Amazon SageMaker

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.

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.

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.

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.

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.

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.