Deploying AI Agents in Production Environments Training Course
Deploying AI agents into production environments is a critical step for operationalizing AI models and ensuring their scalability, reliability, and performance in real-world applications for government.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to master the techniques for deploying and managing AI agents in production environments within the public sector.
By the end of this training, participants will be able to:
- Design and implement scalable AI deployment pipelines for government use.
- Use tools like Docker and Kubernetes to containerize and orchestrate AI agents in a secure and compliant manner.
- Monitor and optimize AI agents in production environments to meet public sector performance standards.
- Implement CI/CD workflows for AI agent deployments that align with government IT practices.
- Ensure compliance with security and data governance requirements specific to the public sector.
Format of the Course
- Interactive lecture and discussion focused on government applications.
- Extensive exercises and practice sessions tailored to public sector scenarios.
- Hands-on implementation in a live-lab environment that simulates government IT environments.
Course Customization Options
- To request a customized training for this course, tailored to the specific needs of your government agency, please contact us to arrange.
Course Outline
Introduction to AI Deployment for Government
- Overview of the AI deployment lifecycle for government applications
- Challenges in deploying AI agents to production environments within the public sector
- Key considerations: scalability, reliability, and maintainability for government systems
Containerization and Orchestration for Government
- Introduction to Docker and containerization basics for government use cases
- Using Kubernetes for AI agent orchestration in government operations
- Best practices for managing containerized AI applications in the public sector
Serving AI Models for Government
- Overview of model serving frameworks (e.g., TensorFlow Serving, TorchServe) for government applications
- Building REST APIs for AI agent inference in government systems
- Handling batch versus real-time predictions for government operations
CI/CD for AI Agents in Government
- Setting up CI/CD pipelines for AI deployments in government agencies
- Automating testing and validation of AI models for government use
- Rolling updates and managing version control for government systems
Monitoring and Optimization for Government
- Implementing monitoring tools for AI agent performance in government operations
- Analyzing model drift and retraining needs for government applications
- Optimizing resource utilization and scalability for government systems
Security and Governance for Government
- Ensuring compliance with data privacy regulations in government operations
- Securing AI deployment pipelines and APIs for government use
- Auditing and logging for AI applications in the public sector
Hands-On Activities for Government
- Containerizing an AI agent with Docker for government systems
- Deploying an AI agent using Kubernetes for government operations
- Setting up monitoring for AI performance and resource usage in government applications
Summary and Next Steps for Government
Requirements
- Proficiency in Python programming for government applications
- Understanding of machine learning workflows and their integration into public sector projects
- Familiarity with containerization tools, such as Docker, to enhance deployment efficiency
- Experience with DevOps practices (recommended) to support continuous integration and delivery processes for government systems
Audience
- MLOps engineers working in the public sector
- DevOps professionals supporting government initiatives
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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