Amazon Tensor Flow vs. Appian

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.

Incentivized

Appian works great for automating manual processes and integrating multiple systems through its toolset. It gives great flexibility for establishing rules for approvals, routings, escalations, and the like. Because of the low code toolset, it's very easy to deploy and make changes as needed as processes evolve and as the organization learns to utilize the system better. Minimal maintenance is required to support the applications build on the platform. Some of the automated testing integration with tools like Jenkins is limited so that may be an issue for some.

Incentivized

• 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.

Incentivized

• Allows at a glance workflow documentation which assists in the need we have for information readiation.
• Drag and drop interface for workflow development greatly speeds our apps time to market.
• Using the advanced features of Appian, we are able to create working sites in a fraction of the time it would take to do so using "traditional" development.

Incentivized

• 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.

Incentivized

• Search issues when type ahead and database search are used in the same field.
• Buttons implementation where user is require[d] to click on the button description - if clicks on the button outside that text - button will not work.
• Problems with using certain off-the-shelf performance tools like WebLoad or Neoload. That is because of different dynamic variables being used internally in Appian - which these tools are unable to correlate. We are still investigating using other tools like Jmeter to overcome dynamic correlation problem for performance testing.

Incentivized

We recently renewed our license with Appian. We are convinced that its flexibility, relative ease of use, the support they provide, there mobile advancements and their general willingness and desire to see us succeed all contributed to our reason to renew our agreement with Appian

Appian is a low code environment, because of this, a very good visual interface is required. Appian is providing a feature-rich dashboard [that] we can use for building the dashboards and other interfaces. Appian also provides patches and releases to enhance these features. A developer can start off development just by going through a basic course from the Appian learning community.

Incentivized

Appian is one of the leading low code business automation platforms that support RPA, decision rules, case management, workflow automation, and machine learning all in a single bundle. But it is also harder to implement and replace the traditional business process.

Incentivized

As analyst I participated in a developer boot camp. At times it was hard to keep up but most of the time it made sense. Trainer took the time to explain and slowed pace down to answer questions etc.

Incentivized

Very boring; hard to get through quickly, but rather effective in demonstrating the use of the platform.

Incentivized

It was really seamless. SaaS in the true definition of the word. We logged on and started using the product. Very easy.

Incentivized

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.

Incentivized

Appian has enormously transformed and keeps on updating the product every quarter to meet the latest needs of the world with new innovations & technologies being integrated within the platform. What gives more pleasure than a product that keeps on continuous[ly] improv[ing]?

Incentivized

• Positive: It has allowed us to work with our overseas teams without any large hardware investing.
• Positive: Pre-trained models significantly reduce the time to develop solutions for our clients.
• Negative: Since it's a relatively new tool, you have to be careful about not paying for large errors while learning to use the tool.

Incentivized

• I believe it has negatively impacted our release dates. There may have been a misunderstanding as to the learning curve, even though it is "low code."
• The look and feel of the applications created using Appian have uniformity and it's easier to have "reuse" between applications.
• There is less developer control when it comes to features. I think this mainly has to do with the amount of plugins available. I would think there should be many more available plugins. But again, our use case is probably different than most others.

Incentivized