Amazon SageMaker

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.

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.

Less appropriate scenarios:

• For data analysis tasks.

• For a data scientist who has less of a programming background.

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.

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.

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.

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.