Likelihood to Recommend 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.
Read full review Overall, IBM Watson Discovery is an amazing technology that we use with our clients to address various business problems, but the biggest challenge has always been about ingesting, analyzing, enriching, and searching huge collections of documents and allowing our end users and SMEs to be able to search for what they need to reduce the time and efforts spent daily on a manual search through various collections of documents. We have successfully managed to reduce manual work by over 80%, and now our SMEs are being used for the skills they have to gather insights rather than do manual work.
Read full review Pros 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. Read full review It is an excellently fast platform with documents and the answers to queries. With automation learning beneficial as it saves time. When searching for a document, everything stays located and easy to find. Acceptance of various documents. It has a quite comfortable Technical support, always available when required. Read full review Cons 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. Read full review I believe AI should be more flexible about providing data. However, it's understandable that you need to provide the details you need in a more specific and detailed way. The interface could use more tweaking. Being new to the program, it was kind of hard to navigate. Luckily, there was a customized feature of the dashboard that I could set up, and having something that you know where you are placed always feels familiar and comfortable. Read full review Usability Powerful insights with a little bit of a learning curve
Read full review Support Rating Similar to all IBM Watson and Salesforce product solutions, the overall support would be a 10/10. Their provided FAQ's help with frequently experienced issues and if still unable to figure something out, their customer service representatives are always super responsive. With instant chat functions available, it is easy to ask a quick question rather than sitting on hold.
Read full review Alternatives Considered 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.
Read full review Discovery differs from its competitors due to the better ease of implementation and the high level of natural language recognition, it is equal in integration resources such as API and workflow or process pipeline, but it loses in the price for a high volume of documents and/or research. If you own or plan to use other services from the IBM Watson family, there is no doubt that Watson discovery is your best option. Another important point is if you plan to use a cloud or on-premise service (local server or private cloud).
Read full review Return on Investment 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. Read full review We find its Enterprise plan expensive for a country of LATAM. For US or Europe based businesses, looks great. A Big Data and massive queries based company would find the service expensive. Maybe a flat price plan would be helpful. Have you thought in making a cheaper plan where you take the learning from your customer's data to enrich your AI tool? Read full review ScreenShots