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 …
Amazon SageMaker
Amazon SageMaker
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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.
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What is Azure Machine Learning?
Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.
What is AWS Lambda?
AWS Lambda is a serverless computing platform that lets users run code without provisioning or managing servers. With Lambda, users can run code for virtually any type of app or backend service—all with zero administration. It takes of requirements to run and scale code with high availability.
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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 Types | SaaS |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
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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.
February 04, 2019
Amazon SageMaker is quite good at machine learning, as long as you're in the Amazon Ecosystem.
Amazon SageMaker is used by a specific department that supports machine learning models development and deployment. From my perspective, the software makes a valiant effort at making data mining and machine learning more user-friendly, something that is not always an easy job. SageMaker addresses clients who wish to use machine learning for market predictions, looking for data mining details, and predictive analytics. It's great for what it attempts to do well.
- Amazon SageMaker is great for visually seeing the development of machine learning models. The process is set up in a logical step-by-step process.
- Amazon SageMaker makes training data models about as easy as it gets. It's straight-forward to construct training and test samples.
- Amazon SageMaker makes deploying machine learning models much easier than other open-source tools.
- Amazon SageMaker is a great tool for a data scientist, although surprisingly, comparing different machine learning models with SageMaker is not as easy as one would think. I think Amazon needs to team up with a data scientist who does ensemble modeling.
- Because SageMaker is targeted for machine learning models, other models a data scientist might use require more effort to get them incorporated. My guess is Amazon is moving to make SageMaker a more complete tool.
- SageMaker can take a long time to run on larger data sets. That's the case with every big data science tool I've used, but SageMaker doesn't seem to be as quick as other tools.
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.
March 29, 2018
Amazon SageMaker Review
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.
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.