Building your own AI and data science platforms, services, and solutions requires a significant investment of time, budget, and manpower. As a result, it doesn’t always make sense to devote your organization’s resources to doing so. Alternatively, there are many innovative companies developing groundbreaking solutions that you could purchase.
At the Virtual AI Expo on March 30th-31st, many of these companies will be sharing their data science platforms, services, and solutions during 20+ demo talks. Below are just a few of them:
The Next Evolution of PyTorch Performance Debugging
Maxim Lukiyanov, PHD | Principal Product Manager in the Azure AI Platform | Microsoft
As the size of your deep learning model increases, its performance can decline. In this talk, you’ll learn how to identify and solve common performance bottlenecks in PyTorch models using state-of-the-art profiling tools.
Deepnote – a Collaborative Data Science Notebook
Jakub Jurovych, PhD | Founder and CEO | Deepnote
Discover Deepnote, a new kind of data science notebook that is collaborative, intuitive enough for non-technical users, yet powerful enough for data scientists, and able to seamlessly integrates with your data stack, including the Jupyter ecosystem.
The Kit and Kaboodle for Big Data and Data Science
Bob Foreman | Senior Software Engineer | LexisNexis Risk Solutions
This talk will cover how ECL can enable you to build powerful data queries with ease, as well as HPCC Architecture, Embedded Languages and external datastores, Machine Learning Library, Visualization, Application Security, and more.
Jupyter Notebooks for Teams: Best Practices for Quality, Reproducibility, and Collaboration
Aaron Richter, PhD | Senior Data Scientist | Saturn Cloud
Although essential, Jupyter notebooks can become difficult to manage and keep clean as a data science team grows. This talk describes several best practices for working with Jupyter notebooks on a data science team.
Datatron in Action: Take Your Ml Models From Training to Production
Rohan Khade | Senior Data Scientist | Datatron
In this session, you will see how Datatron solves common challenges in deploying, governing, monitoring models, and managing the ML lifecycle in a centralized model catalog. We will also discuss how Datatron handles the underlying MLOps infrastructure and operations related to security, scalability, and governance.
AI Expo Keynote Speakers
With an Expo Hall Pass, you will also have access to ODSC’s Keynote Talks, including
Predict Business Outcomes with AI for a Multi-Cloud World: Ritika Gunnar | VP, Data and AI Expert Labs and Learning | IBM
The Tragedy of the Data Commons: Professor James A. Hendler | Director, Institute for Data Exploration and Applications | Rensselaer Polytechnic Institute
Accelerate AI with the Open Hybrid Cloud: Mike Piech |Vice President and General Manager, Cloud Storage and Data Services | Red Hat
From Data to Decisions: The Important Role of ModelOps: Bryan Harris | Executive Vice President and Chief Technology Officer | SAS