Amazon SageMaker vs Google Cloud AI

• 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.

Read full review

• New products - Google is constantly releasing and adding new products to this API, it seems to be on of the fastest-growing products for this.
• Speed - The API is a lot faster than most of the alternative Computer Vision and general-use Machine Learning APIs out there.
• Comprehensive results - The results the API returns for most of the products don't require recurring API requests for processing. Everything is included and organized exactly in the JSON response as mentioned in their documentation for most of these Cloud AI products.
• The documentation format for this API is much better than some of Google's other documentation.

Read full review

• 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.

Read full review

• It is great that it helps shortcut and boost productivity, but AI is very much in infancy so it comes back with some crazy responses.
• Not sure how secure the Google Cloud AI is with fraudsters and cybercriminals. Not that we have seen compromises, but building trust all of the time is crucial.
• I always support the need for more training, procedures or on the spot tips for cloud applications. This would make it more seamless, especially since tech is changing so rapidly. It's hard to keep up, so we all need some help to make it 'smoother'.

Read full review

• SageMaker was a positive return on investment for those analysts that wanted to use new tools and had the aptitude to implement machine learning models with new resources. Such individuals were quick to improve their productivity with SageMaker.
• SageMaker was not a useful tool for analysts used to running reports on small data. Machine learning is a new area, and analysts that are only marginally interested in machine learning will find SageMaker not worth the effort.
• When used in combination with other machine learning tools, SageMaker is a perfect addition because it complements the Amazon ecosystem while the other tools provide more simple model development environments.

Read full review

• Artificial intelligence and automation seems 'free' and draws the organization in, without seeming to spend a lot of funds. A positive impact, but who is actually tracking the cost?
• We want our employees to use it, but many resist technology or are scared of it, so we need a way to make them feel more comfortable with the AI.
• The ROI seems positive since we are full in with Google, and the tools come along with the functionality.

Read full review