Unlock the full potential of AI for your organization with this comprehensive two-day course, designed to emphasize the value of AI in operations and provide practical guidance on testing against AI systems. You’ll gain a solid understanding of AI and its applications, focusing on how AI can be used to streamline operations, improve decision-making, and optimize workflows. Throughout the event you’ll explore the AI testing lifecycle, how to evaluate AI model performance, and maintain security and ethical considerations. By the end of the training, you’ll have the knowledge and skills needed to harness the power of AI to drive operational excellence and effectively test AI systems.


* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on exercises and engaging group activities. Throughout the course you’ll learn how to:

- Develop the ability to identify and evaluate potential AI applications for enhancing operations within your organization, leading to improved decision-making and optimized workflows.
- Gain proficiency in designing and executing effective test plans for AI systems, ensuring the successful integration and
deployment of AI models in real-world operational environments.
- Acquire the skills needed to navigate the AI testing lifecycle, from the development and validation stages to the
deployment and monitoring of AI models, ensuring the reliability and quality of AI systems.
- Master the process of evaluating AI model performance using key metrics, allowing participants to assess the operational fit of AI models and strike a balance between performance, complexity, and cost.
- Develop a high-level understanding of security and ethical considerations in AI, equipping participants with the knowledge to implement AI systems responsibly and securely, mitigating potential risks and challenges.

  • Introduction to AI

  • What is AI?

    Difference between AI and Machine Learning

    Types of AI: Narrow AI vs. General AI

    Popular AI and ML algorithms

    AI applications in various industries

  • AI and ML in the current lifecycle

  • State of AI and ML today

    Recent advancements and limitations

    Future potential

  • AI in Operations

  • Operational use cases for AI

    Integrating AI into existing workflows

    AI-driven decision making

    Identifying potential AI applications in your organization

  • Implementing and testing AI in companies

  • Case studies of successful AI implementations

    Test cases from real-world AI rollouts

    Overcoming common challenges during AI implementation and testing

    Activity: Designing a test plan for a hypothetical AI application

  • AI testing lifecycle

  • Overview of the AI testing lifecycle

    Development, validation, and deployment phases

    Ensuring AI model quality and reliability

    Activity: Identifying key testing milestones in an AI project

  • Testing AI in an operational environment

  • Preparing the test environment

    Types of tests for AI systems

    Monitoring AI system performance

    Handling AI system failures and updates

  • Evaluating AI model goodness and performance metrics

  • Key performance metrics for AI models

    Determining the operational fit of AI models

    Balancing performance, complexity, and cost

  • Security and ethical considerations

  • Security concerns in AI implementations

    Ethical considerations in AI and ML

    Strategies for ensuring AI security and ethics

  • Resources and next steps

  • Continued learning resources

    Online courses, books, and communities

    How to stay updated on AI developments

    Closing discussion and feedback


The ideal audience for this course includes professionals involved in the development, testing, deployment, or management of technology solutions and are seeking to leverage AI and machine learning to optimize their organization's operations. Key roles that would benefit from this course include: - Test Engineers and Quality Assurance Analysts - IT Managers and Project Managers - Business Analysts and Operations Managers - Software Developers and Engineers




In order to be successful in the course you should possess: - Basic understanding of technology systems: Attendees should have a foundational knowledge of technology systems, such as software applications, databases, and networks, to better comprehend AI integration and its implications in operational environments. - Familiarity with data analysis and interpretation: Participants should have experience in working with data, including basic data analysis and interpretation skills, as AI and machine learning often involve utilizing data for decision-making and predictions. - Problem-solving and critical thinking skills: Attendees should possess strong problem-solving and critical thinking abilities, as these skills are essential when identifying potential AI applications, designing testing strategies, and evaluating AI model performance in operational contexts.


Length: 2.0 days (16 hours)


Not Your Location? Change

Course Schedule:

To request a custom delivery, please chat with an expert.