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Conference: KubeCon + CloudNativeCon North America 2022
Authors: Saravanan Balasubramanian, Savin Goyal
2022-10-27
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The presentation discusses the challenges of introducing machine learning into applications and the need for infrastructure that can provide end-to-end solutions for the entire life cycle of machine learning. It also covers the importance of workflow orchestration and reproducibility in machine learning.
- Infrastructure that can provide end-to-end solutions for the entire life cycle of machine learning is necessary for successful implementation of machine learning into applications
- Workflow orchestration is important for productionizing machine learning workflows
- Reproducibility is important for ensuring trust in machine learning models
- Model deployment can mean many different things depending on the business context
Conference: Transform X 2022
Authors: Tom Vu
2022-10-19
Want to take advantage of the latest research and advances in ML? Looking to chase the next big thing? Often the most useful machine learning models are based on the pain points you see every day. Tom Vu, Senior Director and Head of Data Science and Machine Learning at Flexport, has identified and applied disruptive machine learning opportunities that resulted in over $2 billion of value during the course of his career. In this keynote, Vu shares his insights on how the limitations posed on human reasoning help identify ML opportunities, the importance of understanding processes and pain points relative to cognitive load, and what led him to decide to build an AI model to predict costs from CAD drawings. Vu’s portfolio of applied research projects and interests include routing, scheduling, assignment optimization under uncertainty, geospatial-temporal forecasting, imperfect information games, natural language processing, and computer vision. Prior to Flexport, Vu was the Head of Data Science and Analytics at WeWork, and the Chief Data Scientist at Boeing. He has over 20 years of experience implementing vision, transforming unmet business opportunities into realized software solutions.
Conference: Transform X 2022
Authors: Glenn Hofmann
2022-10-19
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The speaker discusses the use of machine learning in various areas of their organization, including sales, finance, underwriting, recruiting, and fraud detection. They emphasize the importance of building relationships with the business, having infrastructure for data scientists, and experimenting with different business models. The speaker also shares anecdotes about specific challenges they faced in implementing machine learning.
- Machine learning is used in various areas of the organization, including sales, finance, underwriting, recruiting, and fraud detection
- Building relationships with the business is important for successful implementation of machine learning
- Infrastructure for data scientists is necessary, including data curation and development and deployment environments
- Experimentation with different business models is important
- Challenges include dealing with unstructured data and separating relevant information from irrelevant information
Conference: CloudOpen 2022
Authors: Jaehyun Sim
2022-06-23
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The presentation discusses the challenges faced in managing a Python Package Index (PyPI) server in a cloud-native environment and explores different options for hosting a PyPI server.
- The speaker discusses the challenges of managing a PyPI server in a cloud-native environment
- The speaker explores different options for hosting a PyPI server, including public PyPI, self-hosted PyPI, and cloud-based PyPI solutions
- The speaker emphasizes the importance of portability, security, resiliency, and speed in a PyPI hosting solution
- The speaker shares an anecdote about the challenges of managing a tangled codebase with embedded machine learning models in multiple services
- The speaker suggests separating the machine learning model portion of the codebase into different repositories and managing them separately as packages in a PyPI server
Conference: OpenAI + Data Forum 2022
Authors: Chin Huang, Ted Chang
2022-06-23
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Overview of K-Serve with Model Mesh and demo of model inference using online features
- K-Serve is a standards-based model serving platform built on top of Kubernetes
- Model Mesh in K-Serve is designed to address Kubernetes' resource limitations and allows for high density and scalability
- Model Mesh architecture includes serving runtime deployments, containers for model mesh logic, adapters for retrieving models, and model servers for inference
- Scalability test showed that 20k simple stream models could be deployed into two serving runtime pods in a small Kubernetes cluster
- Demo showed integration of open source model mesh model serving layer with Feast for multi-region model serving in a Kubernetes cluster
Conference: OpenAI + Data Forum 2022
Authors: Patrick Titzler
2022-06-21
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The presentation discusses the development of a library pipeline feature for Jupyter notebooks to enable the creation of machine learning workflows. The feature includes a visual pipeline editor, CLI, and support for three different runtime environments. The goal is to make it easier to break down large notebooks into smaller ones and automate the execution of pipelines in a production environment.
- Library pipeline feature for Jupyter notebooks enables the creation of machine learning workflows
- Includes a visual pipeline editor, CLI, and support for three different runtime environments
- Goal is to make it easier to break down large notebooks into smaller ones and automate the execution of pipelines in a production environment
Conference: KubeCon + CloudNativeCon Europe 2022
Authors: Marcel Hild, Kenneth Hoste
2022-05-18
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The presentation discusses the challenges faced by data scientists in a cloud-native environment and how Open Data Hub and Red Hat Open Shift Data Science can help overcome these challenges.
- Data scientists face challenges in a cloud-native environment due to the lack of control over the environment and the need for specialized software
- Open Data Hub and Red Hat Open Shift Data Science provide a best-of-breed distribution of common data science tools in a cloud-native context
- The presentation includes a demo of how to identify dog breeds using Open Data Hub and how to add additional software to the mounted volume
- Red Hat Open Shift Data Science can be consumed as a service on cloud.reddit.com and integrates with other vendors
Conference: Transform X 2021
Authors: Nicol Turner Lee
2021-10-07
Join Nicol Turner-Lee, Senior Fellow at The Brookings Institution, as she explores the societal need for fair AI that is equitable to all. She describes the individual and enterprise consequences of bias in AI and the need for multi-disciplinary and multi-methodological approaches to preventing it. How does unintentional bias in AI occur? How do you self-regulate your AI algorithms, so they benefit everyone in the demographics you are trying to serve? How can the fields of data science, law, and sociology join forces to ensure that AI is unbiased? Join this session to hear a thoughtful and research-driven exploration about the risks, impacts, and approaches to mitigating unintended bias in AI.