Amazon Tensor Flow vs Anaconda

• Amazon Elastic Compute Cloud (EC2) allows resizable compute capacity in the cloud, providing the necessary elasticity to provide services for both, small and medium-sized businesses.
• Tensor Flow allows us to train our models much faster than in our on-premise equipment.
• Most of the pre-trained models are easy to adapt to our clients' needs.

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• First of all it is very easy to install and it is user friendly. You just download a Anaconda from its official site and you can start using it for coding (I usually code using Jupiter Notebook) Compared to Pycharm it is easier navigate in Anaconda(Jupiter Notebook)
• For me it is a best environment to use if I have small projects. Jupiter Notebook is running tasks much faster compared to Pycharm and other IDE's.
• In my work I usually need different scientific packages that are not commonly used. As Anaconda have thousands of libraries it helps me making my job easier

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• SageMaker isn't available in all regions. This is complicated for some clients overseas.
• For larger instances, when using a GPU, it takes a while to talk to a customer service representative to ask for a limit increase. Given this, it's recommendable to ask in advance for a limit increase in more expensive and larger cases; otherwise, SageMaker will set the limit to zero by default.
• Since the data has to be stored in S3 and copied to training, it doesn't allow to test and debug locally. Therefore, we have to wait a lot to check everything after every trail.

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• Anaconda could greatly benefit by integrating with Git and other versioning software.
• The software's default installation is relatively bloated, slower on older machines, and could be improved by allowing for a lean default installation environment.
• Anaconda has an issue with supporting the current version of Computer-Vision, a commonly used machine learning package.

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• Positive: It has allowed us to work with our overseas teams without any large hardware investing.
• Positive: Pre-trained models significantly reduce the time to develop solutions for our clients.
• Negative: Since it's a relatively new tool, you have to be careful about not paying for large errors while learning to use the tool.

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• We can get any new employee set-up on Python for Machine learning in minutes, without any assistance from IT. That's real $ savings.
• We started to experiment with Machine Learning a lot more, which leads to creating new projects which can have a tremendous impact on the business.

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