Enterprise-Grade ML, Without the Enterprise Platform Bill
ML Platforms cost real money — licensing, seats, implementation, often $100K+ a year before you’ve shipped a single model. Most companies don’t need the platform. They need the outcome: reliable models in production, decisions that hold up to scrutiny, and a system your team can actually run without a vendor on retainer.
I deliver that directly, at a fraction of the cost — as an independent data scientist with 25+ years of engineering experience and a PhD in machine learning. If you’ve already invested in a platform, I complement it: implementation support, model development, and the analytical judgment platforms don’t provide out of the box.
I use Knowledge-Centric Machine Learning Systems (KMDS) as the framework underneath the work — a methodology for delivering ML products with auditability, transparency, and reproducibility built in from day one. Because operational data is rarely clean or static, the real value comes from capturing and structuring the analytical knowledge, feature definitions, and data dependencies that make a solution sustainable long after the engagement ends.
Watch a short video describing my background and approach.
Who I Help
- Teams evaluating Dataiku, DataRobot, or similar platforms who are hesitant on cost or implementation time
- Organizations that already own a platform license but need hands-on help getting value from it
- Organizations with operational data collected daily, weekly, or monthly
- Leaders who need consistent definitions, lineage, and explainable decisions — without the platform overhead
- Groups focused on lending, customer analytics, operations, forecasting, service delivery, or asset management
How I’m Different From a Platform
A platform sells you software and leaves the thinking to your team. I do the thinking and build the system — direct engagement, no seat licenses, no minimum contract tiers.
I use Knowledge-Centric Machine Learning Systems (KMDS) as the framework underneath the work — a methodology for delivering ML products with auditability, transparency, and reproducibility built in from day one. Because operational data is rarely clean or static, the real value comes from capturing and structuring the analytical knowledge, feature definitions, and data dependencies that make a solution sustainable long after the engagement ends.
Explore the full KMDS methodology →
Featured Example: SBA Loan Analytics
Raw SBA loan data becomes the example for a repeatable operational analytics workflow — the same kind of system a platform would charge enterprise rates to build.
Raw SBA Loan Dataset
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Dataset Understanding
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Entity Identification
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Cleaning Recommendations
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Feature Engineering
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Modeling Ready Dataset
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Knowledge Preservation
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Repeatable Business Decisions
See the KMDS migration repository and technical overview article for details.
Technical Focus Areas
- Statistical Learning Systems
- Graph Machine Learning
- Analytical Infrastructure
- Optimization & Recommendation Systems
- Explainable AI Systems
- Knowledge Graph Systems for ML and AI explainability and traceability
Background
25+ years of engineering experience developing analytical solutions across retail, telecom, transport, finance, government, and software tools, plus formal PhD-level training in machine learning. Selected publications spanning scalable statistical learning, graph-oriented analytical systems, optimization, and enterprise-scale ML infrastructure are available on Google Scholar.
Let’s Talk
If you’re weighing the cost of a Dataiku or DataRobot license — or already have one and need someone to make it work — let’s talk about a direct engagement.
Available for contract and advisory engagements.