AI Training Sessions

Empower your full stack developers to become AI engineers who can build and deploy intelligent, AI-driven applications.

use cases

Why transform Full Stack Engineers
into AI Engineers?

Accelerate Development

Automate repetitive tasks, streamline testing, and boost productivity with AI-powered code suggestions and tools.

Improve Product Quality

Integrate advanced AI features, optimize user experiences, and adapt products to real-time data and user behavior.

Enhance Collaboration & Efficiency

Leverage AI-driven documentation, code reviews, and design suggestions to speed up development cycles.

Differentiator

Integrate pre-trained models and AI tools into existing products.

Design, develop, and deploy scalable AI systems.

Collaborate with data scientists, engineers, and stakeholders to align AI solutions with business goals.

Monitor performance, troubleshoot issues, and ensure reliability in production.

use cases

AI Engineers

Focus on practical implementation and deployment of AI solutions.

Data Scientists

Specialize in extracting insights and building new models.

ML Engineers

Concentrate on research, model optimization, and algorithm refinement.

use cases

Starting at $7,000 per group for a two-day session.

Custom add-ons available for specialized technologies or advanced topics.

Discounts offered for multiple team bookings or extended training sessions.

Overview of AI for Full Stack Software Developers: Foundational understanding of AI concepts, including machine learning and deep learning, focusing on how these fit into a full stack developer's skillset.
Latest Trends in AI: Explore recent breakthroughs in generative AI, LLMs (Large Language Models), and practical applications relevant to web and backend development.
Use Cases in Full Stack Development: How AI is transforming areas like intelligent user interfaces, backend automation, code generation, and testing.
AI Development Tools Overview: Introduction to tools like GitHub Copilot, Cursor, Supermaven, and other AI-driven IDE assistants that enhance productivity. Learn how to use them efficiently, including their strengths and weaknesses.

API integration for AI features: Integrate services like OpenAI API, Anthropic, and self-hosted open-source models into web apps for smarter front-end and backend capabilities.
Prompt engineering: Master prompt crafting to optimize language model outputs and tackle development challenges.
AI frameworks & libraries: Explore LangChain, LangGraph, LlamaIndex, and their integration into full-stack tech stacks.
Building agentic systems: Design systems that autonomously perform tasks using AI-driven decision-making.
Embeddings & vector databases: Use embeddings and vector databases for intelligent search and recommendations.
Retrieval-Augmented Generation (RAG): Combine retrieval methods with language models for context-aware responses.
Cost optimization: Manage and optimize AI system costs while balancing performance and budget.
Multimodal AI UI/UX: Create intuitive experiences combining text, images, voice, and other inputs/outputs.
AI system security: Implement best practices to secure sensitive data, maintain consistency, and prevent errors.
Responsible AI: Address ethical concerns like data privacy, bias, and transparency, with real-world mitigation strategies.

AI trends & predictions: What to expect in the next 2-5 years and how full stack developers can stay ahead by expanding their AI capabilities.
Use Cases in Full Stack Development: How AI is transforming areas like intelligent user interfaces, backend automation, code generation, and testing.