Azure Machine Learning vs. TensorFlow vs. Zoho Analytics

For [a] data scientist require[d] to build a machine learning model, so he/she didn't worry about infrastructure to maintain it.

All kind of feature[s] such as train, build, deploy and monitor the machine learning model available in a single suite.

If someone has [their] own environment for ML studio, so there [it would] not [be] useful for them.

TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).

Incentivized

Zoho Analytics is the best way to consume data created by Zoho products . It's robust and quick build formula libraries and auto generated reports. A data source can be integrated and be ready for consumption within minutes. This gives a well developed baseline for organizing to develop advance analytics. It's mobile dashboards are very intuitive and useful for leaders who are on the move.

• User friendliness: This is by far the most user friendly tool I've seen in analytics. You don't need to know how to code at all! Just create a few blocks, connect a few lines and you are capable of running a boosted decision tree with a very high R squared!
• Speed: Azure ML is a cloud based tool, so processing is not made with your computer, making the reliability and speed top notch!
• Cost: If you don't know how to code, this is by far the cheapest machine learning tool out there. I believe it costs less than $15/month. If you know how to code, then R is free.
• Connectivity: It is super easy to embed R or Python codes on Azure ML. So if you want to do more advanced stuff, or use a model that is not yet available on Azure ML, you can simply paste the code on R or Python there!
• Microsoft environment: Many many companies rely on the Microsoft suite. And Azure ML connects perfectly with Excel, CSV and Access files.

Incentivized

• A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
• Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
• Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.

Incentivized

• Zoho Analytics’ predictive analytics capabilities can help forecast future trends, allowing for proactive planning and risk management.
• Performance Monitoring: We can track key performance indicators (KPIs) across departments, such as sales, marketing, finance, and HR. This aids in identifying bottlenecks and areas for improvement.
• Decision making regarding resource allocation.

Incentivized

• It would be great to have text tips that could ease new users to the platform, especially if an error shows up
• Scenario-based documentation
• Pre-processing of modules that had been previously run. Sometimes they need to be re-run for no apparent reason

Incentivized

• RNNs are still a bit lacking, compared to Theano.
• Cannot handle sequence inputs
• Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.

• I'm guessing it's out there somewhere but I really could have used a 'quick start guide' or guided start.
• Once I figured it out, it makes sense how to make sure the right data is provided in order to make dashboards quite flexible--- but without examples, I found it quite a challenge
• The initial organization of Analytics is NOT intuitive. Once in context, the organizational features make sense, but (at least initially) it would have been most useful if the organization of Analytics reports in Zoho Analytics had saved me a lot of time.

Incentivized

I'd give this an 11 if I could! As our business moves forward we hope to use Zoho Analytics more then we do now. Creating better reports and dashboards for our management team to evaluate the health of our business and to provide more insightful reports for our customers. The possibilities are endless with this tool

Incentivized

Easy and fastest way to develop, test, deploy and monitor the machine learning model.

- Easy to load the data set

-Drag and drop the process of the Machine learning life cycle.

Support of multiple components and ease of development.

Incentivized

For an end user, Zoho Analytics is pretty easy to use and very easy to access the dashboard. Linking data from multiple sources is very convenient. Multiple people can work on preparing and publishing the dashboards simultaneously, which helps delegate tasks.

Incentivized

ZOHO is a very reliable company/product. We never had any issues with downtime or inaccessibility to our data. Any type of maintenance that they had to perform was clearly communicated and never an issue. We use a lot of external hooks and we've never had any issues with getting ZOHO to communicate with any of those hooks.

Incentivized

ZOHO has obviously invested a lot of time effort and money in to creating a reliable infrastructure with high availability. We've never had any issues with performance and all of our data crunching small to large has always been well within reason. We have come to appreciate the performance of ZOHO and will continue to use it for all of our data needs.

Incentivized

Support is nonexistent. It's very frustrating to try and find someone to actually talk to. The robot chatbots are just not well trained.

Incentivized

Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.

Incentivized

The support team is honestly not that great. At times, it seems as if members of our own team know more about the product than the support team. They must not have a lot of training or the turnaround is quick

Incentivized

Not sure

Use of cloud for better execution power is recommended.

Incentivized

If your external data sources are previously organized and correlated (e.g.: in your datawarehouse or database) your implementation will be easier. Of cource some not previously predicted correlation would be necessary to be done during the implementation, but if your organization let it to be all done into Zoho Analytics, it will take more time from your team.

Incentivized

It is easier to learn, it has a very cost effective license for use, it has native build and created for Azure cloud services, and that makes it perfect when compared against the alternatives. As a Microsoft tool, it has been built to contain many visual features and improved usability even for non-specialist users.

Incentivized

Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice

Incentivized

Zoho Analytics has the best UI and user friendly to create reports and dashboard along with features like Zia Assistance that guide in creating reports and dashboard and also help in the forecasting of the data based on the past records.

As far as I know, Zoho Analytics has been able to fulfill every need we've had for it. Our reports have gotten better and more detailed with pretty much every new issue of our magazine. It just keeps getting better, and we keep feeding it more data to digest and present to us.

Incentivized

• Productivity: Instead of coding and recoding, Azure ML helped my organization to get to meaningful results faster;
• Cost: Azure ML can save hundreds (or even thousands) of dollars for an organization, since the license costs around $15/month per seat.
• Focus on insights and not on statistics: Since running a model is so easy, analysts can focus more on recommendations and insights, rather than statistical details

Incentivized

• Learning is s bit difficult takes lot of time.
• Developing or implementing the whole neural network is time consuming with this, as you have to write everything.
• Once you have learned this, it make your job very easy of getting the good result.

Incentivized

• Analytics has been the foundation of my role. I manage 2000 accounts myself, so I need analytics to help me know whom to serve first.
• Analytics provides a lot of visualization options that I need to serve my customers.
• Analytics has excellent options for accountability with our training team to know where each account is in their customer journey.

Incentivized