AI ML Apps with Amazon Web Services
Updated July 30, 2021

AI ML Apps with Amazon Web Services

Shivani Pandey | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with Amazon Web Services

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

  • 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.

Cons

  • Some tools have very complex UI to use.
  • Costing for certain tools and services is bit on the higher side.
  • It helped us drive digital transformation for our client.
  • Improve business process agility and decision making across different project functions.
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.

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

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.
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.

Amazon Web Services Feature Ratings

Infrastructure-as-a-Service (IaaS)
8.4
Service-level Agreement (SLA) uptime
9
Dynamic scaling
10
Elastic load balancing
9
Pre-configured templates
7
Monitoring tools
8
Pre-defined machine images
8
Operating system support
8
Security controls
9
Automation
8

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