Amazon SageMaker Reviews and Ratings

Use Cases and Deployment Scope

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.

Likelihood to Recommend

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.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

Well suited scenarios:

<ul><li>For quick POC of ML and DL.</li></ul><ul><li>To train a model on a large dataset using multiple servers.</li></ul><ul><li>To host a model to be used by multiple applications.</li></ul>Less appropriate scenarios:

<ul><li>For data analysis tasks.</li></ul><ul><li>For a data scientist who has less of a programming background.</li></ul>

Use Cases and Deployment Scope

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.

Likelihood to Recommend

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.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

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.

Use Cases and Deployment Scope

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.

Likelihood to Recommend

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.