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. 

If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals. 

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. 

We can collaborate with you to tune this course and level of coverage to target the skills you need most. The below agenda is estimated. Course agenda, topics and labs are subject to adjust during live delivery in response to student skill level, interests and participation. 

Day 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 

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 

Day 2 

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 
• Activity: Creating a test environment for a hypothetical AI application 

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 

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