At Microsoft Ignite, we announced the general availability of Azure Machine Learning designer, the drag-and-drop workflow capability in Azure Machine Learning studio which simplifies and accelerates the process of building, testing, and deploying machine learning models for the entire data science team, from beginners to professionals. We launched the preview in November 2019, and we have been excited with the strong customer interest. We listened to our customers and appreciated all the feedback. Your responses helped us reach this milestone. Thank you.
“By using Azure Machine Learning designer we were able to quickly release a valuable tool built on machine learning insights, that predicted occupancy in trains, promoting social distancing in the fight against Covid-19.” - Steffen Pedersen, Head of AI and advanced analytics, DSB (Danish State Railways)
Artificial intelligence (AI) is gaining momentum in all industries. Enterprises today are adopting AI at a rapid pace with different skill sets of people, from business analysts, developers, data scientists to machine learning engineers. The drag-and-drop experience in Azure Machine Learning designer can help your entire data science team to speed up machine learning model building and deployment. Specially, it is tailored for:
Azure Machine Learning designer is fully integrated with Azure Machine Learning dataset service for the benefit of versioning, tracking and data monitoring. You can import data by dragging and dropping a registered dataset from the asset library, or connecting to various data sources including HTTP URL, Azure blob, Azure Data Lake, Azure SQL or upload from a local file with Import Data module . You can use right click to preview and visualize the data profile, and preprocess data using a rich set of built-in modules for data transformation and feature engineering.
In Azure Machine Learning designer, you can build and train machine learning models with state-of-the art machine learning and deep learning algorithms, including those for traditional machine learning, computer vision, text analytics, recommendation and anomaly detection. You can also use customized Python and R code to build your own models. Each module can be configured to run on different Azure Machine Learning compute clusters so data scientists don’t need to worry about the scaling limitation and can focus on their training work.
You can evaluate and compare your trained model performance with a few clicks using the built-in evaluate model modules, or use execute Python/R script modules to log the customized metrics/images. All metrics are stored in run history and can be compared among different runs in the studio UI.
While interactively running machine learning pipelines, you can always perform quick root cause analysis using the graph search and navigation to quickly nailed down to the failed step, preview logs and outputs for debugging and troubleshooting without losing context of the pipeline, and find snapshots to trace scripts and dependencies used to run the pipeline.
Data scientists and machine learning engineers can deploy models for real-time and batch inferencing as versioned REST endpoints to their own environment. You don’t need to worry about the deep knowledge of coding, model management, container services, etc., as scoring files and the deployment image are automatically generated with a few clicks. Models and other assets can also be registered in the central registry for MLOps tracking, lineage, and automation.
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