Amazon SageMaker Reviews & Product Details

Has GPU options instances to choose from Review collected by and hosted on G2.com.

Takes a long time to spin up plus setting up a virtual environment within sagemaker is almost impossible Review collected by and hosted on G2.com.

It offers a lot of pretrained deep learning and ML models that reduces the time of small projects drastically. It's easy to work with for the first time and doesn't require prior knowledge. It's the best for deploying models with simplicity. Review collected by and hosted on G2.com.

It's quite expensive indeed and especially for large projects that will need more running time. It doesn't leave much room for customization as it has a set workflow with not much flexibility. Review collected by and hosted on G2.com.

Sagemaker was very easy to use and the interface was user friendly Review collected by and hosted on G2.com.

There was no access for the user to check her daily stats. The access was restricted. Review collected by and hosted on G2.com.

Amazon Sagemaker is a managed service for data science, We like SageMaker because it supports a lot of in-built algorithms, we especially use liner learner algorithms for solving predictive maintenance use cases Review collected by and hosted on G2.com.

We noticed, as a startup, when we are beginners. If we failed to stop unused sage make endpoint.That will end up in huge bills. Maybe looking for a better solution to fix this issue. Review collected by and hosted on G2.com.

We can quickly and easily build, train, and deploy machine learning models at any scale. Review collected by and hosted on G2.com.

All features are good is Amazon Sagemaker machine learning Review collected by and hosted on G2.com.

AWS Sagemaker is one of the best services that can help in enabling data scientists and facilitates developers to quickly and easily build, train, and deploy machine learning models at any scale at any time. Review collected by and hosted on G2.com.

The service is not easy to learn. You gotta get yourself trained and certified first. Review collected by and hosted on G2.com.

1. Auto-scaling of Infra

2. Out of the box versioning of model and training

3. Great integration with AWS eco-system

4. Easy monitoring and debugging

5. Custom algorithm support with out of the box algorithm

6. Multi Model Server support

7. Great support for small team where we don't have infra expert and only need to focus on Machine Learning

8. Great support on AWS CLI Review collected by and hosted on G2.com.

1. Very bad documentation

2. Only AWS components are used like S3 for data, ECR for Docker etc

3. Very high cost for endpoints even they are not in used

5. Not good for team where we have infra experts, giving less control for underlying infra Review collected by and hosted on G2.com.

Hyperparameter optimization, integration with EC2, Forecasting and Personalized services and AutoPilot Review collected by and hosted on G2.com.

Deployment phase of a data science model Review collected by and hosted on G2.com.

1) The flexibility to deploy your own algorithms using Docker which is missing in other big cloud ML platforms such as GCP AI. (This is very useful for its integration to other existing ML tools such as MLflow) 2) Support for A/B testing of ML Models 3) Support for batch processing 4) Support for lambda scheduling 5) A very well managed GitHub sagemaker repository supported by blogs to provide all necessary examples to get started. 6) Support for Reinforcement learning. 7) Support for third party software through AWS market place. Review collected by and hosted on G2.com.

1) The very unclear documentation/support for Models monitoring after deployment over Sagemaker endpoints. 2) Too many APIs to do the same thing. Which might confuse the user in finding which one is the best way. 3) UI can be better. 4) Project Management and adding collaborators feature is missing or not directly supported. 6) The logs tracking through Cloudwatch can be better managed. 7) It should provide an out-of-the-box workflow management solution such as Airflow. Review collected by and hosted on G2.com.

Sagemaker lets you build, train and deploy your models all in one place. I like how it lets you choose different machine types for each phase of development so no resources are wasted. Sagemaker also supports distributed training. I've used Sagemaker to build, train and deploy deep learning models using PyTorch, Tensorflow as well as Keras. It provides lots of custom conda environments to support development using any framework, like p36, p32 etc. Sagemaker instances comes preloaded with some sample notebooks. There's also a large repository of materials available to help you get started. Review collected by and hosted on G2.com.

It's bit pricey and the UI doesn't exactly tell you if you've go unused instances or deployed models lying around unlike the GCP equivalent. Amazon sneaks up on you with unexpected bills if you aren't careful. It's a bit difficult to get started with as the whole user creation vs instance creation kinda trips you up. As soon as you click on preview, amazon directs you to the user creation part and you've a whole lot of unnecessary set up when all you probably need is to create a single notebook instance. Review collected by and hosted on G2.com.