TrustRadius
Incomplete but PromisingWe 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.,7,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.,AWS Lambda, Amazon API Gateway, Stitch, Amazon RedshiftAWS - The best!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.,9,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 ReviewAt 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.,9,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.,
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Amazon SageMaker
6 Ratings
Score 7.4 out of 101
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Amazon SageMaker Reviews

Amazon SageMaker
6 Ratings
Score 7.4 out of 101
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Gavin Hackeling profile photo
August 29, 2018

Amazon SageMaker Review: "Incomplete but Promising"

Score 7 out of 10
Vetted Review
Verified User
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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.
Read Gavin Hackeling's full review
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May 21, 2018

Amazon SageMaker Review: "AWS - The best!"

Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
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March 29, 2018

"Amazon SageMaker Review"

Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
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Amazon SageMaker Scorecard Summary

About 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:  Machine Learning

Amazon SageMaker Technical Details

Operating Systems: Unspecified
Mobile Application:No