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
Oracle Database
Score 7.9 out of 10
N/A
Oracle Database, currently in edition 23ai, is a converged, multimodel database management system. It is designed to simplify development for AI, microservices, graph, document, spatial, and relational applications.
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.
I believe Oracle Database is still the best RDBMS database which is the database to consider for OLTP applications and for Adhoc requests. They are good in Datawarehousing in certain aspects but not the best. Oracle is also a great database for scaling up with their Clusterware solution which also makes the database highly available with services moving to the live instance without much trouble.
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.
New (actually it is more than five years old) multi-tenant architecture is not as straightforward as SQL Server, but it has been enhanced in Oracle 12c Release 2 and later 18c and 19c.
Many features require additional licensing (either as options or as packs) that increase the total cost
It is very likely to use this 12c (or next version) of Oracle Database. Nothing close to it in the marketplace in terms of performance, reliability and overall database management efficiency. If Oracle did one thing really good - it is it's OLTP Database I must say.
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.
Many of the powerful options can be auto-configured but there are still many things to take into account at the moment of installing and configuring an Oracle Database, compared with SQL Server or other databases. At the same time, that extra complexity allows for detailed configuration and guarantees performance, scalability, availability and security.
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.
1. I have very good experience with Oracle Database support team. Oracle support team has pool of talented Oracle Analyst resources in different regions. To name a few regions - EMEA, Asia, USA(EST, MST, PST), Australia. Their support staffs are very supportive, well trained, and customer focused. Whenever I open Oracle Sev1 SR(service request), I always get prompt update on my case timely. 2. Oracle has zoom call and chat session option linked to Oracle SR. Whenever you are in Oracle portal - you can chat with the Oracle Analyst who is working on your case. You can request for Oracle zoom call thru which you can share the your problem server screen in no time. This is very nice as it saves lot of time and energy in case you have to follow up with oracle support for your case. 3.Oracle has excellent knowledge base in which all the customer databases critical problems and their solutions are well documented. It is very easy to follow without consulting to support team at first.
Overall the implementation went very well and after that everything came out as expected - in terms of performance and scalability. People should always install and upgrade a stable version for production with the latest patch set updates, test properly as much as possible, and should have a backup plan if anything unexpected happens
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.
Oracle Database is among the easiest to integrate with, program against, have a reliable cluster with DR, and has the most understood and well-documented databases. It suits really well if the software shop is primarily Java-based, and deals with large volumes of data with a high degree of diversity among the applications by purpose and use. Paid support is recommended as well as planned periodic patching and upgrades.
We wasted lots of money (Oracle is crazy expensive), time and effort on the project and were highly relieved when we found a different approach to supporting our aging ERP app that did not include Oracle.
Because of the difficulty of using Oracle, we spent a lot of money on consultants to help us over the conversion hump. Also wasted. And it was interesting to see them struggle with the software. Upgrades never went well always requiring multiple site visits, for example.
