Post by rahim on Jan 31, 2024 10:27:45 GMT
Cloud AI PlatformGoogle has been heavily involved in the area of machine learning and artificial intelligence for a long time and has, among other things, developed TensorFlow , a now very popular framework for the development of sophisticated ML models. In 2017, Google also launched Kubeflow , an open source project that aims to enable distributed machine learning for Kubernetes .The previous flagship in the area of ML-as-a-Service was the Google Cloud ML Engine , which was integrated into the Google Cloud AI Platform in 2019 .
With the AI platform, Google is trying to bring all DB to Data assets under one roof, which will cover the entire spectrum of ML services, including data preparation, training, tuning and provision of models.AI Hub acts as a central hub for discovering, sharing and deploying ML models and contains a collection of models based on popular frameworks such as Tensorflow , PyTorch , Keras , XGBoost and Scikit-learn and running in Kubeflow , on virtual servers, Jupyter Notebooks or via Google's AI APIs.As with Amazon SageMaker, the entire ML workflow is heavily.
Code-based and runs via Python scripts, with the corresponding advantages in terms of flexibility and disadvantages in terms of usability. Amazon SageMaker has a somewhat broader range of integrable frameworks with MXNet , Chainer and SparkML , but the latest version of Tensorflow with all new features is always available on the Google AI Platform , while Amazon SageMaker is often a few weeks behind.ML platforms in comparisonAmazonSageMaker Azure ML Studio Azure MLServices Google AIPlatformGraphical UI – Yes – –Built-in algorithms Yes.