Amazon Deep Learning AMIs vs. Azure NetApp Files vs. OpenText Magellan

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.

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In my opinion, I would say it is more suitable to huge workloads, where you really needs the reliability and performance that a Netapp storage provides to you, larger share sizes, etc. Use cases where you really needs to store large amounts of non-structured data. It is not a cheap solution, I mean, you can find other options to store your data on the Cloud at smaller prices. So, for small companies, or companies that depends mostly on web-applications, or don't have such a specific requirements, I would not go with Azure Netapp Files.

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If you do not have a large budget and are a large organization, I would steer clear of Actuate. If you are looking to do very complex washboarding, I would not use them. Your developers have to be very skilled to work with this. Plan to bring in consultants if necessary to help your process. Adhoc reporting is weak. If your pricing is user based and you expand, this could be very expensive.

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

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• We have found that it scales very well. In some cases we had a large existing storage infrastructure and were able to migrate it while other times we started from scratch with low storage demands and Azure NetApp Files fit the bill each time.
• We have been impressed with the replication capabailities.
• The read and write speed when interacting with files is a major asset.

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• The report outputs can vary across different types such as HTML, PDF, and Excel
• Their open source offering is very sufficient
• There are great boards and blogs for developers and engineers to expand and use their features.
• The people from the company that I've worked with are professional and courteous.

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• 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

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• It does not support file locking although its available as an add-on
• Design is far from native and has a learning curve
• We would like to have backup functionality built in so that we don't run into the issue where the replication process makes a copy of the corrupted data.

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• The documentation on all the available features, but most importantly on the scripting side, can be improved
• The standard look & feel of some basic options, like parameter selection or sorting and filtering, looks dated and can't be customized
• The server portal needs to provide better tools
• More integration is needed with other OpenText products

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I am no longer working for the company that was using Actuate but I believe they would continue to use it because the stitching costs would be to high. It would require a complete rewrite of the reports and the never version of Actuate (BIRT) even required an almost complete report rewrite

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It is quite intuitive to use. It is fit specifically for doing sentiment, emotion, and intention analysis as well as text classification and text summarization. I would have given 10 if it is fit for the purpose of doing image processing and analysis as well. There is a huge market to analyze video and image data.

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Always there on the front and backend for us and the client.

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.

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Azure NetApp Files is very well integrated with Microsoft Azure, we use the same request methods that everyone knows from Azure. NetApp and Microsoft has built a very efficient solution that allows you to transfer virtually any service to the Microsoft public cloud. Azure NetApp Files also protects our data very well.

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It is vastly superior to these in many ways, for complex reporting it is a much more sophisticated solution. Visualizations are very good. Javascript extensibility is very powerful, others don't support this or as well. Pentaho and MS are both OLAP oriented. Pentaho is moving more toward big data, which was not our primary focus. Others are stuck in the Crystal Reports Band metaphor.

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

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• The main hurdle in promoting this solution is the price. Its price definitely requires an improvement. It is more expensive than other options, so customers go for a cheaper option.
• Its security and ease of use are most valuable.
• It is stable and scalable.

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• Actuate can handle 50 to 60 sub reports inside a report very well.
• Dynamically creating the datasource, chart, graph, reports are the main advantages. We can do any level of drilling, and can create a performance matrix dashboard efficiently.