Premier Analytics Consulting at the Machine Learning Conference (MLCon) NYC 2026

This hands-on training seminar provides a practical introduction to building intelligent AI-powered applications using Python and modern large language model (LLM) architectures. Designed for data scientists, developers, analysts, ML engineers, researchers, and technical practitioners, this workshop focuses on how to design, build, and implement AI systems that combine language models with document-based retrieval workflows to deliver reliable, context-aware insights for analytical workflows. Registered attendees will learn how modern AI applications are built beyond simple prompting by integrating language models with embedding models, document parsing and chunking strategies, FAISS-based vector indexing and semantic search, and Retrieval-Augmented Generation (RAG) pipelines in Python. Through guided exercises and working Python examples, attendees will construct intelligent document-based workflows that enable language models to retrieve relevant information, analyze unstructured text, and generate grounded responses informed by real data. This workshop emphasizes practical implementation and architectural understanding, helping attendees move beyond using AI tools as black boxes and learn how to design reproducible AI systems for analytical pipelines, research environments, and enterprise applications using Python and LangChain-based workflows. Additional focus is placed on improving reliability and consistency through guardrails, reducing hallucinations, and managing probabilistic LLM behavior. Through structured demonstrations and hands-on exercises, attendees will learn how to: ➤ Understand the architecture of modern AI systems powered by large language models (LLMs) ➤ Implement effective prompting strategies for structured analytical tasks ➤ Parse, prepare, and chunk documents for downstream retrieval workflows ➤ Generate vector embeddings for documents and unstructured text collections ➤ Build and query a FAISS vector index to perform semantic retrieval over document chunks ➤ Design and implement Retrieval-Augmented Generation (RAG) pipelines in Python ➤ Develop fundamental LangChain-based workflows for document loading, retrieval, grounding, and response generation ➤ Evaluate AI outputs and apply prompting and coding guardrails to improve reliability and consistency ➤ Apply practical strategies to mitigate hallucinations and manage LLM variability By the end of this workshop, attendees will have built a functional AI RAG framework in Python capable of indexing documents, retrieving relevant information, and generating grounded, context-aware responses using modern LLM-based architectures.