Amazon SageMaker

RATING:

4.7

(3)
Overview

About Amazon SageMaker

Amazon SageMaker is an ML service that can build and deploy sophisticated machine learning models. Amazon SageMaker speeds up machine learning innovation by providing customers with sophisticated ML capabilities that can be accessed using integrated development frameworks and no-code interfaces. Amazon SageMaker allows data scientists and developers to train sophisticated machine learning models in minutes.

Amazon SageMaker Pricing

Contact AWS for information on pricing. AWS provides a usage-based pricing model for all their SageMaker products. The price scales to the type of instance used.

Free trial: 

Not Available

Free version: 

Available

SageMaker Studio
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Amazon SageMaker Reviews

Overall Rating

4.7

Ratings Breakdown

Secondary Ratings

Ease-of-use

4

Customer Support

4

Value for money

4.5

Functionality

4.5

Most Helpful Reviews for Amazon SageMaker

3 Reviews

Senamela

Information Technology and Services, 11-50 employees

Used weekly for less than 6 months

Review Source: Capterra
This reviewer was invited by us to submit an honest review and offered a nominal incentive as a thank you.

OVERALL RATING:

5

EASE OF USE

5

VALUE FOR MONEY

5

CUSTOMER SUPPORT

5

FUNCTIONALITY

5

Reviewed November 2022

Great Gateway to Machine Learning

Sagemaker has made my ML Journey less painful and more enjoyable.

PROS

I've had fun using Sagemaker to train models, I also enjoyed the functionality that allows us to provision resources to better suit the ML Training needs.

CONS

Make it easier to delete domains, since leaving resources on the account incurs charges quickly.

Reason for choosing Amazon SageMaker

Full Suite of Products and a wide array of options.

Eunice

Market Research, 11-50 employees

Used daily for less than 12 months

Review Source: Capterra
This review was submitted organically. No incentive was offered

OVERALL RATING:

5

EASE OF USE

4

FUNCTIONALITY

5

Reviewed February 2023

Excellent logiciel pour le Machine Learning

J'ai commencé à utiliser Amazon SageMaker au travail car j'avais des soucis d'environnement virtuel et d'incompatibilité sur mon Mac (notamment avec des librairies python comme TensorFlow ou Spacy). SageMaker a réglé tous mes soucis, il est très facile de passer d'un environnement à l'autre (déjà créé par SageMaker d'ailleurs). Je gagne également beaucoup de temps avec les instances plus puissantes.

PROS

J'aime beaucoup passer d'une instance à l'autre, par exemple lorsque je dois faire tourner un modèle de Machine Learning assez lourd avec beaucoup de données, je choisie une instance plus puissante et c'est donc beaucoup plus rapide, cela me fait gagner un temps fou!

CONS

Ce que je n'aime pas c'est que les instances s'allument parfois toute seule (et sont parfois payantes), je dois toujours vérifier que seulement celles dont j'ai besoin sont allumées.

Maha

Computer Software, 501-1,000 employees

Used weekly for more than 2 years

Review Source: Capterra
This review was submitted organically. No incentive was offered

OVERALL RATING:

4

EASE OF USE

3

VALUE FOR MONEY

4

CUSTOMER SUPPORT

3

FUNCTIONALITY

4

Reviewed August 2023

Amazon SageMaker: Simplified Machine Learning Models in the Cloud

Great experience in training and deploying ML models for IoT and home automation.

PROS

I enjoyed the simple machine learning model training using SageMaker. I used it for creating recommender systems for IoT home automation networks. It is very simple to load data sets from with in the Amazon EC2 cloud using the Amazon S3 datastore. The amazing capability in SageMaker is the automatic feature selection and creation for the ML data model using the SageMaker Data Wrangler. This proved to be very accurate and reduced a lot of the noise in my data sets with minimal configuration on my side. Features are even managed in a separate container to be managed later by allowing addition/removal/updating feature entries. Real-time predictions is another great property in Amazon SageMaker in addition to checking the conformity of the data in the ML model across subsets of the data set to ensure balanced data points/features. Sagemaker can even map the role each feature plays in the prediction to give the developer better sense on how the prediction output is biased/affected by each individual feature.

CONS

Definitely it would be great to have an offline local reference implementation to test against. However, this is a cloud system after all and accordingly, developers lose the local testing advantages.