AI & Machine Learning
From raw data to an intelligent capability in your product
Design and implementation of ML and deep learning models, and controlled LLM integration into systems.
Problem
Problems I work on
Data without insight
Data accumulates in the system but never becomes a decision, prediction, or action.
Need for an intelligent capability
A product needs a capability that simple rules cannot provide.
LLM integration
A language model must be used in a product in a controlled and reliable way.
Insufficient current model
The existing model lacks the accuracy, speed, or stability required and needs a redesign.
Approach
What I do
Data analysis and feature design
Inspecting data, cleaning it, and building features aligned with the real goal.
Model design and training
Choosing the right approach — from classical models to deep learning — then training and tuning.
Evaluation and validation
Evaluating with metrics tied to the final decision, not only generic benchmarks.
Inference API and integration
Deploying the model as a service your product can call.
Documentation
Documenting data, model, assumptions, and reproducibility.
Engagement
How engagement works
- 01
Clarify the need
Clarify the decision the model should improve and what success looks like.
- 02
Work with data
Review existing data and design a collection path if data is insufficient.
- 03
Model and evaluation
Training, tuning, and evaluating against product-relevant metrics.
- 04
Deploy and hand off
Integration, documentation, and knowledge transfer.
Technical evidence
Experience in machine learning, neural networks, and related research.
- Related projects on GitHub
- Research background in machine learning and computational modeling
- Detailed case studies will be added in a later phase.
FAQ
- Which kinds of models?
- From classical machine learning to neural networks and deep learning; the choice follows the data and the problem.
- What is your approach to LLMs?
- Controlled, reliable use of LLMs in products, with attention to cost, latency, and correctness.
- I have no data yet — can we still start?
- Yes. A data collection path is designed first so effort is not wasted on aimless data.
- How does the model reach my product?
- Typically as an inference API that your system can call.
Need an intelligent capability in your product?
Describe the problem and data; we will talk about the right approach.