Course Overview
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.
Exploring AI Operations: Strategies for Testing and Deploying Intelligent Systems for Success is a two day hands-on course that offers insights into real-world implementation and testing scenarios, equipping you with the tools and strategies necessary to ensure successful AI integration and deployment. This course focuses on the practical application of AI rather than the programming or math that underpin the algorithms and functionality.
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.
Who should attend
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 - Individuals responsible for ensuring the quality and reliability of software and systems, who need to develop robust testing strategies for AI-driven applications.
- IT Managers and Project Managers - Professionals overseeing technology projects and teams, who want to ensure the successful integration and deployment of AI and ML in their organizations.
- Business Analysts and Operations Managers - Professionals responsible for optimizing business processes and operations, who are interested in leveraging AI and ML to enhance efficiency and productivity.
- Software Developers and Engineers - Professionals responsible for building and maintaining software solutions, who seek to incorporate AI and ML technologies into their products or services.
- Data Scientists and Analysts - Individuals working with data to generate insights and make data-driven decisions, who seek to utilize AI and ML techniques for data analysis and prediction.
Prerequisites
In order to be successful in the course you should possess:
- Basic understanding of technology systems in business operations: 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.
Course 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.
Outline: Exploring AI Operations: Strategies for Testing and Deploying Intelligent Systems for Success (TTAI2130)
1. Introduction to AI
- What is AI?
- AI vs Machine Le
- Types of AI: Narrow AI vs. General AI
- Popular AI and ML algorithms
- AI applications in various industries
2. AI and ML in the current lifecycle
- State of AI and ML today
- Recent advancements and limitations
- Future potential
3. AI in Operations
- Operational use cases for AI
- Integrating AI into existing workflows
- AI-driven decision making
- Identifying potential AI applications in your organization
4. 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
5. 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
6. 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
- Activity: Creating a test environment for a hypothetical AI application
7. 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
- Activity: Evaluating a sample AI model using performance metrics
8. Security and ethical considerations
- Security concerns in AI implementations
- Ethical considerations in AI and ML
- Strategies for ensuring AI security and ethics
9. Resources and next steps
- Continued learning resources
- Online courses, books, and communities
- How to stay updated on AI developments
- Closing discussion and feedback