17 May 2022
Valencia, Spain
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Machine Learning [clear filter]
Tuesday, May 17

09:40 CEST

MLExray: Observability for Machine Learning on the Edge - Michelle Nguyen, Stanford
Anyone who’s ever deployed on the edge has had this hope before: “It ran perfectly on my cloud environment, it’ll surely work when I deploy it across these other different environments”. Unfortunately, much of the time, this hope falls flat. This is frustratingly true for those deploying machine learning models on the edge. These models are often painstakingly trained and fine-tuned over months and days to achieve those extra few percentage points of accuracy… Only to see performance drop by over 10% once deployed to an edge device. This session will cover common problems encountered when deploying machine learning models on the edge, and how MLExray, an open-source observability framework created at Stanford, can be used to help debug these issues when they inevitably occur. *MLExray has been accepted into MLSys 2022: https://arxiv.org/pdf/2111.04779.pdf

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Michelle Nguyen

Principal Engineer, New Relic
Michelle Nguyen is a Principal Engineer at New Relic working on Pixie. She was Pixie's first engineer and works across the stack--from Pixie's deployment mechanisms to its distributed query engine. Before Pixie, Michelle was at Trifacta helping build intuitive and interactive UIs... Read More →
avatar for Natalie Serrino

Natalie Serrino

Principal Engineer, New Relic
Natalie Serrino is a Principal Engineer at New Relic working on the Pixie open source project. She focuses on Pixie’s data layer, more specifically, the PxL language, the PxL compiler, and Pixie’s edge query engine for analytics.

Tuesday May 17, 2022 09:40 - 10:15 CEST
Pavilion 4, Room C | Level 2 | Central Forum

11:00 CEST

Empower Heterogeneous Edge AI Acceleration with K8s - Tiejun Chen & Zitong Xu, VMware
As an emerging trend in the area of edge computing, edge workloads tend to be managed and orchestrated by k8s. In the meantime, as the the top one workload of edge computing, edge AI accelerations have been enabled by different vendors' edge AI accelerators quickly, including Nvidia edge GPU series, Intel Movidius VPU, Google edge TPU, etc. Actually you can see many ASIC-based edge AI accelerators and even some high-end CPUs used in edge AI. Obviously, edge users have the challenges around empowering these heterogeneous edge AI on the edge with upstream k8s or those edge k8s versions due to missing a general unified framework on k8s. Here we'd like to introduce our unified framework as a plugin to k8s with the following key mechanisms - 1. Extend the Node Feature Discovery to detect edge AI accelerators automatically 2. Unify different vendors' device plugin to provision work nodes according to it's own edge AI accelerator 3. Introduce transparent backend accelerations to boost ML upstream frameworks such as Tensorflow, Pytorch, etc 4. Attaching remote GPU to edge In our project we provide a unified edge AI framework to help k8s empower heterogeneous AI acceleration on the edge.

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avatar for Tiejun Chen

Tiejun Chen

Technical Leader & Architect, VMware
Tiejun Chen is one technical leader and Architect from ATG, Advanced Technology Group, VMware OCTO. Before joined VMware, he ever worked at Wind River System Linux and Intel OTC - Opensource Technology Center. He ever made many presentations at kubecon China 2021, LC3 China 2017... Read More →

Zitong Xu

Developer, VMware
Zitong Xu is from VMware OCTO - ATG. She is the core developer specific to machine learning and k8s, and many experiences of building edge computing with the partners and the customers.

Tuesday May 17, 2022 11:00 - 11:30 CEST
Pavilion 4, Room C | Level 2 | Central Forum

13:35 CEST

Model Serving at the Edge Made Easier - Paul Van Eck & Animesh Singh, IBM
As edge devices consume the world, the ability to deploy AI models on these devices becomes increasingly vital. Challenges surrounding the management of numerous models across a multitude of edge hosts can be tricky. Not only that, the limited compute power that edge hosts provide makes it necessary to eliminate as much overhead as possible. These are common pain points holding users back from large scale adoption. However, with the combination of ModelMesh with technologies like K3s and MicroShift, the practicality of employing such a system has increased dramatically. As the multi-model serving backend of KServe, ModelMesh offers a small-footprint control-plane for managing model deployments on Kubernetes. Using multi-model runtimes with intelligent model loading/unloading, ModelMesh is able to make the most out of a limited set of resources while still providing the capability to serve many models for inference. Come to this talk to get the edge on edge model serving!

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avatar for Paul Van Eck

Paul Van Eck

Open Source Software Engineer, IBM
Paul Van Eck is a software engineer in the Cognitive OpenTech Group at IBM. Over the past several years, he's been actively involved in open source AI technologies such as Kubeflow and TensorFlow. Currently, Paul is focused on the supporting the deployment of ML models on Kubernetes... Read More →
avatar for Animesh Singh

Animesh Singh

Distinguished Engineer and CTO - Watson Data and AI OSS Platform, IBM
Animesh Singh is CTO and Director for IBM Watson Data and AI Open Technology, responsible for Data and AI Open Technology strategy. Creating, designing and implementing IBM’s Data and AI engine for AI and ML platform, leading IBM`s Trusted AI efforts, driving the strategy and execution... Read More →

Tuesday May 17, 2022 13:35 - 14:05 CEST
Pavilion 4, Room C | Level 2 | Central Forum
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