Review on Grace AI Platform by Marcelo Montalvo

Easy deployment of models with all major cloud vendors including AWS Lambda / Fargate as well as Open Source frameworks such as Apache MXNet, CNTK etc. Support across multiple languages like Java/Python makes it suitable for wider developer audience. No direct link from GCP Console on Cloud Run service - hence requires custom API calls or running locally using Docker image which we do not have any control over their release cycle (and need additional licenses). In some cases model size limit may be too high for large datasets that are being processed in these environments. Considerations should take into account both serverless deployments vs traditional serverside architectures when choosing solution architecture. We deployed various machine learning algorithms to run real time data analytics against public IoT devices and sensors - resulting metrics were fed back directly to customers through application dashboards. The ease with which one can create models using Python API's or RAPI (R as well). Also it has good documentation available online along side training material provided through their website itself that makes learning process easy & fast! I would like if they have more tutorials from other languages such as Java / Scala etc., but overall its pretty cool experience so far! Very helpful in building predictive analytics model quickly without having any coding knowledge required!!