Amazon SageMaker

4.7
Overall rating
Reviews

4.7
Overall rating
(3)
Reviews

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 Software - SageMaker Studio
Amazon SageMaker Software - SageMaker Pipeline
Amazon SageMaker Software - SageMaker Debugger
Amazon SageMaker Software - SageMaker Data Wrangler
Amazon SageMaker Software - SageMaker Clarity
Amazon SageMaker video
Amazon SageMaker Software - SageMaker Studio - thumbnail
Amazon SageMaker Software - SageMaker Pipeline - thumbnail
Amazon SageMaker Software - SageMaker Debugger - thumbnail
Amazon SageMaker Software - SageMaker Data Wrangler - thumbnail
Amazon SageMaker Software - SageMaker Clarity - thumbnail

Amazon SageMaker pricing

Amazon SageMaker has a free version and does not offer a free trial.

Starting Price:
Not provided by vendor
Free Version:
Yes
Free trial:
No

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Amazon SageMaker Reviews

Feature rating

Value for Money
4.5
Functionality
4.7
Ease of Use
4
Customer Support
4
3 reviews of 3
Maha
  • Industry: Computer Software
  • Company size: 501–1,000 Employees
  • Used Weekly for 2+ years
  • Review Source
Value for Money
4
Functionality
4
Ease of Use
3
Customer Support
3

4
Reviewed on 19/08/2023

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.

Senamela
  • Industry: Information Technology & Services
  • Company size: 11–50 Employees
  • Used Weekly for 1-5 months
  • Review Source
Value for Money
5
Functionality
5
Ease of Use
5
Customer Support
5

5
Reviewed on 21/11/2022

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.

Eunice
  • Industry: Market Research
  • Company size: 11–50 Employees
  • Used Daily for 6-12 months
  • Review Source
Value for Money
0
Functionality
5
Ease of Use
4
Customer Support
0

5
Reviewed on 08/02/2023

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.

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