Course: Cloud Computing and MLOps for AI & ML
duration: 3 hours |
Language: English (US) |
access duration: 90 days |

Details
In this course you will delve into various topics at the intersection of technology and decision-making. Discover the power of cloud computing in artificial intelligence (AI), exploring its benefits, challenges, and implementation strategies. Uncover the essentials of MLOps, understanding how it revolutionizes machine learning and AI development. Gain insights into the world of ML pipelines, learning about their importance, best practices, and testing methodologies.
Result
After completing this course, you will have a comprehensive understanding of cloud computing, MLOps, and ML pipelines, equipping you with the expertise to leverage cutting-edge technologies in decision-making.
Prerequisites
No formal prerequisites. Basic knowledge of cloud computing and AI and ML are recommended.
Target audience
Software Developer, Project Manager, Manager
Content
Cloud Computing and MLOps for AI & ML
Cloud Computing and MLOps: Cloud and AI
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.
Cloud Computing and MLOps: Introduction to MLOps
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.
Cloud Computing and MLOps: ML Pipelines
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.
Course options
We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.
Optional practice exam (trial exam)
To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam.
Optional practice lab
To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediately. The lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience.
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