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Phase 2: Build & Migrate

Workload Starter

For new AI/ML workloads that haven't been built yet. ISC engineers work through discovery, architecture design, and produce a detailed implementation blueprint.

Building something new? You're in the right place. Migrating existing workloads? Start with the Workload Assessment instead.

Overview

The Workload Starter is for customers with new AI/ML workload ideas that have not yet been built. ISC engineers work through discovery, requirements gathering, architecture design, and produce a detailed implementation plan that becomes the blueprint for the Workload Accelerator phase.

The Starter exists so the build doesn't start on guesswork - it fixes architecture, resources, and scope before implementation, which is what makes the Accelerator predictable in cost and timeline.

What you get

  • Workload architecture document
  • Infrastructure design for PaletteAI deployment
  • Implementation plan with phases and milestones
  • Resource requirements and cost estimate
  • Technology stack recommendations

Quick facts

Prerequisite: Implementation completed
Typical duration: 1-3 weeks

How It Works

1

ML/AI Architecture

Define the model architecture, training and serving approach, and framework choices for the workload.

2

Data Architecture

Map data sources, storage, and pipelines - how data is ingested, versioned, and served to the workload.

3

Infra Architecture

Design target architecture on PaletteAI, select Cluster Profile stack, plan MLOps pipeline.

4

Implementation plan

Translate the architecture into a phased plan: milestones, resource and GPU requirements, cost estimate, and the scope basis for the Accelerator engagement.

5

Continuous Validation and Governance Architecture

Define monitoring, validation, and governance: quality gates, drift checks, and the controls that keep the workload compliant in production.

6

Review & handover

Present to stakeholders, deliver documentation, scope the Accelerator engagement.

Deliverables

  • Workload architecture document
  • Infrastructure design document
  • Phased implementation plan with milestones
  • Resource and cost estimate
  • Technology stack recommendation with rationale
  • Accelerator engagement scope document

This is for you if...

This service is for data science and ML teams that have a workload idea or a proof-of-concept but need expert guidance to design a production-ready architecture on PaletteAI.

It's also the right starting point if your team is new to Kubernetes-based AI infrastructure and wants a clear blueprint before committing to a full build.

Design your next AI workload

Got a workload idea? The Starter gives you a production-ready design. Contact us to get started.