Applied ML engineer building APIs, batch workflows, and practical data systems.

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Services & scopePortfolioData tools

Applied ML, APIs, and practical data systems.

The portfolio and Data tools pages are public proof of how I build. Below are fixed-scope engagements for teams—models, serving, and batch jobs first; spreadsheets or BI when that is what the problem needs.

Featured
ML systems, APIs & batch workflows
Build ML systems that run reliably — APIs for real-time decisions, batch pipelines for scoring, and retrieval systems grounded in your data. Delivered as a scoped engagement with code, clear boundaries, and handover your team can run.

Typical project range: €1,200 – €8,000

Codemodel or index artefactsrunbookPython
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Dashboard & KPI reporting
KPIs, trends, and breakdowns in Excel, Google Sheets, or Power BI—a single view built on your data, with light handover your team can maintain.

Typical project range: €300 – €900

DashboardKPI definitionsdocumentationExcel
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Data cleaning, profiling & prep
Messy tables turned into something you can trust: cleaning, profiling, and documentation so the next step—dashboards, forecasts, or automation—does not collapse under bad inputs.

Typical project range: €120 – €400

Clean dataprofile summarydocumentationExcel
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Reporting & spreadsheet automation
Stop rebuilding the same report every week. Scheduled or on-demand pipelines—in Sheets, Apps Script, or Python—that mirror the discipline behind batch scoring: same inputs, same rules, reproducible outputs.

Typical project range: €200 – €700

Automated workflowsscriptsdocumentationExcel
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Analysis, forecasting & AI-assisted reporting
Structured marketing and ops reporting, simple forecasts only where history is solid enough to justify them, and optional summaries over aggregates. Scoped to your exports and questions—not running campaigns or ad ops for you.

Typical project range: €150 – €550

Reportsforecasts where fitoptional AI summaryAnalytics
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Final pricing depends on data complexity and project scope.

How I Work

1. Define scope and outputs
We agree what to solve, what goes in, what must come out, and whether the core is an API, batch work, or a smaller reporting slice. Scope is clear in writing before anything is built.
2. Review data and limits
I check data shape, quality, and hard limits—what is reliable, what is missing, and what is out of scope. You get a straight answer on what the data can support.
3. Build the system
I build reproducible pipelines or services with clear steps. APIs and batch jobs come first when they fit; dashboards or extra reporting only when they belong in that system.
4. Validate and deliver
I test what matters for the agreed scope, keep runs repeatable under the same rules, and leave short notes your team can follow. You receive something you can run and own—not a throwaway demo.

Core stack

PythonSQLAPIsFastAPIBatch processingDockerpytestscikit-learnExcel, Sheets, Power BI, Looker Studio

Scope and fit

Send goal, current stack, data sources, and timeline. I will say whether the work matches applied ML, APIs, and batch workflows, or a narrower reporting and data-prep scope.

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© 2026 Vahdettin Karataş. All rights reserved.
Applied ML systems, APIs, and practical automation.
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