Item: Amazon Web Services
Rating: 8
Author: Shivani Pandey

Use Cases and Deployment Scope

We are using Amazon Web Services to develop and deploy machine learning applications on the cloud. We have used AWS like Comprehend and SageMaker to build deep learning models. Also, we have used other services like Lambda for server-less functions.

Pros and Cons

Excellent training material and documentation available for each tool and service of Amazon Web Services.
Large user community and support available online.
Scalable as per the demand and requirement.
Management of services used is very easy from the centralized dashboard.

Some tools have very complex UI to use.
Costing for certain tools and services is bit on the higher side.

Return on Investment

It helped us drive digital transformation for our client.
Improve business process agility and decision making across different project functions.

Support Rating

The support from Amazon Web Services is excellent. We have not faced any major issues as of now. During the initial deployment, we faced issues but the support person helped us fix them in a timely manner.

Key Insights

Do you think Amazon Web Services delivers good value for the price?

Yes

Are you happy with Amazon Web Services's feature set?

Yes

Did Amazon Web Services live up to sales and marketing promises?

Yes

Did implementation of Amazon Web Services go as expected?

Yes

Would you buy Amazon Web Services again?

Yes

Usability

Amazon Web Services has provided us with many usable tools like AWS Forecast, DeepRacer, Deep Lens, and TensorFlow which helped us to do fast implementations. In short, it makes your deployments easy and fast.

Likelihood to Recommend

It is well suited for your project if you require a secure and reliable cloud platform. It provides on-demand scalable services and hardware which can fulfill the requirements of small as well as large application.

AI ML Apps with Amazon Web Services

Overall Satisfaction with Amazon Web Services