Amazon TensorFlow enables developers to quickly and easily get started with deep learning in the cloud.
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botkeeper
Score 9.6 out of 10
Small Businesses (1-50 employees)
Boston based Botkeeper is the world's first and original robotic bookkeeper. The Botkeeper solution uses a combination of skilled accountants, machine learning, and AI to provide the best bookkeeping at the lowest possible cost. Instead of replacing your existing accounting software, we'll hook right up to it! Botkeeper can easily and quickly integrate with Quickbooks Online or Xero. Getting up and running is simple- 1. Data is extracted from both financial and non-financial sources.…
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OpenText Magellan
Score 9.0 out of 10
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OpenText Magellan Analytics Suite leverages a comprehensive set of data analytics software to identify patterns, relationships and trends through data visualizations and interactive dashboards.
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Pricing
Amazon Tensor Flow
botkeeper
OpenText Magellan
Editions & Modules
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No answers on this topic
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Offerings
Pricing Offerings
Amazon Tensor Flow
botkeeper
OpenText Magellan
Free Trial
No
Yes
No
Free/Freemium Version
No
Yes
No
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
No setup fee
No setup fee
No setup fee
Additional Details
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Our goal is to offer bookkeeping solutions that are not only best in class, but available to all companies at all stages of growth. We have our Free Package, all the way up through Custom Packages, and everything in between!
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Community Pulse
Amazon Tensor Flow
botkeeper
OpenText Magellan
Features
Amazon Tensor Flow
botkeeper
OpenText Magellan
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Amazon Tensor Flow
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Ratings
botkeeper
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Ratings
OpenText Magellan
7.0
2 Ratings
16% below category average
Customizable dashboards
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7.02 Ratings
Report Formatting Templates
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7.01 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Amazon Tensor Flow
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Ratings
botkeeper
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Ratings
OpenText Magellan
8.3
3 Ratings
3% above category average
Drill-down analysis
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8.03 Ratings
Formatting capabilities
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8.03 Ratings
Integration with R or other statistical packages
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9.01 Ratings
Report sharing and collaboration
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00 Ratings
8.02 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Amazon Tensor Flow
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Ratings
botkeeper
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Ratings
OpenText Magellan
8.3
2 Ratings
1% above category average
Publish to Web
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00 Ratings
8.02 Ratings
Publish to PDF
00 Ratings
00 Ratings
8.02 Ratings
Report Versioning
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9.02 Ratings
Report Delivery Scheduling
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8.02 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
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.
Well, Suited Scenarios are: Automating the data entry, analysis, bookkeeping, managing finances fairly on this basis, and pulling out burndown charts, reports, and other analytical chapters. hence it helps in understanding the business, cash flows, or issues related to that which surely end up with the ideas of revenue growth directly and indirectly as well. And the most important thing in all this is done by AI-powered tools so management of resources overhead is no more. Scenarios where it is less appropriate: When it comes to a strong comprehensive accounting then the bookkeeper fails there and we need to integrate with third-party service providers to achieve the goal. So this is the worst scenario. And for detailed resources, one needs a vast knowledge base, and that's not the case with the Botkeeper as its knowledge base appears to be limited to basic.
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.
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.
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.
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
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.
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.
Botkeeper is quite new to accounting solutions. The tech base is quite new and cutting edge. Service support is quite fast and good. Integration support is open to another third party to enhance in all possible ways. Eliminates overhead of hiring permanent resident accountants. Great user experience and well-optimized tools.
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.
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.