Amazon Tensor FLow
Rating: 9 out of 10
IncentivizedUse Cases and Deployment Scope
We use Amazon Tensor Flow mainly for classification, regression, and clustering when using large databases and for overseas clients. The cloud capabilities allow us to smoothly provide a full service for our clients overseas
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.
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.
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.
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.
Alternatives
Microsoft Azure Machine Learning Workbench, Google Cloud AI and Watson Studio (formerly IBM Data Science Experience)
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.
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.
MQ
Mauricio Quiroga-Pascal Ortega
CEO in Information Technology at DisperSurance (Computer Software, 51-200 employees)
Vetted Review
1 year of experience