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Conference: Transform X 2022
Authors: Richard Socher, Russell Kaplan
2022-10-19
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The speaker discusses the use of large language models in search engines and how they can improve productivity for users. They also mention the potential for AI in healthcare, agriculture, and robotics.
- Large language models are being incorporated into search engines to generate essays and provide code completion
- Other companies can contribute apps to the ecosystem
- AI can improve healthcare by making processes more efficient
- AI can also be used in agriculture and robotics
Conference: KubeCon + CloudNativeCon North America 2021
Authors: Maksim Chudnovskii, Igor Gustomyasov
2021-10-13
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The presentation discusses various methods and technologies used in machine learning cooperation systems for anomaly detection, root cause analysis, and predictive auto-scaling in Kubernetes clusters.
- The system is divided into two main parts: preparation and evaluation of models, and real-time execution of trend models
- Istio is used as a service mesh to collect service mesh metrics, and Permittelsa is used as a data layer to collect time series data
- Combining workloads in schedule groups can reduce network resource consumption and optimize overall latency
- Anomaly detection methods can significantly reduce the flow of notifications and automate the process of establishing monitoring thresholds
- Predictive auto-scaling can proactively predict the required number of service ports using time series data and feature generation
Conference: Transform X 2021
Authors: Soumith Chintala
2021-10-07
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The presentation discusses the journey of PyTorch, an open-source machine learning framework, and how the team focused on a specific market to excel and scaled their efforts while taking deliberate risks and measuring their progress.
- PyTorch focused on the ML researcher market and aimed to provide flexibility and debuggability in their framework
- The team took deliberate risks and made a bet on the future of the ML researcher market
- Measurement and metrics were important for PyTorch to track their progress and iterate on their product
- Scaling efforts required prioritizing and figuring out whether to vertically integrate or modularize the project
Conference: Transform X 2021
Authors: Mac Thornberry
2021-10-07
Former House Armed Services Committee Chairman Representative Mac Thornberry will discuss the national security implications of adopting AI and ML capabilities with Scale’s Head of Federal, Mark Valentine. Mark and Mac will outline potential reforms within the national security community to accelerate adoption.
Conference: Transform X 2021
Authors: Stephen Balaban
2021-10-07
In this session, Stephen Balaban, CEO of Lambda, shares a playbook for standing up machine learning infrastructure. This session is intended for any organization that has to scale up its infrastructure to support growing teams of Machine Learning (ML) practitioners. Stephen describes how large ML models are often built with on-premise infrastructure. He explores the pros and cons of this approach and how the workstations, servers, and other related resources could be scaled up to support larger workloads or numbers of users. How do you scale from a single workstation to a number of shared or dedicated servers for each ML practitioner? How can you use a single software stack across laptops, servers, clusters, and the cloud? What are the network, storage, and power considerations of each step in that journey? Join this session to hear some best practices for scaling up your machine learning platform, to serve the growing needs of your organization.