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Amazon SageMaker

Amazon SageMaker

Overview

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.

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Recent Reviews

AWS - The best!

9 out of 10
May 21, 2018
Incentivized
Amazon SageMaker is currently being used by our analytics and technology groups but managed by the associates at our firm. It addresses …
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Product Details

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.

Amazon SageMaker Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo
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Comparisons

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Reviews and Ratings

(49)

Reviews

(1-1 of 1)
Companies can't remove reviews or game the system. Here's why
February 21, 2022

Top things to know about Amazon Sagemaker

Verified User
Consultant in Marketing
Marketing & Advertising Company, 201-500 employees
Score 9 out of 10
Vetted Review
Verified User
Incentivized
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 and Cons
  • 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
  • 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
Likelihood to Recommend
Amazon Sagemaker suits well in areas of data science and Machine learnings where medium to high-volume data is to be used for analysis.
For a lean and platform agnostic deployment, it provides kubernetes integration to containerize the solution and deploy on any platform.
It is one of the best solution for technical users for training Machine Learning models.
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
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