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Conference: Transform X 2022
Authors: Varun Mohan
2022-10-19
Graphics Processing Units (GPUs) are used for training artificial intelligence and deep learning models, particularly those related to ML inference use cases. However, using GPUs to deploy models at scale can create several challenges for ML practitioners. In this session, Varun Mohan, CEO and Co-Founder of Exafunction, shared the best practices he’s learned to build an architecture that optimizes GPUs for deep learning workloads. Mohan explained the advantages for using GPUs for ML deployment, as well as where they might not have as many benefits. Mohan also discussed cost, memory, and other factors in the GPU-vs-CPU equation. He discussed inefficiencies that may arise in different scenarios and some of the issues related to network bandwidth and egress. Mohan offered techniques, including the importance of batching workloads and optimizing your models, to solve these problems. Finally, he discussed how some companies are using GPUs to run their recommendation and serving systems. Before Exafunction, Mohan was a technical lead and senior manager at Nuro, where he saw the power of deep learning and the large challenges of productionizing it at scale.
Conference: OpenAI + Data Forum 2022
Authors: Bhakti Radharapu
2022-06-23
How do I measure fairness? Is my ML model biased? How do I remediate bias in my model? This talk presents an overview of the main concepts of identifying, measuring and remediating bias in ML systems at scale. We begin by discussing how to measure fairness in production models and causes of algorithmic bias in systems. We then deep-dive into performing bias remediation at all steps of the ML life-cycle: data collection, pre-processing, in-training, and post-processing. We will focus on a gamut of open source tools and techniques in the ecosystem that can be used to create comprehensive fairness workflows. These have not only been vetted by the academic ML community but have also scaled very well for industry-level challenges. We hope that by the end of this talk, ML developers will not only be able to "flag" fairness issues in ML but also "fix" them by incorporating these tools and techniques in their ML workflows.
Conference: OpenAI + Data Forum 2022
Authors: Christian Kadner
2022-06-23
tldr - powered by Generative AI
The Q4 Pipelines team proposes a new component registry to address problems with authoring, publishing, and maintaining components. The registry will have a unified YAML format, versioning and tagging capabilities, and direct integration with the Q4 Pipelines SDK. Third-party registries can also implement the server-side of the API. The Machine Learning Exchange is an example of a registry that is implementing the new protocol. It offers various asset types, including pipelines, components, models, data sets, and notebooks. Watson Studio Pipelines is also in open beta and provides a canvas for running experiments and integrating notebooks.
- Q4 Pipelines proposes a new component registry to address problems with authoring, publishing, and maintaining components
- The registry will have a unified YAML format, versioning and tagging capabilities, and direct integration with the Q4 Pipelines SDK
- Third-party registries can also implement the server-side of the API
- The Machine Learning Exchange is an example of a registry that is implementing the new protocol and offers various asset types
- Watson Studio Pipelines is in open beta and provides a canvas for running experiments and integrating notebooks
Conference: Transform X 2021
Authors: Michael “Rabbi” Harasimowicz, Rachael Martin
2021-10-07
Scale’s Head of Federal, Mark Valentine, will explore AI and ML applications for DoD and the IC with Lockheed Martin’s Mike Harasimowicz and Rachel Martin from the National Geospatial-Intelligence Agency. The panel will discuss the problem sets the Government seeks to use AI/ML to solve, review the current state of government use of AI/ML, set a vision for the future, and examine the path to achieve it.
Conference: Transform X 2021
Authors: Chun Jiang, Alessya (Labzhinova) Visnjic, Adrian Macneil, Ville Tuulos, Elliot Branson
2021-10-07
tldr - powered by Generative AI
Importance of structured and quality data cataloging for machine learning in production
- Structured and easily queryable location for data cataloging is important
- Quality of data should be known to avoid wasting time on processing and feature processing
- Catch regressions early by putting checks upstream in the build process
- Lock device version for on-device logging
- Record metadata for debugging purposes
- Involve subject matter experts for debugging machine learning models
Conference: Transform X 2021
Authors: Bret Taylor
2021-10-07
tldr - powered by Generative AI
The importance of building a data culture and focusing on customer experience in implementing AI in enterprises
- Focusing on customer experience is the right place to start in implementing AI in enterprises
- Clear business value and a defined business case are important in securing funding for AI projects
- Short-term opportunities to make employees more productive with AI should be prioritized
- Creating a data culture is crucial in making data-informed decisions and empowering everyone in the organization
- Automation is a top priority in enterprises to eliminate manual processes
Conference: Transform X 2021
Authors: Daniel Levine, Caryn Marooney, Anu Hariharan
2021-10-07
Venture capital (VC) investors have dramatically increased the pace of dealmaking in the field of AI and ML. In 2020, over 1600 rounds worth over $27b were closed by US-based startups in this space, and 2021 is on track to set an even higher record. Navigating this landscape can be difficult for founders. In addition to the operational aspects—recruiting a team, building technology, working with prospects and customers, finding product-market fit—founders also face fundraising-related decisions with implications for the years to come. What are investors looking for? Which ones have experience with AI startups? What should be expected post-investment? In this panel, top VC investors in the AI and ML space will share how they evaluate startups, give advice to founders raising capital, and explain best ways to leverage your existing (and future) investors.
Conference: Transform X 2021
Authors: Fred Turner
2021-10-07
Fred Turner, CEO of Curative, sits down with Melisa Tokmak, General Manager of Document AI at Scale, to discuss the challenges inherent in building highly scalable healthcare systems at a critical time — the start of a global pandemic. Together they explore how AI helped Curative quickly scale to deliver over 25 million COVID-19 tests. Fred and Melisa discuss the use of ML to speed up healthcare delivery where existing data silos typically include unstructured or ‘messy’ data. Their discussion includes those critical areas that are traditionally paper-centric, like patient onboarding, insurance, and billing. They consider how best to drive receptiveness with relevant organizations to further invest in AI. How do you scale a coherent end-to-end patient experience from zero-to-millions of patients quickly and efficiently with AI? What tools and processes can you use to scale up teams, infrastructure, and healthcare testing quickly? What are the opportunities for AI to help increase the quality and speed of healthcare services? Join this session to hear the opportunities for AI to enable and scale healthcare to reach more people quickly and conveniently.
Conference: Transform X 2021
Authors: Aerin Kim
2021-10-07
tldr - powered by Generative AI
ML linters and other mechanisms enhance labeler productivity when labeling complex images and scenes, resulting in higher quality data for customers.
- Quality is important in ML and affects precision, recall, and IOU.
- Scale AI published four papers this year, including a dataset on Fitzpatrick skin type and a Reddit comment and reply dataset.
- Scale AI's 3D annotation platform and ML-powered linters catch incorrect annotations.
- ML linters and other mechanisms improve labeler productivity and result in higher quality data for customers.