Top things to know about Amazon Sagemaker
Amazon Sagemaker has multiple applications and use cases in our organization. It is used to create machine learning models for our call …
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.
Recent Reviews
Product Details
- About
- Tech 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 Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
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Reviews and Ratings
(49)Reviews
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October 09, 2020
SageMaker : Good option for 'fail early learn fast' in ML and DL
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.
- 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.
- Microsoft Azure, Google Cloud AI, IBM Watson Analytics, IBM Watson Studio (formerly IBM Data Science Experience) and Salesforce Analytics Cloud
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.
August 29, 2018
Incomplete but Promising
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.
- 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!
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.
- 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.