DP-3014 Implementing a Machine Learning solution with Azure Databricks
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Duration
1 day
Location
Course Language English
Training Solutions Online Live
Scalable machine learning solutions are created through the interaction of data engineering, modeling, and cloud technology. Azure Databricks is establishing itself as a central tool for modern AI architectures.
Key topics
- Building Databricks environments in Azure.
- Data integration and transformation.
- Development, training, and evaluation of ML models.
- Using MLflow for transparency and traceability.
- Automated model deployment
- Operation, maintenance, and optimization.
Prerequisites
Solid foundation in data analysis, scripting languages, and understanding of cloud services and machine learning.
Target audience
Data scientists, data engineers, and IT professionals with a focus on AI-powered cloud solutions.
The content promotes a holistic understanding of machine learning in Azure and supports the professional development of robust, scalable AI systems.
- Start Azure Databricks
- Identify Azure Databricks workloads
- Understanding key concepts
- Discover Spark
- Create clusters
- Use Spark in notebooks
- Use Spark for data files
- Visualize data
- Understanding the principles of machine learning
- Machine learning in Azure Databricks
- Prepare data for ML
- Train a model for ML
- Evaluate ML model
- MLflow features
- Experiments with MLflow
- Model registration and deployment
- Optimize hyperparameters with Hyperopt
- Evaluate Hyperopt experiments
- Scale Hyperopt experiments
- Automation of model training.
- Using AutoML in Azure Databricks
- Scripts for AutoML workflows
- Understanding deep learning
- Train models in PyTorch
- Distributing PyTorch training with Horovod
