Your 90-Day ML Plan, Built in 3 Days
Stack decisions, build-vs-buy analysis, team structure, and a sequenced roadmap — the strategic foundation before you commit to an architecture.
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The ML Strategy & Roadmap sprint is the starting point for teams that need to make consequential ML infrastructure decisions in the next 90 days — and need to make them correctly the first time.
Why Strategy Before Architecture
The most expensive ML infrastructure decisions are the ones made implicitly — by choosing a tool, starting a build, or hiring for a role without first establishing the strategic frame that determines whether those choices are correct.
Stack decisions are not reversible on short timescales. Choosing a training infrastructure and building your data pipeline around it creates dependencies that take quarters to unwind. The same is true for feature store architecture, model serving infrastructure, and MLOps tooling. Getting these decisions right before you build is worth the 3-day investment.
Build-vs-buy analysis requires a structured framework. Most ML teams evaluate vendors by talking to sales reps and reading documentation. The analysis we deliver scores options against your specific constraints — team size, latency requirements, data volume, budget, and vendor lock-in tolerance — and produces a recommendation you can defend to your board.
What the Roadmap Delivers
A 90-day ML roadmap is not a list of features. It is a sequenced set of capability decisions:
What to build first — and why that sequence is correct given your dependencies. The roadmap shows which infrastructure decisions unblock which product capabilities.
What to buy vs. build — for each ML infrastructure component, with the scoring matrix that produced the recommendation. Decisions documented with rationale survive team changes; decisions documented as conclusions do not.
Who to hire — and in what order. ML team structure recommendations are tied to roadmap milestones: the roles you need for Phase 1 are different from the roles you need for Phase 3.
The Cost of Not Having a Roadmap
Teams without a shared ML roadmap make consistent, predictable mistakes: they build infrastructure that becomes a bottleneck at 10× scale, choose tools that conflict at integration, and hire for skills that become redundant two quarters later. The 3-day sprint pays for itself in the first major architectural decision it prevents.
Engagement Phases
Current State Assessment & Goal Alignment
Structured intake covering your current ML maturity, team capabilities, business objectives, and technical constraints. We map your existing stack, identify the decisions that need to be made in the next 90 days, and align on the criteria that will drive them — latency, cost, team expertise, vendor lock-in tolerance.
Stack Analysis & Decision Framework
Evaluation of your stack options against your specific constraints — training infrastructure, model serving, data pipeline, feature engineering, experiment tracking, and model registry. We produce a build-vs-buy analysis for each component and a stack recommendation with explicit rationale.
Roadmap Documentation & Debrief
Delivery of the 90-day ML Roadmap: sequenced initiatives, stack decisions documented with rationale, team structure recommendations, and a hiring plan if headcount is needed. Includes a 60-minute working session to walk through the roadmap with your team and finalise decisions.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| Strategic Clarity | Competing opinions on stack direction — no shared decision framework | Documented stack decisions with explicit rationale — team aligned in 72 hours |
| Stack Confidence | Weeks of vendor evaluation with no conclusion — decision paralysis | Build-vs-buy analysis complete — shortlist of 2–3 options with scoring rationale |
| Hiring Readiness | No clarity on what ML roles to hire next — generic JDs, wrong candidates | Role definitions tied to roadmap milestones — hiring plan ready to execute |
Tools We Use
Frequently Asked Questions
What makes a good ML roadmap — what should it actually contain?
A useful ML roadmap is not a Gantt chart of features. It is a sequenced set of infrastructure and capability decisions — what to build, what to buy, in what order, and why. It should document the constraints that drove each decision so future engineers understand the reasoning. The roadmap we deliver includes explicit dependencies between initiatives so you can see which decisions unblock which capabilities.
Who should be in the kick-off session on Day 1?
The technical co-founder or CTO, the ML lead (if separate), and optionally a business stakeholder who can articulate the commercial objectives for ML. We do not need a full engineering team — the session is designed to extract the right information efficiently from the people who have it. Most kick-offs run 90–120 minutes.
How are decisions documented so they don't get relitigated six months later?
Every major decision in the roadmap is documented with the decision itself, the options considered, the criteria used to evaluate them, and the rationale for the choice. This is the format used in Architecture Decision Records (ADRs). Teams that have this documentation relitigate significantly fewer decisions because the reasoning is captured, not just the conclusion.
What if our stack or priorities change after the roadmap is delivered?
The roadmap is a living document — we deliver it in a format you can maintain. More importantly, the decision framework and criteria we document are more durable than any specific recommendation. When your constraints change, the framework tells you which decisions need to be revisited and why.
Build ML that scales.
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