Course coverage

- [Instructor] What parts of the machine learning world are we covering in this course? Let's take a look at the activities in the machine learning ecosystem. In general, we can divide these activities into build activities and run activities. Build activities focus on creating and testing the model. Run activities focus on deploying, executing and monitoring the model. There are core machine learning activities in each of them. Feature engineering, model training, testing and packaging are some core ML activities on the build side. Model deployment and inference are the core activities on the run side. Knowledge and experience in core ML activities is a prerequisite for this course. Then, surrounding these core activities is MLOps, which again, can be split into build and run. On the build side of MLOps, we have various activities like requirements management, data and training pipelines, data governance, experiment tracking and integrations. On the run side of MLOps, we have infrastructure management, deployment, serving, monitoring, and responsible AI. For this course, we will only focus on the run side of MLOps. We will focus on build-side activities of MLOps in another course, MLOps Essentials: Model Development and Integration. For each activity in MLOps run, we will discuss the purpose and context for the activity. We will discuss techniques, methods, and approaches used. We will then touch upon the tools available for performing this activity, and also some of the best practices. Each of these activities have enough depth to warrant their own courses. We will be discussing only an overview of the activity and how they will fit in into the overall MLOps context. For a deep dive into these specific activities, I recommend further reading. What are the prerequisites for this course? This course provides a high-level overview of MLOps and does not need deep, technical hands-on experience. But it is recommended to have prior understanding of machine learning applications. Knowledge of core ML activities and prior experience in building and running machine learning models is desired. This experience can be either in doing or managing machine learning. This course is suitable for multiple roles, including data scientists, ML engineers, managers, and product owners working in the AI domain. Finally, the course MLOps Essentials: Model Development and Integration is a prerequisite for this course. That course covers the overview on MLOps, and also the development-side activities, which lead into the topics in this course. Finally, a word about various tools and technologies discussed in this course. The MLOps tools world is a rapidly evolving ecosystem. This can change significantly in a short period of time. So, it's recommended to evaluate the ecosystem periodically, especially when you get into actual implementation. So, we are recommending tools based on the status at the time of this recording, but it can change rapidly. Also, there are MLOps tools from the three big platforms, AWS, GCP and Azure. We are not going to discuss them specifically in this course, but do check them out if you are already on any of these platforms.