Amazon Forecast vs. Amazon SageMaker

I personally get the feeling that Amazon Forecast must have been a direct product released on the models Amazon themselves must have used at some port. Amazon Forecast definitely shines when using it for product demand, inventory, and pricing throughout store locations, etc. Everything, including data-set importing, works best in this context. When applying it to more edge cases like resource planning around events, it can be a bit more tricky to apply effectively.

Incentivized

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.

Incentivized

• Develop benchmarks for use in future forecast.
• Performs contingency planning during challenging financial times.
• Anticipate the impact of new expanse.

Incentivized

• 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.

Incentivized

• Potential very high costs
• Predicting ongoing running costs
• Very product focused

Incentivized

• Searching and descriptions can be easier to read and interpret.
• Training modules and customer service training representative could make on boarding employees easier.

Incentivized

There was no other product or service that was considered before making the choice to go with Amazon Forecast as the forecasting that the company was looking for had everything running in the AWS environment, the choice had to be obvious. Integrating with Amazon Forecast also ensured that everything is under a single roof and we didn't have to go multiple places looking for data when needed.

Incentivized

We have not invested in another machine learning software at this time and so far this has proved very successful with our machine learning teams. As mentioned, I am training these individuals simply on the fundamentals of the software and using it/customizing it for their needs. It has been very easy to do this and has gotten great reviews across the organization so far.

Incentivized

• Insights have been available much more quickly and well designed manner
• Integration with other tools has been flawless

Incentivized

• Using SageMaker, we can truly implement 'fail early, learn fast,' using an on-demand server for training.
• It also saves your money from investing in a physical server for very rare use.
• However, the pricing is high, but it will cost you only for what you use.

Incentivized