Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Even the most capable Kubernetes or MLOps stack fails if data scientists have to wait days on Jira tickets just to get a GPU workspace, an S3 bucket or a staging endpoint; teams either grind to a halt or build shadow infrastructure. This course applies modern Platform Engineering principles (Team Topologies, Platform as a Product) to the day-to-day workflows of AI and data science teams.

Designing low-cognitive-load abstractions, provisioning on-demand workspaces (JupyterHub, DevContainers, VS Code Remote), scaffolding end-to-end MLOps and RAG repositories via Backstage Software Templates (Golden Paths), enabling declarative self-service model deployment and measuring internal platform adoption are covered hands-on. Participants design a Golden Path template and self-service user journey for their own engineers.

Who Should Attend?

  • AI platform and MLOps engineers building Internal Developer Platforms (IDPs) for data scientists and ML engineers
  • Platform product managers and Developer Experience (DevEx) specialists running portals such as Backstage
  • Engineering leads looking to eliminate ticket-ops and shorten time-to-production for AI teams
  • Prerequisite: Git, Docker, CI/CD and working knowledge of Kubernetes or cloud infrastructure

Key Takeaways

  • Design Golden Path abstractions that let data scientists ship models without writing raw Kubernetes or Terraform
  • Build Backstage Software Templates and CLI scaffolders that provision Git repos, CI/CD, MLflow projects and storage in minutes
  • Provide on-demand remote GPU development environments with pre-baked images and automatic idle culling
  • Turn a concise model.yaml spec into a deployed, monitored and autoscaled inference endpoint via GitOps
  • Manage the AI platform as an internal product using Time-to-First-Model, deployment frequency and developer NPS metrics

Curriculum

AI Platform as a Product and Managing Cognitive Load
  • Eliminating ticket-ops, mapping data scientist versus ML engineer personas and bounding cognitive load
  • Balancing flexibility with standardisation: designing Golden Paths (Paved Roads) rather than rigid cages
On-Demand Cloud Workspaces and Interactive GPU Environments
  • Provisioning browser and IDE workspaces with JupyterHub, Kubeflow Notebooks and DevContainers
  • Curated base images, persistent home volumes and automatic idle-GPU culling to prevent resource hoarding
Project Scaffolding and Backstage Software Templates
  • One-click scaffolding for tabular ML, computer vision and RAG/agent repositories with Copier and Backstage
  • Automating Git repo creation, CI/CD wiring, MLflow/W&B project setup and a working day-one smoke pipeline
Declarative Self-Service Model Deployment Abstractions
  • Hiding raw KServe/Kubernetes boilerplate behind a concise model.yaml contract (model URI, hardware tier, latency SLA)
  • Using Crossplane, Helm library charts and ArgoCD to auto-provision endpoints, alerts and Grafana dashboards
Internal Developer Portals, Docs-as-Code and Adoption Metrics
  • Surfacing models, datasets, Feature Views and inference APIs inside a unified Backstage catalogue
  • Measuring platform ROI: Time-to-First-Model, deployment lead time, self-service ratio and internal developer satisfaction

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