Amazon Tensor Flow vs. InterSystems IRIS

A well-suited scenario for using AWS Tensor Flow is when having a project with a geographically dispersed team, a client overseas and large data to use for training. AWS Tensor Flow is less appropriate when working for clients in regions where it hasn't been allowed yet for use. Since smaller clients are in regions where AWS Tensor Flow hasn't been allowed for use, and those clients traditionally don't have enough hardware, this situation deters a wider use of the tool.

Incentivized

It is best suited in the scenario where a single interface is required for providing [a complete end-to-end] solution to the customers. You don't need [a] separate platform to write code or [perform] database operations. All you need is InterSystems IRIS software and you are done. You can also use analytics functionality which is one of the greatest [features] which many customers need for their solution[.]

• 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.

Incentivized

• Connecting with mutliple vendor platforms and systems allowing us to consume and provide data as needed.
• Allowed us to implement our own RESTful api engine to server data to our internal applications.
• Setting up their recommendations for high availability allow us to perform server maintenance with minimal down time to our users.

• 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.

Incentivized

• Enhanced documentation, more comprehensive and user-friendly documentation, including detailed tutorials and examples
• Improving compatibility and integrations with others programming languages
• Introducing tools and techniques to optimize the performance of ObjectScript applications, such as profiling tools, performance monitoring utilities, and code optimization guidelines
• A better compatibility with Python libraries

The interface is very intuitive, the documentation is very good so it is not complicated to operate.

The security is complex, but you can create a special role to access and the user ONLY can operate with the part that it allows.

Also, you can examine the data very quick with the SQL Browser integrated.

The InterSystems WRC has always been helpful and responsive. The folks I have spoken with are always understanding of our needs and questions and regardless of if the question is simple or complex we are always met with the same professionalism and helpfulness every time. I have no hesitations contacting InterSystems for help!

Will done based on a proper planning, so this will makes execution more easier and better.

Microsoft Azure is better than Amazon Tensor Flow because it provides easier and pre-built capabilities such as Anomaly Detection, Recommendation, and Ranking. AWS is better than IBM Watson ML Studio because it has direct and prebuilt clustering capabilities AWS, like IBM Watson ML Studio, has powerful built-in algorithms, providing a stronger platform when comparing it with MS Azure ML Services and Google ML Engine.

Incentivized

Tibco was not originally planned to be used for HL7 Integrations and as such we had to create some very complicated processes in order for the messages to parse and validate appropriately. It was simply not built for this type of interoperability. Comparatively, InterSystems IRIS for Health (HealthConnect) has out of the box HL7 features that would parse messages, offer a variety of validation options, simplified data lookups and transformation and reduced the amount of time it took to develop connections with out vendor systems. InterSystems IRIS also allows one to push just single files into production at a time so there is less of a chance of us pushing something that should not be in production yet as our previous system was set up to with TIBCO deployments

Incentivized

• 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.

Incentivized

• Very Fast and scalable software to deal with big data flow, safe and robust.
• The studio IDE is quite old fashion and lacks a few facilities
• amazing for complex data consumption, its milyi-model capability wich allows multiple different models for a single set of codes.