Amazon Tensor Flow vs Kira

• 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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• UI/UX - tagging and naming feels much easier than you'd expect machine learning to feel
• Accuracy - Kira's built-in models perform well out of the box
• Assistance - Kira's support team gets back to me same day if I have a question

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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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• Inability to relabel smart fields to suit the review process means it is hard to align it to particular projects (e.g. it would be useful to relabel the "Assignment" smart field as "Is the contract assignable?")
• Not enough non-English smart fields.
• Needs the ability to resell user-trained smart fields in a marketplace.
• Output is not customizable enough.
• Built-in analysis tools are useful but a little basic.

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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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• Positive ROI: Increased comfort level of attorneys and use of tech
• Neutral ROI: It has not significantly change how we handle projects, since there still is a need for manual review
• Negative ROI: It has been cost prohibitive to scale it

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