Practical AI training for developers, data scientists, and technical architects - from ML basics and prompt engineering to production deployment.
Target Audience
This training is designed for technical professionals who want to apply AI in practice.
Backend and full-stack developers
With programming experience who want to integrate AI capabilities into applications.
Data engineers and data scientists
Who want to build, train, and operationalize ML systems.
Technical architects and tech leads
Who are responsible for AI implementation decisions and architecture quality.
Investment in AI engineering capability creates measurable returns.
With AI-supported development workflows and robust RAG architectures, developer productivity typically increases by 14-32%. Instead of waiting for external consulting support, your team can deliver AI features from concept to deployment.
Only a minority of AI initiatives reach production. After this training, your team can assess model fit, hardware implications, and build-vs-buy tradeoffs more confidently, reducing failed experiments and wasted budget.
Each trained developer becomes an internal AI multiplier. They understand RAG architecture, model integration patterns, and production constraints, helping scale capability across teams and projects.
By the end of the training, participants build working prototypes such as RAG-driven document search, LLM-assisted code review workflows, and automated data extraction pipelines that can be extended into production.
Structured AI upskilling programs are linked to better retention. This training signals long-term investment in engineering growth and strengthens your position as a modern technology employer.
Training costs are frequently offset by the first project delivered in-house. Instead of paying high day rates for external AI consultants, your own engineering team can implement and iterate AI solutions directly.
Modules
Flexible modules - scope and depth tailored to your requirements:
Learning Outcomes
Practical capabilities your team can apply directly in production work.
Model selection, evaluation methods, and practical tradeoffs in real projects.
Integrate OpenAI, Claude, and local model options into your own applications.
Build reliable prompt workflows, structured outputs, and tool integrations.
Implement retrieval-augmented generation on your own organizational data.
Containerize, serve, observe, and scale AI workloads in production settings.
Address data privacy, prompt injection risk, and secure architecture practices.
FAQ