DRI: @eduardobonet
MLOps is a Single-Engineer Group within our Incubation Engineering Department. This group works on early feature exploration and validation related to the MLOps group within the ModelOps stage.
Mission: Make GitLab a tool Data Scientists and Machine Learning Engineers love to use.
Vision: Identify opportunities in our portfolio to explore ways where GitLab can provide a better user experience for Data Science and Machine Learning across the entire Machine Learning life cycle (model creation, testing, deployment, monitoring, and iteration).
February 6th 2023: Model Experiment Tracking: Iterating on UX Review, Adding Usage Metrics
Machine Learning Experiment Tracking
Mission: Make it dead simple for Data Scientists to track their model experiments within GitLab, while making it easy to access the experiments results across the product (on Model Registry, on MRs, on Issues, etc)
More on ML Experiment Tracking
MLops is a large and young field. We used Jobs to be Done to make it explicit what are the problems users expect MLOps to solve:
We are keeping a backlog for potential exploration areas. Anyone is welcomed to pitch in new ideas using the Backlog Epic
| Reach | Impact | Confidence | Effort | Colab | MLOps Branding* | RICE+ | |
|---|---|---|---|---|---|---|---|
| JupyterLab-GitLab Plugin | 2 | 2 | 2 | 2 | 1 | 3 | 24 |
| Enable GitLab Runners for ML Use cases | 3 | 3 | 3 | 2 | 2 | 1.5 | 13.5 |
| Analysis Repository, GitLab Pages for Data Science | 1 | 2 | 1 | 2 | 1 | 3 | 6 |
| Improve pipeline usage for ML Use cases | 3 | 1 | 3 | 3 | 3 | 1 | 2 |
