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Applying Machine Learning and AI services on AWS Learning Path

The course is part of this learning path

Applying Machine Learning and AI Services on AWS
course-steps 5 certification 1 lab-steps 2


This course provides a quick review and summary of what was learned during the "Applying Machine Learning and AI services on AWS” Learning Path.



Congratulations on completing the "Working with Machine Learning and AI Services on AWS" Learning Path. 

We hope you found this content both informative and engaging. Before we finish, we'll provide a quick recap of the concepts we've covered. We started with the course on Distributed Machine Learning, where we showed you how to build an ML cluster with Apache Spark installed. 

We trained a decision tree machine learning model using MLlib and Scala. Next, you were introduced to the Amazon Deep Learning AMI and TensorFlow with our hands-on lab. In this lab, you launched an instance of this AMI and used the TensorFlow framework. 

Following on from this, we introduced you to the first of Amazon's AI application services, Amazon Rekognition. This course showed you how to integrate computer version features onto your own applications. From here, you were able to use an Amazon Rekognition lab to perform automated image labeling. 

And finally, we covered Amazon Lex, another of Amazon's AI application services. This course showed you how you can build Chatbot style interfaces, and again integrate into your own applications.

Okay, so that brings this learning path to a close. We suggest that you perform the final assessment exam if you haven't done so already. This will give you an idea of which areas you might need to continue or review. Please feel free to contact us at support@cloudacademy.com if you have any questions and/or comments. 

Congratulations again on completing this learning path. You're well on your way to mastering machine learning on the AWS platform.

About the Author

Learning paths17

Jeremy is the DevOps Content Lead at Cloud Academy where he specializes in developing technical training documentation for DevOps.

He has a strong background in software engineering, and has been coding with various languages, frameworks, and systems for the past 20+ years. In recent times, Jeremy has been focused on DevOps, Cloud, Security, and Machine Learning.

Jeremy holds professional certifications for both the AWS and GCP cloud platforms.