Amazon Deep Learning AMIs vs. Litera Kira

Amazon AMIs has been very useful for the quick setup and implementation of deep learning for data analysis which is something I have used the service for in my own research. We commonly use the service to enable students to run intensive deep learning algorithms for their assessments. This service works well in this scenario as it allows students to quickly set up a suitable environment and get started with little hassle. If you are looking to run simple, surface level deep learning algorithms (kind of contradictory statement I know) then AMI is more complicated than most will need. When it comes to teaching the basics of Machine Learning, this kind of system is unnecessary and there are other alternatives which can be used. That being said this service is a must if you are looking to run complex deep learning via the cloud.

Incentivized

Kira is a great due diligence tool and can be well utilised on both large and small transactions. It also has good application if you are looking to compare multiple documents against a model form document or market standard templates. Kira is less useful if you are looking to review emails (e.g. as part of a disclosure exercise); or if your review involves non-Latin based script languages.

• Setting up environment
• Support for different types of machines
• Perfect for Machine Learning / Deep Learning use cases
• Nvidia / Cuda / Conda support easily

Incentivized

• 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

Incentivized

• Some aspects of the User Interface are quite confusing and activating packages can be a bit convoluted
• It can be a bit confusing to switch between frameworks for novice users

Incentivized

• 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.

Incentivized

If our firm had more contracts in English, the usability of Kira would be rated higher. However, since we have to train clauses in Portuguese in order to use Kira, it makes its usability lower. We still are not able to fully use Kira for reading contracts in Portuguese. It takes a long time and many associate hours to make Kira usable in other languages.

Incentivized

Customer Support is excellent. The online help portal is probably the best I have ever seen. Great videos with content easily found. The HelpLine is staffed by knowledgeable people. The videos have saved us providing a lot of in-house training, which we would struggle to resource. The account managers really know the product and their law firm clients and share best practices and trends.

Incentivized

Both of these services provide similar functionality and from my experience both are top class services which cover most of your needs. I think ultimately it comes down to what you need each service for. For example Amazon DL AMIs allows for clustering by default meaning I am able to run several clustering algorithms without a problem whereas IBM Watson Studio doesn't provide this functionality. They both provide a wide range of default packages such as Amazon providing caffe-2 and IBM providing sci-kitlearn. My main point is that both are very good services which have very similar functionality, you just need to think about the costs, suitability of features and integration with other services you are using.

Incentivized

Kira offers a lot more out of the box than other providers and is also more flexible around integrations. This, plus the clear pricing structure, is why we went for it instead of (or as well as) others. Diligen, RAVN, Leverton, Della, Seal not in list.

Incentivized

• Saves a lot of Infra Costs
• Saves a lot of time in handling environment issues
• Easy to start a new instance

Incentivized

• 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

Incentivized