1. Applied Machine Learning Model Development for Research
From research question to a working ML model
This training focuses on building a machine learning model tailored to a specific scientific or industrial research problem.
- Translate a research question into an ML formulation
- Select an appropriate model type (supervised, unsupervised, hybrid)
- Define inputs, outputs, constraints, and evaluation criteria
- Train, test, and validate models using real research data
- Interpret results in a scientifically meaningful way
Outcome: A working ML pipeline relevant to your own research or domain.
2. Dataset Design, Construction & Validation for Machine Learning
How to build datasets that actually work
Many ML projects fail not because of the model — but because of the data. This training teaches how to design datasets for training, validation, and robustness testing.
- Design datasets for training, validation, and robustness testing
- Identify bias, leakage, and hidden correlations
- Structure data so it can evolve over time
- Validate datasets scientifically, not just statistically
Outcome: The ability to design datasets that support reliable ML conclusions.
3. Multi-Dataset Integration from Public & Scientific Sources
Learning from data that was never meant to work together
Different datasets rarely share the same attributes or features — even in the same field. This training teaches methods to integrate partially-overlapping datasets.
- Integrate multiple datasets with partially overlapping features
- Handle incomplete / inconsistent feature coverage across datasets
- Align, normalize, and reconcile heterogeneous parameters
- Combine datasets from publications, experiments, and repositories while preserving scientific meaning
Outcome: Robust ML-ready datasets from fragmented scientific data.
4. Building Domain-Specific Databases for Machine Learning
From raw data to a reusable ML knowledge base
- Structure databases around features, labels, metadata, and uncertainty
- Support iterative model training and re-training
- Integrate public databases with proprietary internal data
- Design for traceability and scientific auditability
Outcome: A blueprint (and often an implementation plan) for a domain-specific ML database.
5. Private, Offline AI Assistants & Company-Specific Chatbots
AI that knows your company — and nothing else
BIT trains organizations to build local, offline AI systems that operate without internet access and are trained exclusively on company proprietary knowledge.
- Train models on internal codebases, workflows, protocols, designs, and documentation
- Ensure data isolation and access control
- Deploy AI assistants usable only by company employees
Outcome: A secure internal AI system that understands your business deeply — without exposing data externally.
6. Converting Company Engineering Knowledge into a Shared, Interactive AI System
From siloed expertise to a living, collaborative internal intelligence
This training is designed for R&D and engineering managers who want to transform the company’s entire
engineering knowledge into a shared, interactive AI-powered system.
The goal is to optimize knowledge sharing, enhance performance, and strengthen cross-team collaboration.
- Define what constitutes company-wide engineering knowledge across software, hardware, mechanics, QA, and operations
- Design a structured process for extracting, validating, and maintaining proprietary knowledge assets
- Convert distributed engineering knowledge into a unified, queryable ML representation
- Enable access to this knowledge through a secure, local interactive chatbot
- Integrate the AI system directly into development environments and internal tools
- Create a living, continuously evolving internal knowledge base that improves over time
Outcome: A manager-defined, engineering-implementable framework for an internal AI system that turns
organizational knowledge into a shared, interactive resource — improving collaboration, decision-making, and execution speed.