ML Strategy
Strategy before systems.Know what to build first.
These services are for organizations that want to move deliberately — with an honest picture of where they stand, a clear-eyed view of what's feasible, and a prioritized plan before committing to build.
ML Discovery
Identify the AI projects worth building before you invest.
Most organizations can identify several places where AI might help. The harder question is which of those applications are actually worth pursuing — and why. Teams generate long lists of possibilities without a clear framework for evaluating feasibility, data requirements, technical risk, or real business impact. The result is investment in projects that are technically interesting but strategically marginal, or initiatives that current data and infrastructure can't realistically support.
I identify the most promising AI opportunities within your organization and evaluates them against your data assets, technical capabilities, and business priorities. Through targeted stakeholder interviews and a rapid assessment of your available data and systems, I produce a prioritized list of potential AI applications — each assessed for feasibility, expected impact, implementation complexity, and organizational readiness. The output is a focused set of opportunities that are both technically realistic and strategically valuable, with clear guidance on which to pursue.
Prioritized list of 3–5 high-value AI opportunities tailored to your organization
Feasibility assessment for each opportunity covering data availability, technical complexity, and implementation risk
Estimated implementation scope and timeline ranges for each proposed project
Expected business impact and ROI framing for leadership evaluation
Recommendation for the most appropriate first project to pursue
Executive summary document suitable for leadership review
1 – 2 weeks: stakeholder interviews, rapid data and system review, opportunity analysis, and delivery of the final report.
$5,000 – $10,000
Pricing depends primarily on the number of stakeholders interviewed and the breadth of systems or datasets reviewed. Smaller organizations with a single product area sit toward the lower end; companies with multiple teams or more complex data environments fall toward the upper end.
ML Assessment
Understand your starting point before you invest.
Organizations often reach a point where they know ML could help — but don't have an honest picture of where they actually stand. Data infrastructure that seemed adequate turns out to be a bottleneck. Models that were never formally evaluated turn out to be underperforming. Team capabilities and organizational readiness are unclear. Without a reliable baseline, ML investments tend to go in the wrong direction, or at the wrong time.
I provide a comprehensive technical assessment of your organization's ML readiness — infrastructure, data pipelines, codebase quality, team capabilities, and operational processes. I interview your technical team, review your systems and code, evaluate your data assets, and synthesize findings into a prioritized roadmap with realistic timelines and effort estimates. You get a clear plan your leadership can act on.
ML readiness report covering infrastructure, data, codebase, team capabilities, and operational readiness
Prioritized improvement roadmap with effort estimates and sequencing recommendations
Slide deck for presentation to technical leadership and stakeholders
30 days of virtual support for questions on recommendations
3 – 4 weeks from kickoff to final report delivery.
$15,000 – $25,000
Scope is the primary driver: the number of systems and data pipelines reviewed, the size of the team interviewed, and whether the assessment covers a single product area or the full organization. Larger, more complex organizations sit toward the upper end.
ML Strategy Workshop
Clarity on where to invest your data science resources.
Leadership teams often sense their organization should be doing more with data and ML, but lack a clear framework for deciding what to prioritize, what's realistic given current capabilities, and how to sequence investments. Without that clarity, ML initiatives tend to be driven by what's technically interesting rather than what's strategically valuable — and the organization ends up with a portfolio of projects that don't go anywhere.
A facilitated full- or half-day strategy session with your executive and technical leadership. In the weeks before the workshop, I review your organization's goals, interview key stakeholders, and assess your current data maturity. The workshop surfaces high-impact ML opportunities across your organization, stress-tests them against your actual capabilities and constraints, and produces a prioritized execution plan with resource requirements. You leave with a shared strategic framework and a defensible rationale for what to build first.
Pre-workshop data maturity assessment and gap analysis
Customized workshop slide deck and facilitation materials
Prioritized ML opportunity roadmap with ROI framing
Resource requirements and team structure recommendations
Governance recommendations for data and ML initiatives
Executive summary of findings
2 – 3 weeks total: 1 – 2 weeks of preparation and stakeholder interviews, workshop day, 1 week for post-workshop deliverables.
$10,000 – $20,000
Duration and the depth of pre-workshop preparation are the main drivers. Organizations that require more extensive stakeholder interviews or industry-specific customization sit toward the upper end.
ML Training Workshop
Practical ML skills your team can apply immediately.
Generic ML courses don't translate to your business problems. Your team works through curriculum designed for someone else's data and someone else's use cases, and leaves with knowledge they struggle to apply. The gap between what your team knows and what they need to know to work effectively with ML systems keeps widening — not because they aren't capable, but because the training was never designed for their actual context.
A customized full-day training session built around your team's actual skill level, your real data, and your specific business problems. Before the workshop, I assess your team's capabilities and learning objectives and design a curriculum that meets them where they are. The day combines conceptual foundations, live coding demonstrations, and hands-on exercises using your own data where appropriate. Every participant leaves with working code and reference materials they can return to. Post-workshop office hours ensure the learning transfers to real work.
Customized curriculum and complete slide deck
Hands-on exercise notebooks with your data or realistic synthetic examples
Code examples and reusable templates
Reference documentation and curated reading list
Two post-workshop office hours sessions within 30 days
3 – 4 weeks total: 2 – 3 weeks of curriculum development and preparation, workshop day, 1 week for materials distribution and follow-up.
$10,000 – $20,000
The level of curriculum customization and the technical depth of the audience are the main drivers. A workshop for business stakeholders covering ML fundamentals differs significantly in preparation from one for an engineering team building recommender system. Participant count and data requirements also affect scope.