~/talks

Rishit Dagli

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titleconferencedate
Orchestrating Machine Learning on Edge Devices Using Akri and WebAssemblyKubernetes on Edge Day NA 20232023-11-06
Combines Project Akri and WebAssembly to discover cameras/phones as Kubernetes resources on a k3s cluster and run ML inference via a wasm shim and Akri…
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Lightning Talk: Orchestrating Machine Learning on Edge Devices...PyTorch Conference 20232023-10-16
Claim: PyTorch models can run efficiently on heterogeneous edge devices by compiling inference to WebAssembly, scheduling them via a Wasm broker, and treating…
Lightning on
Quantum Machine Learning 101: An Introduction for Beginners with...Open Source Summit North America 2023: Open Source On-Ramp2023-05-10
Demos build and convert quantum circuits into tensors using TensorFlow Quantum and Cirq, create a parametrized quantum circuit as a trainable layer, and run…
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GitOps for Machine Learning PipelinescdCon + GitOpsCon 20232023-05-08
Shows that GitOps applies beyond CI/CD by wiring GitHub, Kubernetes and Kubeflow to run, version and promote ML work: experiments are declared as PRs, 100…
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Making the Most Out of Your Hardware Accelerators in a Kubernetes ClusterKubernetes Batch + HPC Day Europe 20232023-04-18
Naive runs with one Tesla T4 plus one vCPU are slower than two vCPUs because the GPU spends long idle time waiting on CPU preprocessing; adding prefetching and…
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Supercharge Your Kubernetes AI Deployments with WasmKubeDay Japan 20222022-12-07
Claims a small WebAssembly AOT-compiled inference of MobileNet runs 2–2.5× faster than the same Python container and shows the benchmark code. Demonstrates…
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WebAssembly Based AI as a Service on the Edge with KubernetesKubernetes on Edge Day North America 20222022-10-25
Shows a concrete pipeline: Rust code compiles to a .wasm module, optionally AOT‑compiled with WasmEdge to native, runs TensorFlow Lite MobileNetV2 inference…
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Building Machine Learning Inference Through Knative Serverless...KnativeCon North America 20222022-10-24
Claims a concrete serverless path for model inference by using KFServing on Knative to run scikit‑learn, TensorFlow and PyTorch models with YAML that defines…
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⚡ Lightning Talk: Open Source, Kubernetes, And CloudNative From the Eyes Of a High-SchoolerKubeCon + CloudNativeCon North America 20222022-10-24
Claims hands-on experience contributing to Kubernetes and CNCF projects as a recent high-school grad and shows how that happened via new contributor meetups…
Lightning on
Deploying ML at Scale with Kubernetes and TFXOpen Source Summit Latin America 2022: Open AI + Data Forum2022-08-23
Deploys a SavedModel (ResNet ~110 MB stored on GCS) with TensorFlow Serving inside Kubernetes, exposing REST on port 8501 and gRPC endpoints, serving a sample…
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Federated Machine Learning with KubernetesOpen Source Summit Latin America 2022: Open AI + Data Forum2022-08-23
A concrete demo shows federated training of a Keras MNIST-style model wrapped with TensorFlow Federated where 10 simulated clients each train locally and send…
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WebAssembly Based AI as a ServiceOpen Source Summit Latin America 20222022-08-23
Claims that WebAssembly runtimes plus Rust give huge inference wins: benchmarks show 12–15x vs Node.js and ~25x vs Python; a MobileNet V2 demo runs in 215 ms…
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Deploying ML at Scale with Kubernetes and TFXOpen Source Summit Latin America 20222022-08-23
Shows a concrete pipeline that packages a TensorFlow SavedModel (ResNet ~110 MB) to TensorFlow Serving, deploys it in Kubernetes with a LoadBalancer, and…
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Federated Machine Learning with KubernetesOpen Source Summit Latin America 20222022-08-23
Shows a working federated training pipeline that uses TensorFlow Federated to train on an EMNIST-style dataset partitioned across 10 simulated clients, runs…
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WebAssembly based AI as a Service with KubernetesKubernetes Community Days Africa 20222022-07-07
Claim: WebAssembly runtimes deliver dramatically faster ML inference and much smaller images than Linux containers. Demonstrations use WasmEdge, QuickJS and…
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Superpower your Android apps with ML: Android 11droidcon APAC 20202020-12-14
Shows how to import a TFLite model into Android Studio, instantiate the generated class, and run inferences with three lines of code using the Model Binding…
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