2. What skills will I gain from LLMOps training?

You can develop skills in LLM application deployment, evaluation, prompt management, model configuration, monitoring, observability, security, and production optimization. The course also covers automated testing, token tracking, latency monitoring, and quality management.

3. How is LLMOps different from traditional MLOps?

LLMOps shares several principles with MLOps, including deployment, monitoring, testing, automation, and governance. However, LLM applications introduce additional considerations such as prompt versioning, token usage, conversational context, response quality, hallucinations, and model-specific evaluation.

4. Does the course cover LLM evaluation and testing?

Yes. The curriculum covers evaluation strategies, datasets, response quality, accuracy, relevance, hallucination detection, and automated evaluation. Learners also explore how evaluation can be incorporated into ongoing LLM application workflows.

5. What practical projects are included in the LLMOps course?

Projects include building an LLM deployment pipeline, creating an automated evaluation system, implementing application monitoring, managing prompt versions, and optimizing production LLM applications. The final project brings these components together into an end-to-end LLMOps workflow.

LLMOps Course: Deploy and Manage LLM Applications

Category Data Analytics & AI

Level Experts

Students 13 Already Enrolled

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An LLMOps course focuses on the systems and workflows required to develop, deploy, evaluate, monitor, and maintain large language model applications in production. This course introduces the lifecycle of LLM applications and the technologies used to manage reliable, scalable, and secure AI services.

Learners explore LLM architectures, APIs, prompts, templates, conversation state, model configurations, evaluation datasets, response testing, hallucination detection, deployment, containers, cloud infrastructure, and application scaling. The curriculum also covers prompt versioning, model comparison, experiment tracking, token usage, latency, errors, quality monitoring, and observability.

Security and governance are integrated throughout the course, including sensitive information protection, API secrets, prompt injection prevention, access controls, privacy, and governance practices. Through production-focused projects, learners build deployment pipelines, automated evaluation systems, monitoring solutions, and complete LLMOps workflows.

LLMOps foundations, application lifecycles, workflows, and tools
LLM application architecture, APIs, prompts, context, and configurations
LLM evaluation, testing, datasets, accuracy, relevance, and hallucinations
Deployment, containers, cloud infrastructure, scaling, and production environments
Prompt versioning, model management, experiments, and optimization
LLM monitoring, security, governance, privacy, and production workflows

A basic understanding of large language models, artificial intelligence, or software development is recommended. Familiarity with APIs, cloud platforms, programming, or application development can be beneficial for the technical sections.

This course is designed for AI engineers, software developers, ML professionals, DevOps engineers, cloud professionals, and technical teams building LLM applications. It is also suitable for professionals responsible for deploying, monitoring, evaluating, securing, or maintaining generative AI systems.

1.1 Understanding LLMOps
1.2 LLM Application Development Lifecycle
1.3 LLMOps Versus Traditional MLOps
1.4 LLM Production Challenges
1.5 LLMOps Tools and Technologies
1.6 Designing LLMOps Workflows

2.1 Designing LLM Application Architecture
2.2 Working with LLM APIs
2.3 Managing Prompts and Templates
2.4 Handling Context and Conversation State
2.5 Managing Model Configurations
2.6 Preparing LLM Applications for Deployment

3.1 Designing LLM Evaluation Strategies
3.2 Creating Evaluation Datasets
3.3 Testing Response Quality
3.4 Measuring Accuracy and Relevance
3.5 Detecting LLM Hallucinations
3.6 Automating LLM Evaluation

4.1 Understanding LLM Deployment Options
4.2 Deploying LLM Applications
4.3 Containerizing LLM Services
4.4 Managing Cloud AI Infrastructure
4.5 Configuring Production Environments
4.6 Scaling LLM Applications

5.1 Managing Prompt Versions
5.2 Testing Prompt Changes
5.3 Comparing LLM Models
5.4 Managing Model Configurations
5.5 Tracking Experiments
5.6 Optimizing Model Selection

6.1 Monitoring LLM Application Performance
6.2 Tracking Token Usage
6.3 Monitoring Response Latency
6.4 Detecting Quality Degradation
6.5 Tracking Errors and Failures
6.6 Building LLM Monitoring Dashboards

7.1 Protecting Sensitive Information
7.2 Managing API Keys and Secrets
7.3 Preventing Prompt Injection
7.4 Controlling Model Access
7.5 Managing Data Privacy
7.6 Establishing LLM Governance Practices

8.1 Building an LLM Deployment Pipeline
8.2 Creating an Automated Evaluation System
8.3 Implementing LLM Monitoring
8.4 Managing Prompt Versioning
8.5 Optimizing Production LLM Applications
8.6 Building an End-to-End LLMOps Workflow

An LLMOps course is suitable for AI engineers, developers, ML professionals, DevOps specialists, cloud engineers, and technical teams working with LLM applications. It can also benefit professionals transitioning from traditional ML operations into generative AI application management.

LLMOps Course: Deploy and Manage LLM Applications

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