Rajesh Shreedhar Bhat (Walmart) and Dinesh Ladi (Walmart)
Towards Personalization in Global Digital Health by Africa Perianez (benshi.ai)
Scaling Online ML Predictions at DoorDash by Hien Luu and Arbaz Khan
Offer Recommendation System with Apache Spark at Burger King by Luyang Wang and Kai Huang (Intel)
Recommender-Based Transformers by Denis Rothman
Building A Product Assortment Recommendation Engine by Ethan Dubois (Anheuser Busch) and Justin Morse (Anheuser Busch)
I hope you enjoy this selection of amazing talks on the past, present and future of machine learning. You’ll also want to be sure to attend the Apache Spark Data Science and Machine Learning keynotes on Thursday AM (PDT) where you’ll hear the latest announcements and product releases from Databricks and related open source projects.
See you in the Dev Hub at Summit!
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