Tool or Technology
TT0055

Ollama in Action

Deploy, Integrate & Build Local AI Workflows

Ollama
Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Develop functional proficiency in installing, configuring, managing, and running open models using Ollama.
  • Select and configure appropriate language and multimodal models based on capability, hardware resources, context requirements, and business use cases.
  • Integrate Ollama with applications using REST APIs, Python/JavaScript SDKs, structured outputs, embeddings, and OpenAI-compatible interfaces.
  • Build practical local AI solutions using RAG, vision, tool calling, and reusable model configurations.
  • Apply performance, security, privacy, testing, and operational practices when deploying Ollama-based AI applications.

Technology covered

OllamaOllama CLIModel LibraryModelfilesREST APIPython SDKJavaScript SDKOpenAI-Compatible APIStructured OutputsEmbeddingsRetrieval-Augmented Generation (RAG)Tool CallingVision Models

Who should attend

  • AI & Machine Learning Engineers
  • Software Developers
  • Backend Developers
  • Full-Stack Developers
  • Data Scientists
  • Data Engineers
  • AI Application Developers
  • Solution Architects
  • DevOps & Platform Engineers
  • MLOps Engineers
  • Technical Leads
  • Enterprise AI & Innovation Teams

Prerequisites & Participant Readiness

  • Basic programming knowledge, preferably Python or JavaScript
  • Familiarity with command-line or terminal operations
  • Basic understanding of APIs and JSON
  • Basic understanding of Generative AI and Large Language Models is beneficial
  • Familiarity with application-development concepts is recommended
  • Basic knowledge of embeddings or vector search is helpful for the RAG module
  • No previous Ollama training is required

TOC Modules

Concepts
  • Understanding Ollama and its role in running AI models locally
  • Understanding Large Language Models, multimodal models, and embedding models
  • Understanding local AI versus cloud-hosted AI architectures
  • Exploring common enterprise applications for locally managed AI models
  • Understanding model size, parameters, context windows, and hardware requirements
Practical activities
  • Installing and validating Ollama
  • Running a first model using the Ollama CLI
  • Performing basic conversations and model interactions
  • Inspecting the local Ollama environment

Scenarios

Private Internal Knowledge Assistant

Internal Documents → Chunking → Ollama Embeddings → Semantic Retrieval → Relevant Context → Local LLM → Structured Answer → Human Validation

Participants build a locally operated knowledge assistant that searches internal reference information and generates contextual answers, improving research and information-retrieval efficiency while allowing the organisation to control the solution architecture.

Local AI Document Processing Workflow

Business Document / Image → Local Ollama Model → Information Extraction → Structured JSON → Validation → Business Application / Workflow

Participants build an AI-assisted document-processing workflow that transforms unstructured text or visual information into structured application-ready data, reducing manual extraction and documentation effort.

Related programmes

Continue with programmes from the same capability area.

Take the next step

Ready to make this programme work for your team?

Customise modules, duration and business scenarios for your team.

Instructor-ledVirtualHybrid

Designed around your roles, tools and real workflows.