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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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Pricing

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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.

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

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What is Vertex AI?

Vertex AI on Google Cloud is an MLOps solution, used to build, deploy, and scale machine learning (ML) models with fully managed ML tools for any use case.

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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

(48)

Reviews

(1-5 of 5)
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Score 9 out of 10
Vetted Review
Verified User
Incentivized
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.
  • 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
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.
  • Studio Lab
  • Model Training
  • Pipelines
  • Kubernetes Integration
  • 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
Score 10 out of 10
Vetted Review
Verified User
Incentivized
We are using the SageMaker service from AWS for POC, and to build the final model on the large dataset of healthcare domain under the R&D department. SageMaker also provides hosting functionality, so that we can host a created model for the end-level application which is accessible through a simple API call from any application.
  • 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.
  • 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.
Well suited scenarios:
  • For quick POC of ML and DL.
  • To train a model on a large dataset using multiple servers.
  • To host a model to be used by multiple applications.
Less appropriate scenarios:
  • For data analysis tasks.
  • For a data scientist who has less of a programming background.
  • 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.
Amazon SageMaker comes with other supportive services like S3, SQS, and a vast variety of servers on EC2. It's very comfortable to manage the process and also support the end application by one click hosting option. Also, it charges on the base of what you use and how long you use it, so it becomes less costly compared to others.
Amazon Simple Queue Service (SQS), Amazon Redshift, Amazon S3 (Simple Storage Service), Amazon Elastic Compute Cloud (EC2)
Gavin Hackeling | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Incentivized
We use SageMaker in the engineering and data science departments to host Jupyter notebooks, periodically retrain models, and serve models in production. Data scientists work in Jupyter notebooks hosted on SageMaker notebook instances instead of their local machines. We often inject models into AWS-provided containers, and use SageMaker to provide a managed, auto-scaling HTTP interface.
  • 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.
  • 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.
SageMaker is great for serving Jupyter notebooks, particularly if you already use other AWS products, such as S3. SageMaker's model retraining function is useful if you write a few Lambda functions to invoke jobs. Its model serving function is useful if your team has limited resources and is willing to submit to SageMaker's opinions.
  • 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.
May 21, 2018

AWS - The best!

Score 9 out of 10
Vetted Review
Verified User
Incentivized
Amazon SageMaker is currently being used by our analytics and technology groups but managed by the associates at our firm. It addresses the business problems of reporting and having one ultimate software of data and analysis that can be used across locations and employees. It allows for one place to store the best algorithms for predicting data on cases and court trials.It also provides examples on actual data sets that can be used, algorithms and easy to run notebooks.
  • 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.
  • Searching and descriptions can be easier to read and interpret.
  • Training modules and customer service training representative could make on boarding employees easier.
SageMaker is well suited for an organization with a robust IT department that might not be as specifically well versed in model building and deployment features. It has built in algorithms and models as well as computations that lay the framework for what our department uses this tool for. It allows our team to get things done whether they are very experienced data scientists or a recent IT new hire who might not know all the ropes yet. They are both able to dive in and develop models and algorithms because the foundation is already there. It is also well suited for a company that uses amazon already as the integration is very easy. I would say the integration might be harder for an organization that does not use Amazon or a smaller organization that might not need as much heavy data or reporting.
  • 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.
Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as a whole. The training was simple as well.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
At my previous company, I worked for a top social media brand on their recruiting models and functions. In this role, they were utilizing Amazon SageMaker in its early stages. Because of this, I was tasked with training and onboarding these employees in the use of the tool and guide them through this process. It addressed the problems of building and managing the machine learning process but takes away a lot of the unmanageable parts of this.
  • 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.
  • 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.
It allows for one-click processes and for things to be auto checked before they are moved through the process but through the system. It also makes training easy. I am able to train users on the basic fundamentals of the tool and how it is used very easily as it is fully managed on its own which is incredible.
  • 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.
We have not invested in another machine learning software at this time and so far this has proved very successful with our machine learning teams. As mentioned, I am training these individuals simply on the fundamentals of the software and using it/customizing it for their needs. It has been very easy to do this and has gotten great reviews across the organization so far.
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