Deploying and Optimizing LLMs with Ollama Training Course
Ollama offers a streamlined approach to deploying and executing large language models (LLMs) locally or within production settings, granting users complete control over performance metrics, cost efficiency, and security protocols.
This instructor-led, live training session (available online or onsite) is designed for intermediate-level professionals seeking to deploy, optimize, and integrate LLMs using Ollama.
Upon completion of this training, participants will be able to:
- Configure and deploy LLMs using Ollama.
- Optimize AI models to enhance performance and operational efficiency.
- Utilize GPU acceleration to significantly improve inference speeds.
- Seamlessly integrate Ollama into existing workflows and applications.
- Monitor and sustain the performance of AI models over time.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical applications.
- Hands-on implementation within a live-lab environment.
Customization Options
- To request customized training for this course, please contact us to arrange.
Course Outline
Introduction to Ollama for LLM Deployment
- Overview of Ollama’s capabilities.
- Advantages of deploying AI models locally.
- Comparison with cloud-based AI hosting solutions.
Setting Up the Deployment Environment
- Installing Ollama and its required dependencies.
- Configuring hardware and GPU acceleration.
- Dockerizing Ollama for scalable deployments.
Deploying LLMs with Ollama
- Loading and managing AI models.
- Deploying models such as Llama 3, DeepSeek, Mistral, and others.
- Creating APIs and endpoints for AI model access.
Optimizing LLM Performance
- Fine-tuning models for efficiency.
- Reducing latency and improving response times.
- Managing memory and resource allocation.
Integrating Ollama into AI Workflows
- Connecting Ollama to applications and services.
- Automating AI-driven processes.
- Utilizing Ollama in edge computing environments.
Monitoring and Maintenance
- Tracking performance and debugging issues.
- Updating and managing AI models.
- Ensuring security and compliance in AI deployments.
Scaling AI Model Deployments
- Best practices for handling high workloads.
- Scaling Ollama for enterprise use cases.
- Future advancements in local AI model deployment.
Summary and Next Steps
Requirements
- Foundational experience with machine learning and AI models.
- Familiarity with command-line interfaces and scripting.
- Understanding of deployment environments, including local, edge, and cloud setups.
Audience
- AI engineers focused on optimizing local and cloud-based AI deployments.
- ML practitioners responsible for deploying and fine-tuning LLMs.
- DevOps specialists managing the integration of AI models.
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