DP-3014 Implementing a Machine Learning solution with Azure Databricks

Length: 1 Day(s), Price: $$675.00 exl GST

Azure Databricks is a collaborative Apache Spark-based platform optimized for Azure, commonly used for big data analytics and machine learning tasks. Industries use Azure Databricks to process large datasets, build predictive models, and improve decision-making through data-driven insights. The platform enables seamless integration with various Azure services, providing a unified environment for data engineering, model development, and deployment. It is designed for high-performance analytics, facilitating scalable and efficient handling of big data workloads across various industry verticals.

  • Understanding the fundamentals of Azure Databricks and its integration within the Azure ecosystem.
  • Learning how to set up Azure Databricks workspaces and clusters for running machine learning workloads.
  • Gaining proficiency in using Databricks Notebooks for developing and managing machine learning models.
  • Acquiring skills in preparing and processing data effectively using Azure Databricks.
  • Exploring various machine learning libraries available in Azure Databricks and how to apply them.
  • Mastering the deployment of machine learning models at scale within the Databricks environment.
  • Grasping best practices for monitoring and optimizing machine learning solutions in Databricks.

Data professionals seeking to utilize Azure Databricks for ML

Data scientists/engineers wanting to apply ML workflows

Azure users aiming to implement scalable ML solutions

Professionals preparing for Azure Databricks certifications

Technical personnel interested in combining Azure services with ML

This learning path assumes that you have experience of using Python to explore data and train machine learning models with common open source frameworks, like Scikit-Learn, PyTorch, and TensorFlow.

1. Explore Azure Databricks

2. Use Apache Spark in Azure Databricks

3. Train a machine learning model in Azure Databricks

4. Use MLflow in Azure Databricks

5. Tune hyperparameters in Azure Databricks

6. Use AutoML in Azure Databricks

7. Train deep learning models in Azure Databricks

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