Azure Machine Learning - Public Preview for November

Three features now available in public preview enable you to choose which models/datasets are right for you without having to run any job, seamlessly read/write data from a Fabric datastore; and Model-as-a-Service with inference APIs and hosted fine-tuning. [Model Benchmarks for Curated Models](https://aka.ms/MC%5FLeaderboard): You can now view the model leaderboard table with models from the Model Catalog, publicly available datasets, and pre-populated metrics. [Create a Fabric OneLake Lakehouse Datastore type](https://aka.ms/OneLake%5FDatastore): You can now directly consume data assets from a Fabric Lakehouse in AzureML by creating a OneLake Lakehouse datastore type. [Model-as-a-Service with inference APIs and hosted fine-tuning](https://aka.ms/MaaS%5Fllama2): It enables machine learning professionals to easily integrate foundation models such as Llama 2 from Meta as an application programming interface (API) endpoint to their applications and fine-tune models without having to manage the underlying GPU infrastructure. * Azure Machine Learning * Features * Microsoft Ignite * [ Azure Machine Learning](https://azure.microsoft.com/en-gb/products/machine-learning/)