DP-604 Implement a data science and machine learning solution for AI with Microsoft Fabric
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Duration
1 day
Location
Course Language English
Training Solutions Online Live
Data, AI, and the cloud are becoming increasingly intertwined. Successful projects rely on platforms that combine analytics and machine learning and map the entire model lifecycle.
Key topics
- Integration of data science into Microsoft Fabric.
- Modeling and training of machine learning approaches.
- Use of modern analytics architectures.
- Automated processes from the data source to the model.
- Operation and further development of AI solutions.
Prerequisites
Solid knowledge of data analysis, initial experience with machine learning, and understanding of cloud environments.
Target group
Technically oriented professionals who develop and use data-based AI models.
The combination of analytics and machine learning in a single platform creates transparency and efficiency. This lays a solid foundation for the professional use of AI in data-driven scenarios.
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Course content
Data science in Microsoft Fabric
- Understanding the data science process
- Explore and process data with Microsoft Fabric
- Train and evaluate models with Microsoft Fabric
Data analysis in Microsoft Fabric
- Examine notebooks
- Provide data for analysis
- Analyzing data distribution
- Checking for missing values in notebooks
- Applying advanced analysis methods
- Visualizing charts in notebooks
Data Wrangler in Microsoft Fabric
- Understanding data wranglers
- Examine data
- Handling missing data
- Transform data with operators
MLflow in Microsoft Fabric
- Train machine learning models
- Using MLflow for model tracking
- Manage models in Microsoft Fabric
Batch predictions in Microsoft Fabric
- Adjust model behavior for batch scoring
- Prepare data for predictions
- Save predictions in delta table
Why is knowledge of Microsoft Fabric for Data Science in such high demand right now?
- Data platforms are merging analytics and AI. Fabric combines both, reduces tool clutter, and significantly accelerates productive ML projects.
What specific problems can be solved with Fabric-based ML solutions?
- Fragmented data, slow pipelines, and lack of scalability are being replaced by integrated workflows and faster model deployment.
What prior knowledge is helpful for getting started quickly?
- A foundation in data analysis, Python, and machine learning makes it easier to get started and ensures direct application in real-world scenarios.
How practical is working with machine learning in Fabric?
- The focus is on real-world use cases: data preparation, model training, and deployment all take place within a single, consistent platform.
What advantages does Fabric offer over traditional ML environments?
- Centralized data storage, integrated tools, and fewer interfaces reduce complexity and increase efficiency throughout the entire ML lifecycle.
What career opportunities does expertise in fabric open up?
- Skills that bridge data, AI, and the cloud are in demand. Fabric expertise clearly positions you for modern data and AI roles.
How does a lack of machine learning expertise affect businesses?
- Unstructured data goes unused, decisions are delayed, and competitive advantages are lost.
What results can realistically be expected after implementing Fabric projects?
- Faster insights, more productive ML models, and better decision-making through integrated data and AI processes.
