Course Outline

Introduction to Autonomous Systems for Government

  • Overview of autonomous systems and their applications in public sector operations
  • Key components: sensors, actuators, and control systems for government use
  • Challenges in the development of autonomous systems for government agencies

AI Techniques for Autonomous Decision-Making for Government

  • Machine learning models to enhance decision-making processes in government operations
  • Deep learning approaches for perception and control in governmental applications
  • Real-time processing and inference capabilities for autonomous systems used by government agencies

Autonomous Navigation and Control for Government

  • Path planning and obstacle avoidance techniques for government vehicles and equipment
  • Control algorithms to ensure stable and responsive navigation in public sector applications
  • Integration of AI with control systems for autonomous vehicles used by government entities

Safety and Reliability in Autonomous Systems for Government

  • Safety protocols and fail-safe mechanisms to ensure public safety in government operations
  • Rigorous testing and validation of autonomous systems used by government agencies
  • Compliance with industry standards and regulations specific to government use

Case Studies and Practical Applications for Government

  • Self-driving cars: AI algorithms and real-world implementations in government transportation services
  • Drones: Autonomous flight control and navigation in public sector surveillance and inspection
  • Industrial robots: AI-driven automation in government manufacturing and maintenance operations

Future Trends in AI-Powered Autonomous Systems for Government

  • Advancements in AI and their potential impact on governmental autonomy
  • Emerging technologies in autonomous system development for government use
  • Exploring future directions and opportunities for the application of autonomous systems in government operations

Summary and Next Steps for Government

Requirements

  • Experience in robotics or artificial intelligence development for government applications
  • Understanding of machine learning and real-time systems
  • Familiarity with control systems and safety protocols

Audience

  • Robotics engineers
  • Artificial intelligence developers
  • Automation specialists
 21 Hours

Number of participants


Price per participant

Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.

Testimonials (2)

the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose

Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow

I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.

Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life TM
Course - Kubeflow

Upcoming Courses

AI-Powered Autonomous Systems

2026-03-20 09:30
21 hours
Knoxville, TN – Regus First Horizon Plaza
$ 4054 (Online)
$ 5854 (Classroom)

AI-Powered Autonomous Systems

2026-04-03 09:30
21 hours
Lansing, MI - Regus - One Michigan Avenue
$ 4054 (Online)
$ 5854 (Classroom)

AI-Powered Autonomous Systems

2026-04-17 09:30
21 hours
Reno, NV - Regus - Mountain View Corporate
$ 4054 (Online)
$ 5854 (Classroom)

AI-Powered Autonomous Systems

2026-05-01 09:30
21 hours
Lexington, KY – Regus at Blazer Parkway
$ 4054 (Online)
$ 5854 (Classroom)

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