zama-ai/concrete-ml
Nov 2024 – Oct 2025
8 Months active
Languages Used
BashJinjaJupyter NotebookPythonYAMLMakefileShellpython
Technical Skills
Backend DevelopmentCI/CDDeep LearningFHE (Fully Homomorphic Encryption)GitHub ActionsMachine Learning
Jordan Fréry
PROFILE
Jordan Fréry
Jordan Frery contributed to the zama-ai/concrete-ml repository by engineering robust backend and CI/CD workflows that improved model deployment, benchmarking, and release reliability. He developed automated pipelines for reproducible CIFAR-10 and ResNet18 benchmarks, integrated GPU and hybrid FHE benchmarking, and enhanced model customization through LoRA fine-tuning for LLaMA. Using Python, PyTorch, and GitHub Actions, Jordan addressed serialization issues, streamlined developer setup, and maintained artifact integrity for reproducible results. His work emphasized operational stability, efficient resource usage, and compatibility across environments, demonstrating depth in backend development, DevOps, and machine learning deployment while supporting secure, enterprise-ready ML workflows.
Overall Statistics
Feature vs Bugs
79%Features
Repository Contributions
30Total
Bugs
4
Commits
30
Features
15
Lines of code
28,408
Activity Months8
Your Network
44 people
Work History
October 2025
5 Commits • 5 Features
Oct 1, 2025October 2025 (2025-10) monthly summary for zama-ai/concrete-ml. Delivered a suite of performance-focused features and CI improvements that establish a solid foundation for benchmarking, secure ML workloads, and enterprise-ready deployment. Key features delivered include GPU benchmarking in CI for CIFAR-10, a hybrid FHE ResNet18 benchmarking workflow, an LLM fine-tuning benchmark for Llama LoRA, embedding layer support in the FHE client-server deployment, and CI stability improvements by temporarily disabling Dependabot during active development. These efforts improved benchmarking fidelity, reduced CI noise during releases, and expanded end-to-end capabilities.
5 Commits • 5 Features
Oct 1, 2025October 2025 (2025-10) monthly summary for zama-ai/concrete-ml. Delivered a suite of performance-focused features and CI improvements that establish a solid foundation for benchmarking, secure ML workloads, and enterprise-ready deployment. Key features delivered include GPU benchmarking in CI for CIFAR-10, a hybrid FHE ResNet18 benchmarking workflow, an LLM fine-tuning benchmark for Llama LoRA, embedding layer support in the FHE client-server deployment, and CI stability improvements by temporarily disabling Dependabot during active development. These efforts improved benchmarking fidelity, reduced CI noise during releases, and expanded end-to-end capabilities.
October 2025
September 2025
1 Commits
Sep 1, 2025September 2025 – Maintenance and benchmark integrity for zama-ai/concrete-ml. Delivered a fix for the MNIST Deep CNN benchmark artifacts by updating pre-trained model files and correcting git-lfs object IDs and file sizes for MNIST_Deep100CNN.pt, MNIST_Deep20CNN.pt, and MNIST_Deep50CNN.pt to ensure benchmarks reference the correct artifacts. This ensures reproducible benchmarks, accurate performance reporting, and smoother onboarding for contributors. Demonstrated skills: artifact management (git-lfs), version control hygiene, benchmark validation, and attention to data integrity within ML tooling.
September 2025
1 Commits
Sep 1, 2025September 2025 – Maintenance and benchmark integrity for zama-ai/concrete-ml. Delivered a fix for the MNIST Deep CNN benchmark artifacts by updating pre-trained model files and correcting git-lfs object IDs and file sizes for MNIST_Deep100CNN.pt, MNIST_Deep20CNN.pt, and MNIST_Deep50CNN.pt to ensure benchmarks reference the correct artifacts. This ensures reproducible benchmarks, accurate performance reporting, and smoother onboarding for contributors. Demonstrated skills: artifact management (git-lfs), version control hygiene, benchmark validation, and attention to data integrity within ML tooling.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025: Key automation and repo hygiene enhancements for zama-ai/concrete-ml focused on reproducible benchmarks and clearer docs. Implemented automated CIFAR-10 benchmark scheduling, reduced repository clutter, and established a foundation for reliable weekly performance checks to support product planning and stakeholder reporting.
1 Commits • 1 Features
May 1, 2025May 2025: Key automation and repo hygiene enhancements for zama-ai/concrete-ml focused on reproducible benchmarks and clearer docs. Implemented automated CIFAR-10 benchmark scheduling, reduced repository clutter, and established a foundation for reliable weekly performance checks to support product planning and stakeholder reporting.
