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 IBM Watson Machine Learning is an AI-based scalable self-learning model for any type of business. It can be used to help any company automate repetitive tasks, predict future trends, and make data-driven decisions. I used it to predict stock prices based on certain variables. It works well, cost me nothing, and gives me the ability to create my own AI-based models that I can use for any purpose.
Bed Raj CITO (Chief Information Technology Officer)
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 Good machine learning tool Easy integration 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 Proper usage of REST API documentation is missing. Not localization friendly, cannot support regional or local language documents. Bed Raj CITO (Chief Information Technology Officer)
Read full review Support Rating IBM had a hard time providing business level support. There were a lot of data scientists and technology experts but rarely a simple business person shows up. Also the way IBM operates IBM Consulting has competing priorities as compared to IBM Technology. This has resulted in a lot of confusion at the client's end.
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 We have been using Microsoft Azure as a machine learning tool. But the challenges remain the same. These are all tools that you need a robust analysis before a decision on the tool. Unfortunately, the technology company cannot make that determination due to lack of core business understanding. Without that the project is doomed.
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 Create secure business environment. Save upto 90% of manual labor. Improve my sales and marketing ROI. Bed Raj CITO (Chief Information Technology Officer)
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