Implementing a Machine Learning solution with Azure Databricks (DP-3014)
Azure Databricks is a cloud-scale platform for data analytics and machine learning. Data scientists and machine learning engineers can use Azure Databricks to implement machine learning solutions at scale.
$995.00
Azure Databricks is a cloud-scale platform for data analytics and machine learning. Data scientists and machine learning engineers can use Azure Databricks to implement machine learning solutions at scale.
More Information:
- Modality: Virtual
- Provider: Microsoft
- Difficulty: Intermediate
- Duration: 1 Day
- Course Info: Download PDF
- Certificate: See Sample
Course Information
About This Course:
Azure Databricks offers a collaborative environment where data scientists and engineers can seamlessly integrate with popular tools like Apache Spark™ for big data processing and MLflow for managing the end-to-end machine learning lifecycle
Course Objectives:
- Get started with Azure Databricks
- Identify Azure Databricks workloads
- Understand key concepts
- Get to know Spark
- Create a Spark cluster
- Use Spark in notebooks
- Use Spark to work with data files
- Visualize data
- Understand principles of machine learning
- Machine learning in Azure Databricks
- Prepare data for machine learning
- Train a machine learning model
- Evaluate a machine learning model
- Capabilities of MLflow
- Run experiments with MLflow
- Register and serve models with MLflow
- Optimize hyperparameters with Hyperopt
- Review Hyperopt trials
- Scale Hyperopt trials
- What is AutoML?
- Use AutoML in the Azure Databricks user interface
- Use code to run an AutoML experiment
- Understand deep learning concepts
- Train models with PyTorch
- Distribute PyTorch training with Horovod
Audience:
- 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
Prerequisites:
- Fundamental understanding of Azure
- Basic knowledge of machine learning concepts
- Experience with Python programming
- Familiarity with Apache Spark and Databricks platform
- Prior exposure to data processing and ETL tasks
