AGILab in the MLOps Toolchain

AGILab focuses on the early experimentation phase of AI projects—roughly Technology Readiness Level 3 (TRL‑3), where teams validate concepts, explore algorithms, and collaborate on data preparation. This page explains how AGILab fits alongside the broader MLOps landscape.

Where AGILab helps

  • Rapid experimentation: templates, Streamlit pages, and CLI mirrors reduce the friction of testing new ideas without scaffolding bespoke dashboards.

  • Multi-algorithm workflows: built-in orchestration (AGI.run / AGI.get_distrib) lets engineers cycle through multiple models using the same datasets and environment setup.

  • Distributed execution without DevOps: Dask-based scheduling, SSH helpers, and worker packaging (agi_cluster, agi_env) allow TRL‑3 teams to scale out experiments without managing Kubernetes or cloud stacks.

  • Offline productivity: bundled Mistral/GPT‑OSS assistants and cached datasets keep experimentation running even on air-gapped networks.

What AGILab does not aim to cover

  • Production deployment (TRL‑6+): model serving, CI/CD, feature stores, online monitoring, or model drift detection belong to the deployment-focused side of MLOps (tools such as Kubeflow, MLflow Serving, Sagemaker, etc.).

  • Enterprise governance: compliance workflows, audit trails, or retraining policies are intentionally out of scope. AGILab’s strength is rapid iteration before promoting assets to hardened pipelines.

Positioning vs. other tools

Suggested workflow

  1. Use AGILab to prototype algorithms, reuse app templates, and validate data processing. Capture run history via ~/log/execute/<app>/.

  2. Once an approach stabilises, export the project (tools/run_configs and src/agilab/apps/<app>) and integrate it with your organisation’s deployment toolchain (MLflow, Kubeflow, internal devops stack).

  3. Track long-running metrics and governance artifacts using your preferred MLOps platform; AGILab does not replace those systems.

See also