Microsoft reveals a new drag-and-drop UI for ML model creation and deployment experience


Today, Microsoft announced new updates for its Azure Machine Learning service. The main aim of these updates is to enable developers and data professionals of any skill level to build advanced machine learning models.

Azure Machine Learning service is now suitable for these three levels of developers and data scientists: 1) Developers and data scientists who like to write code. 2) People including business domain experts, may know a lot about data, but they don’t know much about machine learning or code. 3) People who are learning machine learning concepts, they want to make their own models, but they are not coders.

Microsoft today announced the following new updates:

  • MLOps capabilities with Azure DevOps integration provides developers with reproducibility, auditability and automation of the end-to-end machine learning lifecycle.
  • Automated ML advancements and an intuitive UI make developing high-quality models easier.
  • Visual machine learning interface provides no-code model creation and deployment experience with drag-and-drop capabilities.
  • To enable extremely low latency and cost-effective inferencing, Microsoft is announcing the general availability of hardware-accelerated models that run on FPGAs, as well as ONNX Runtime support for NVIDIA TensorRT and Intel nGraph for high-speed inferencing on NVIDIA and Intel chipsets

Microsoft also announced a Azure Cognitive Services called Decision, which gives specific recommendations to help people make decisions. This new category includes Personalizer, which uses a branch of AI called reinforcement learning to help technology glean knowledge from its own experiences and then offer informed recommendations.

You can learn more about other AI related announcements from Build here.

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