The goal of this module is not to train you to build AI/ML models hands-on, but rather to explain the necessary concepts of ML lifecycle management.

By the end of this module, you will be able to:

Efficiently collaborate with data scientists and engineers to build AI models that align with user expectations.

Understand and control for AI risks and effects.

Apply best practices in data collection, algorithm selection, model implementation, and continuous optimization.

With these skills, you will be well-equipped to contribute to the development of AI solutions that deliver incremental value to users while managing risks and ensuring alignment with business goals.

Now, let's closely examine the end-to-end Machine Learning (ML) development lifecycle. Discover the essential role you play as a product manager, practitioner, or business executive in this lifecycle. Throughout this video, I will highlight key points in the lifecycle where you can make significant decisions to guarantee that your product provides genuine value to users.

In Model Building, the primary step involves translating business requirements. This initial phase is crucial for aligning the model with the specific needs and objectives of the business. By converting these requirements into a model, it sets the foundation for further development and implementation processes.

In "Model Building: 2. Data Collection & Preparation," the focus is on the crucial steps of gathering and organizing data for modeling purposes. This stage is vital in ensuring the accuracy and effectiveness of the models created. By emphasizing the significance of data collection and preparation, the text highlights the foundational role these processes play in the overall modeling workflow. Through careful attention to detail and thorough preparation, model builders can enhance the quality and reliability of their models, ultimately leading to more robust and insightful outcomes.

In this section on Model Building, we delve into the realm of Exploratory Data Analysis. Here, we embark on a journey of discovery, seeking insights and patterns hidden within the data. By carefully examining the data through various visualizations and statistical techniques, we aim to uncover meaningful relationships and trends that can guide us in building accurate and robust models. Exploratory Data Analysis serves as a crucial first step in the model-building process, allowing us to gain a deeper understanding of the data and lay a solid foundation for subsequent analyses.

In the realm of model building, the crucial phase of Experimentation & Validation stands tall. This stage serves as the litmus test, where theories are put to the test and hypotheses are either confirmed or refuted. Through rigorous experimentation, models are refined and fine-tuned to align with real-world data, ensuring their accuracy and reliability. This process not only validates the model's effectiveness but also paves the way for further enhancements and advancements in the field.

Utilizing Do + Learn activities is a highly effective method to engage participants in hands-on learning experiences by collaboratively executing tasks outlined in authentic real-world case studies. This approach not only enhances the understanding of theoretical concepts but also allows individuals to apply their knowledge in practical scenarios, thereby bridging the gap between theory and practice.

By actively participating in these activities, individuals can gain valuable insights into the complexities of the subject matter, develop critical thinking skills, and improve decision-making abilities. Moreover, the interactive nature of Do + Learn activities fosters teamwork, communication, and problem-solving skills, creating a dynamic learning environment that encourages active participation and engagement.

Join weekly sessions to discuss these activities in more details.

Lets say you finalized one of several models your team has experimented. Now, its time to deploy those models to put in action.

Deploying AI is crucial as it transitions from theory to practical use. The process involves integrating AI into real-world settings to provide tangible benefits to users.

In this text, we delve into key model deployment concepts, including deployment mode, deployment performance, and deployment testing patterns. These aspects are crucial for ensuring the successful implementation of models in various operational environments. By understanding deployment modes, optimizing performance, and employing effective testing patterns, organizations can enhance the efficiency and reliability of their deployed models. This overview provides valuable insights for navigating the complexities of model deployment with precision and effectiveness.

The goal of this module is to familiarize you with AI Development Life-cycle and how you as a product manager or business executive participate in the development life-cycle along with you engineering team.

You learnt

a. Stipulate business problem

b. Describe how AI will contribute

c. Specify how AI should learn

d. Oversee AI Development Lifecycle

e. Participate & Perform Model Testing

f. Create “To-be” business process aligning with AI models and

g. Participate in Model Deployment Process