Deploy Azure Data Bricks Model in Azure Machine Learning

✅ Azure Databricks is a data analytics platform optimized for the Microsoft Azure cloud services platform.

✅ Azure Databricks offers 3️⃣ environments for developing data-intensive applications: Databricks SQL, Databricks Data Science & Engineering, and Databricks Machine Learning.

✅ Raw data is often noisy and unreliable and may contain missing values and outliers. Using such data for Machine Learning can produce misleading 📊 results. Thus, data cleaning of the raw data is one of the most important steps in preparing data for Machine Learning. 🖥️

✅ To train a machine learning model with Azure Databricks, data scientists can use the Spark ML library. In this module, you learn how to train and evaluate a machine learning model using the Spark ML library as well as other machine learning frameworks.
✅ You can deploy a model to several kinds of compute target: including local compute, an Azure Container Instance (ACI), an Azure Kubernetes Service (AKS) cluster, or an Internet of Things (IoT) module.
✅ Azure Machine Learning uses containers as a deployment mechanism, packaging the model and the code to use it as an image that can be deployed to a container in your chosen compute target

📚 Read the blog at to know more in detail about these steps and how to Deploy Azure Data Bricks Model in Azure Machine Learning.
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About the Author Yogesh Pathak