How to Develop Machine Learning Models in TensorFlow

Predictive analytics and automation—through AI and machine learning—are increasingly being integrated into enterprise applications to support decision making and address critical issues such as security and business intelligence. Public cloud platforms like AWS offer dedicated services that allow companies to easily implement deep learning models, sometimes even without requiring specialized skills in data modeling or analytics.

TensorFlow is an open-source, powerful framework for developing machine learning models. It is one of the most popular machine learning frameworks because it is flexible and it caters to different levels of data science knowledge. The Amazon Deep Learning AMI comes pre-configured with everything you need to start using TensorFlow from development to production. It allows teams to get started training quickly without worrying about dependencies or costly installations.

Eager to try it out? Before you rush to spin up an AMI on your own infrastructure, get practice with our new Hands-on Lab.  We’ll guide you and your team to create and serve machine learning models in a risk-free playground. Watch my short video below to preview what we’ll be working on in this new lab, TensorFlow Machine Learning on the Amazon Deep Learning AMI.

 Develop a TensorFlow machine learning model with our Hands-on Lab
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Written by

Logan Rakai

Logan has been involved in software development and research for over ten years, including four years in the cloud. At Cloud Academy, he is adding to the library of hands-on labs.


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