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madiepev
MicrosoftJoined Jul 14, 2020
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Machine learning details: Data, design, and deployment
If you’re a beginner who wants to learn about Azure Machine Learning, I can’t think of a better place to start than the Design a machine learning solution learning path on Microsoft Learn. These modules explore how to identify important requirements and when to use which Azure service when you’re working with machine learning models. It starts with data—the foundation of machine learning. Find out how to identify data sources and how to figure out the best tools for loading and transforming the data. Then move into designing a data ingestion strategy, a machine learning model training solution, a model deployment solution, and a machine learning operations (MLOps) solution. Plus, learn about bringing a model from development to production. This learning path has all the helpful details on machine learning data, design, and deployment—practical and necessary knowledge in your quest to #LearnMicrosoftAI.Big details on big data! Build your knowledge of Azure Databricks
As an intermediate-level data scientist or machine learning engineer, you know that you can use Azure Databricks to accelerate AI and innovation, with a single, cloud-based platform for big data processing, analytics, and machine learning. You can use also use it for complex forecasting, computer vision, natural language processing, and other AI workloads. If you’d like to build on your knowledge, I encourage you to check out the Machine learning with Azure Databricks learning path on Microsoft Learn. In these modules, find out how Azure Databricks makes it simple to run large-scale Spark workloads to transform, analyze, and visualize data. Train a predictive model, manage the machine learning lifecycle with MLflow, and build an effective machine learning model for your data with AutoML. With this practical learning path, get big details on big data, as you #LearnMicrosoftAI.
