Cloud computing is the on-demand delivery of computing services over the Internet. It enables scalable artificial intelligence (AI) and other advantages such as increased speed, scalability, and reduced cost. Through this course, learn about the role of cloud computing in AI. Explore the benefits and challenges of cloud computing, how to implement a cloud AI strategy, and the elements of the cloud computing architecture. Next, discover the importance of AI as a Service (AIaaS), the role of AI tools in data management and governance, and best practices for AI cloud security. Finally, learn about key cloud technologies for AI and emerging trends for cloud computing and AI. After course completion, you'll be able to outline elements of cloud computing in AI.

The term MLOps is a combination of machine learning (ML) and DevOps. Used across several industries, MLOps is a valuable method for developing and testing machine learning and artificial intelligence (AI) solutions. Through this course, learn the basics of MLOps. Explore the elements of XOps, MLOps, and DataOps and their uses. Next, examine the importance of version control in machine learning and learn about version control types and tools. Finally, discover the roles and responsibilities of humans in ML pipeline automation and investigate ethical considerations and best practices for MLOps. By the end of this course, you be able to define MLOps and recognize its uses.

ML pipelines help organizations improve the standards of machine learning (ML) models, improve their business strategy, and reduce redundant work and miscommunication. They consist of a series of ML workflow steps performed in a connected and automated/semi-automated way. Through this course, learn the basics of ML pipelines. Discover the uses and benefits of ML pipelines and the characteristics of manual and automated pipelines. Next, explore best practices for building pipelines and the three types of environments in the MLOps process. Finally, examine the importance of CI/CD in ML, the purpose of ML pipeline testing, and ML pipeline testing tools and frameworks. Upon completion, you'll be able to define ML pipelines and their benefits.

Cloud adoption is a strategy utilized by organizations to succeed in the cloud and achieve business outcomes. A sound cloud adoption strategy can help reduce costs, mitigate risks, and achieve scalability. Explore the costs, including hidden ones, associated with cloud adoption. Discover industries that can benefit from cloud adoption, including healthcare, finance, and education, and explore the benefits of automating IT processes through the cloud. Explore how to transition to the cloud and discover critical steps such as assessment, planning, adoption, migration, and management. Finally, explore cloud security considerations and cloud adoption patterns such as cloud-native, cloud-first, "lift and shift," offload, autonomy, incremental, chaos and anarchy, innovation, and experimental models.

Disasters are going to happen. This means that administrators must use the tools that let them survive even the worst-case scenarios. Explore disaster recovery technologies in this course. Consider cloud backup types, targets, and guidelines. See how to execute a point-in-time restore for an Azure SQL database. For governance, rate the capabilities of a service provider to provide disaster recovery. Weigh techniques to assure that a recovery site is available. Review components of a business continuity plan for the cloud. Study how to configure Azure SQL database geo-replication. As an exercise, create a backup plan that meets a scenario's goals.