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 is used by a specific department that supports machine learning models development and deployment. From my perspective, …
We use SageMaker in the engineering and data science departments to host Jupyter notebooks, periodically retrain models, and serve models …
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 is currently being used by our analytics and technology groups but managed by the associates at our firm. It addresses …
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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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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.
- For data analysis tasks.
- For a data scientist who has less of a programming background.
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.
Amazon SageMaker is a great tool for developing machine learning models that take more effort than just point-and-click type of analyses. The software works well with the other tools in the Amazon ecosystem, so if you use Amazon Web Services or are thinking about it, SageMaker would be a great addition. SageMaker is great for consumer insights, predictive analytics, and looking for gems of insight in the massive amounts of data we create. SageMaker is less suitable for analysts who do generally "small" data analyses, and "small" data analyses in today's world can be billions of records.
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.
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.
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.