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Amazon SageMaker

Score8.8 out of 10

59 Reviews and Ratings

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What is Amazon SageMaker?

Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Categories & Use Cases

Reviews

What users are saying about Amazon SageMaker.
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Top things to know about Amazon Sagemaker

Incentivized
Verified User
Consultant in Marketing (201-500 employees employees)

Use Cases and Deployment Scope

Amazon Sagemaker has multiple applications and use cases in our organization. It is used to create machine learning models for our call center team to analyse frequently raised customer problems, widely accepted solutions. These models help in reducing operating cost by automating and optimizing processes with minimal manual intervention. The other usecase include product development which required decision making based on image processing.

Pros

  • Machine Learning at scale by deploying huge amount of training data
  • Accelerated data processing for faster outputs and learnings
  • Kubernetes integration for containerized deployments
  • Creating API endpoints for use by technical users

Cons

  • The UI can be eased up a bit for use by business analysts and non technical users
  • For huge amount of data pull from legacy solutions, the platform lags a bit
  • Considering ML is an emerging topic and would be used by most of the organizations in future, the pipeline integrations can be optimized

Most Important Features

  • Studio Lab
  • Model Training
  • Pipelines
  • Kubernetes Integration

Return on Investment

  • Machine Learning models help in reducing operating cost for manual intensive processes by deploying chatbots
  • Improvement in product roadmap for learning about customer feedback on an early stage
  • Supporting analytics and data science team to share correct insights and models with business teams

Other Software Used

Adobe Target, Progress Sitefinity, Braze

Incomplete but Promising

Incentivized
Gavin HackelingView profile
Data Scientist in Engineering at Wonder (AskWonder.com) (11-50 employees employees)

Pros

  • SageMaker is useful as a managed Jupyter notebook server. Using the notebook instances' IAM roles to grant access to private S3 buckets and other AWS resources is great. Using SageMaker's lifecycle scripts and AWS Secrets Manager to inject connection strings and other secrets is great.
  • SageMaker is good at serving models. The interface it provides is often clunky, but a managed, auto-scaling model server is powerful.
  • SageMaker is opinionated about versioning machine learning models and useful if you agree with its opinions.

Cons

  • SageMaker does not allow you to schedule training jobs.
  • SageMaker does not provide a mechanism for easily tracking metrics logged during training.
  • We often fit feature extraction and model pipelines. We can inject the model artifacts into AWS-provided containers, but we cannot inject the feature extractors. We could provide our own container to SageMaker instead, but this is tantamount to serving the model ourselves.

Return on Investment

  • We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
  • We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
  • For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

Other Software Used

AWS Lambda, Amazon API Gateway, Stitch, Amazon Redshift

SageMaker : Good option for 'fail early learn fast' in ML and DL

Incentivized
Verified User
Employee in Research & Development (501-1000 employees employees)

Pros

  • Provided an instance of Jupyter notebook for development script, which made it very easy to manage and develop any script.
  • Our system is cloud-based, and we are charged only for what we use and how long we use it.
  • We can choose multiple servers for Training, without any headache of distribution.
  • Most of the libraries are supported.
  • All training, testing, and models are stored on S3, so it's very easy to access whenever we require it.

Cons

  • It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
  • Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.

Return on Investment

  • Using SageMaker, we can truly implement 'fail early, learn fast,' using an on-demand server for training.
  • It also saves your money from investing in a physical server for very rare use.
  • However, the pricing is high, but it will cost you only for what you use.

Alternatives Considered

Microsoft Azure, Google Cloud AI, IBM Watson Analytics, IBM Watson Studio (formerly IBM Data Science Experience) and Salesforce Analytics Cloud

Other Software Used

Amazon Simple Queue Service (SQS), Amazon Redshift, Amazon S3 (Simple Storage Service), Amazon Elastic Compute Cloud (EC2)

Amazon SageMaker Review

Incentivized
Verified User
Employee in Human Resources (1001-5000 employees employees)

Pros

  • Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models.
  • Customization and easy to alter and change.
  • If you already are an Amazon user, you do not need to transition over to another software.

Cons

  • I think that although the algorithms are there and you are using one click, there could be more detailed descriptions located in places so that other users are able to easily find the right formula and tools.
  • Mobile friendly options would be a huge plus, even tracking what employees are using this tool for in regards to reporting.

Return on Investment

  • Use of this tool across areas of our organization has allowed for more time to be spent on other things as this is taking a lot of the heavy lifting and developing out of the machine learning process all together.
  • Database of algorithms ready to use and adjust, customizing is key with this group of employees and their daily work while using this software.

AWS - The best!

Incentivized
Verified User
Professional in Legal (51-200 employees employees)

Pros

  • Provides the basis for developing algorithms and data without going very deep into the actual development.
  • Amazon software and so can be used with other Amazon software your organization already uses.
  • Training and on boarding of the software and customer service was great to work with.

Cons

  • Searching and descriptions can be easier to read and interpret.
  • Training modules and customer service training representative could make on boarding employees easier.

Return on Investment

  • Easily integrates with our existing workflow
  • Quick to get us results and reporting back.
  • Database of algorithms and models already there for you to use.