Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.
$0
per month
InterSystems IRIS
Score 7.7 out of 10
N/A
InterSystems IRIS is a complete cloud-first data platform that includes a multi-model transactional data management engine, an application development platform, and interoperability engine, and an open analytics platform. It is is the next generation of InterSystems' data management software. It includes…
Azure can be a more unified product. It feels like 10 different tech teams were building it but we're not talking to each other. An example is when the user needs to know what is the next step. Automatically saving a previous state is very helpful as new users are usually not aware of the functionality.
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[.]
Few models: Even though it has a lot of Machine Learning models, it is quite limited when compared to R. Most Data Scientists still use and prefer R, so the newest models tend to release as R libraries. With Azure ML, we need to wait for Microsoft to evaluate and decide if including a new model is a good idea or not
Tableau interface: last time I checked there was no easy way to connect with Tableau.
Cloud based: You always need a good internet connection to use it.
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
Good UX/UI and overall good usability, but it takes a while to get used to the product & platform. The whole design seems fragmented with little in terms of integration with project management tools such as JIRA, or wireframing. Overall it feels like an unfinished product that's meant for teaching more than for production.
I'm satisfied with the Azure Machine Learning Studio- it fulfilled my goal in a single channel. Even haven't worr[ied] about the maintenance or any fault tolerance. This provide[s] the user interactive UI to grab the features easily. [Their] support teams also very help[ful], they stand with us at any time.
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!
The answer is quite simple: Microsoft Azure Machine Learning Workbench is the cheapest and most user friendly analytics tool I have ever seen! Unless you are running a team of data scientists, this is the tool to go. Most functions (marketing, sales, finance, supply chain, logistics, HR, R&D, etc.) could easily integrate Azure ML in its day to day activity.
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
