DevOps Platforms and Operations: AIOps & MLOps Basics
DevOps
| Beginner
- 14 videos | 1h 25m 8s
- Includes Assessment
- Earns a Badge
MLOps is used to scale and govern machine learning operations, while AIOps provides a way to solve business problems and reduce incident management overhead using AI. In this course, you'll begin with an introduction to what AIOps is and which common IT operations it can be applied to. Discover AIOps capabilities, main business areas which AIOps can be incorporated into, and the benefits to an organization considering adoption. Next, explore various business use cases suitable for AIOps and adoption from a business and technical perspective. Moving on, look at the capabilities provided by MLOps and the benefits of MLOps to a data scientist. Next, explore the steps involved in implementing MLOps including the tools, challenges, and best practices involved. Upon completion of this course, you'll be able to identify the differences between AIOps, MLOps, and DevOps.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
Recognize the uses for artificial intelligence (ai) in it operations
Recognize opportunities to implement aiops within an organization
Outline an approach for implementing aiops
List examples of common aiops tools and how to evaluate them for your requirements
Outline best practices for an aiops implementation
Recognize when and why to adopt a machine learning operations (mlops) practice -
Recognize the uses for mlops from the perspective of a data scientist
Outline the steps required to implement mlops in an organization
Identify mlops tools and how to evaluate them for your requirements
Recognize challenges you might face when implementing an mlops practice
Outline best practices for an mlops implementation
Identify the differences between aiops, mlops, and devops and when to use each
Summarize the key concepts covered in this course
IN THIS COURSE
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23s
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7m 32s
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3. AIOps Use Cases9m 16s
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4. AIOps Implementation8m 17s
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5. AIOps Tools7m
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6. AIOps Best Practices6m 57s
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7. Machine Learning Operations (MLOps)5m 5s
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8. MLOps for the Data Scientist6m 30s
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9. MLOps Implementation6m 3s
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10. MLOps Tools Evaluation7m 4s
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11. Challenges in MLOps7m 59s
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12. MLOps Best Practices5m 50s
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13. AIOps vs. MLOps vs DevOps6m 29s
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14. Course Summary43s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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