Azure-Samples/explore-iot-operations
Nov 2025 – Nov 2025
1 Month active
Languages Used
JSONRustYAML
Technical Skills
Configuration ManagementMachine LearningRustWebAssembly
Anca Antochi
PROFILE
Anca Antochi
During November 2025, contributed to Azure-Samples/explore-iot-operations by delivering two features focused on enabling machine learning inference within WebAssembly environments. Developed the WASI-NN ML Inference Interface, allowing edge workloads to leverage machine learning through the WASI-NN specification, and updated WebAssembly samples to align with the General Availability release. This work involved updating configuration files and metadata using Rust, JSON, and YAML, as well as synchronizing runtime snapshots to ensure compatibility with wasmtime-wasi-nn v36. The efforts improved onboarding efficiency and sample governance, with a technical emphasis on configuration management, machine learning integration, and WebAssembly runtime alignment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
691
Activity Months1
Work History
November 2025
2 Commits • 2 Features
Nov 1, 2025November 2025: Delivered two strategic features for Azure-Samples/explore-iot-operations: WASI-NN ML Inference Interface and GA-ready WebAssembly Samples, aligning samples with GA release and enabling ML inference in WebAssembly environments. No major bugs fixed this month; focus was on feature delivery and release readiness. Impact includes enabling edge ML workloads, improving onboarding efficiency, and strengthening sample governance for GA delivery. Technologies demonstrated include WebAssembly, WASI-NN, GA configuration/metadata management, and runtime snapshot alignment with wasmtime-wasi-nn v36.
2 Commits • 2 Features
Nov 1, 2025November 2025: Delivered two strategic features for Azure-Samples/explore-iot-operations: WASI-NN ML Inference Interface and GA-ready WebAssembly Samples, aligning samples with GA release and enabling ML inference in WebAssembly environments. No major bugs fixed this month; focus was on feature delivery and release readiness. Impact includes enabling edge ML workloads, improving onboarding efficiency, and strengthening sample governance for GA delivery. Technologies demonstrated include WebAssembly, WASI-NN, GA configuration/metadata management, and runtime snapshot alignment with wasmtime-wasi-nn v36.
November 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
JSONRustYAML
Technical Skills
Configuration ManagementMachine LearningRustWebAssembly
Repositories Contributed To
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