May 2025
March 2025
3 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for zama-ai/concrete-ml focusing on delivering reproducible deployment tooling, evaluating LoRA-based fine-tuning, and improving CI/CD reliability. The work delivered features and bug fixes across deployment automation, LLaMA LoRA evaluation notebook, and Ubuntu 24.04 compatibility in CI pipelines.
March 2025
3 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for zama-ai/concrete-ml focusing on delivering reproducible deployment tooling, evaluating LoRA-based fine-tuning, and improving CI/CD reliability. The work delivered features and bug fixes across deployment automation, LLaMA LoRA evaluation notebook, and Ubuntu 24.04 compatibility in CI pipelines.
February 2025
2 Commits • 1 Features
Feb 1, 2025Concise monthly summary for February 2025 highlighting key deliverables, reliability improvements, and technical achievements for zama-ai/concrete-ml.
2 Commits • 1 Features
Feb 1, 2025Concise monthly summary for February 2025 highlighting key deliverables, reliability improvements, and technical achievements for zama-ai/concrete-ml.
February 2025
January 2025
11 Commits • 2 Features
Jan 1, 2025Concise monthly summary for Jan 2025 for zama-ai/concrete-ml. Focus on delivering release workflow stability, CI improvements, and developer environment enhancements, with emphasis on business value and technical achievements.
January 2025
11 Commits • 2 Features
Jan 1, 2025Concise monthly summary for Jan 2025 for zama-ai/concrete-ml. Focus on delivering release workflow stability, CI improvements, and developer environment enhancements, with emphasis on business value and technical achievements.
December 2024
3 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for zama-ai/concrete-ml focused on delivering high-impact CI improvements and expanding model customization, while stabilizing test reliability. Key changes included: 1) CI Performance Enhancement: scaled CI capacity by switching to larger instance types (c6i.32xlarge) for weekly and release builds and enhancing the Makefile to parallelize pytest execution based on CPU cores, significantly improving test throughput; 2) LoRA Fine-Tuning for Llama: integrated LoRA adapters into the hybrid model paradigm, enabling client-side LoRA weights with server-side computation, and updating workflows, docs, and core library to support this capability; 3) Test Stability Fix for Hybrid Converter: addressed test flakiness by increasing the absolute tolerance for floating-point comparisons in test_hybrid_glwe_correctness. Overall, these changes reduced feedback cycles, broadened model customization options, and improved release confidence.
3 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for zama-ai/concrete-ml focused on delivering high-impact CI improvements and expanding model customization, while stabilizing test reliability. Key changes included: 1) CI Performance Enhancement: scaled CI capacity by switching to larger instance types (c6i.32xlarge) for weekly and release builds and enhancing the Makefile to parallelize pytest execution based on CPU cores, significantly improving test throughput; 2) LoRA Fine-Tuning for Llama: integrated LoRA adapters into the hybrid model paradigm, enabling client-side LoRA weights with server-side computation, and updating workflows, docs, and core library to support this capability; 3) Test Stability Fix for Hybrid Converter: addressed test flakiness by increasing the absolute tolerance for floating-point comparisons in test_hybrid_glwe_correctness. Overall, these changes reduced feedback cycles, broadened model customization options, and improved release confidence.
December 2024
November 2024
4 Commits • 2 Features
Nov 1, 2024Month 2024-11 — zama-ai/concrete-ml: Focused on CI/CD efficiency and backend integration to deliver more reliable releases and clearer backend state. Key features delivered in this period address CI resource optimization and GLWE backend readiness for linear layers. Improvements were paired with targeted fixes to stabilize the pipeline and improve test reporting. The work supports faster feedback cycles, reduced infrastructure waste, and clearer diagnostics for deployment decisions.
November 2024
4 Commits • 2 Features
Nov 1, 2024Month 2024-11 — zama-ai/concrete-ml: Focused on CI/CD efficiency and backend integration to deliver more reliable releases and clearer backend state. Key features delivered in this period address CI resource optimization and GLWE backend readiness for linear layers. Improvements were paired with targeted fixes to stabilize the pipeline and improve test reporting. The work supports faster feedback cycles, reduced infrastructure waste, and clearer diagnostics for deployment decisions.
Activity
Loading activity data...
Quality Metrics
Correctness86.4%
Maintainability84.0%
Architecture79.6%
Performance74.0%
AI Usage20.6%
Skills & Technologies
Programming Languages
BashBinaryC++JinjaJupyter NotebookMakefilePythonShellTOMLYAML
Technical Skills
Backend DevelopmentBenchmarkingBuild AutomationCI/CDData ScienceDebuggingDeep LearningDependency ManagementDevOpsDockerDocumentationFHEFHE (Fully Homomorphic Encryption)Full Stack DevelopmentFully Homomorphic Encryption (FHE)
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline