Microsoft Certified: Microsoft Azure Data Scientist Associate – DP-100 Training and certification

DP-100: Azure Data Scientist Associate Training in Coimbatore
Course Overview
The DP-100 certification course, Designing and Implementing a Data Science Solution on Azure, is tailored for professionals who want to build and operationalize machine learning models in the Microsoft Azure environment. This course focuses on leveraging Azure Machine Learning (Azure ML) and related services to design, train, deploy, and manage ML models at scale.
At Linux Training Center in Coimbatore, our hands-on training ensures learners develop practical skills to become job-ready Azure data scientists, with a strong foundation in cloud-based machine learning and AI workflows.
Why Choose DP-100?
With data science becoming critical to decision-making and automation, businesses are increasingly adopting cloud platforms to scale their ML operations. The DP-100 certification helps professionals validate their ability to design and implement ML solutions using Azure’s robust ecosystem.
This course prepares you for the Microsoft Certified: Azure Data Scientist Associate credential and focuses on solving real-world business problems with data-driven techniques.
Who Should Enroll?
The course is ideal for data scientists, AI/ML engineers, data analysts, and Python developers who want to build, train, and manage ML models in Azure. It is also suitable for professionals transitioning from traditional data roles into cloud-based machine learning environments.
A basic understanding of data science concepts, Python, and Azure fundamentals is recommended.
What You Will Learn
Set up an Azure Machine Learning workspace and environment
Run experiments and train models using Azure ML SDKs and tools
Automate model training, tuning, and deployment
Manage compute targets, datasets, and pipelines
Monitor model performance and retrain using MLOps practices
Secure and operationalize models in production environments
Learners gain exposure to tools like Azure ML Studio, Jupyter Notebooks, and CLI to manage complete ML workflows effectively.
Course Highlights
Based on the official Microsoft DP-100 exam syllabus
Real-time labs using Azure ML, datasets, and notebooks
Delivered by industry-certified AI & cloud experts
Project-based learning with end-to-end ML pipeline development
Weekday and weekend flexible batches available
Career Opportunities
After completing this course, you can take on roles such as Azure Data Scientist, Machine Learning Engineer, AI Consultant, or ML Operations Engineer. The DP-100 certification adds significant value to your profile, especially if you are targeting positions that combine data science and cloud infrastructure.
Why Linux Training Center?
Linux Training Center in Coimbatore is known for practical, career-focused IT and cloud training. With experienced instructors and real-world project exposure, we ensure that learners are fully equipped to handle ML model lifecycles in enterprise Azure environments.
Microsoft Certified: Azure Data Scientist Associate - DP-100 Syllabus
Modules
Design and prepare a machine learning solution (20-25%)
Design a machine learning solution
- Determine the appropriate compute specifications for a training workload
- Describe model deployment requirements
- Select which development approach to use to build or train a model
Manage an Azure Machine Learning workspace
- Create an Azure Machine Learning workspace
- Manage a workspace by using developer tools for workspace interaction
- Set up Git integration for source control
Manage data in an Azure Machine Learning workspace
- Select Azure Storage resources
- Register and maintain data stores
- Create and manage data assets
Manage compute for experiments in Azure Machine Learning
- Create compute targets for experiments and training
- Select an environment for a machine learning use case
- Configure attached compute resources, including Azure Databricks and Azure Synapse Analytics
- Monitor compute utilization
Explore data and train models (35-40%)
Explore data by using data assets and data stores
- Load and transform data
- Analyze data by using Azure Data Explorer
- Use differential privacy
Create models by using the Azure Machine Learning designer
- Create a training pipeline
- Consume data assets from the designer
- Use designer components to define a pipeline data flow
- Use custom code components in the designer
- Evaluate the model, including responsible AI guidelines
Use automated machine learning to explore optimal models
- Use automated machine learning for tabular data
- Use automated machine learning for computer vision
- Use automated machine learning for natural language processing (NLP)
- Select and understand training options, including preprocessing and algorithms
- Evaluate an automated machine learning run, including responsible AI guidelines
Use notebooks for custom model training - Develop code by using a compute instance
- Consume data in a notebook
- Track model training by using MLflow
- Evaluate a model
- Train a model by using Python SDK
- Use the terminal to configure a compute instance
Tune hyperparameters with Azure Machine Learning
- select a sampling method
- define the search space
- define the primary metric
- define early termination options
Prepare a model for deployment (20-25%)
Run model training scripts
- Configure job run settings for a script
- Configure compute for a job run
- Consume data from a data asset in a job
- Run a script as a job by using Azure Machine Learning
- Use MLflow to log metrics from a job run
- Use logs to troubleshoot job run errors
- Configure an environment for a job run
- Define parameters for a job
Implement training pipelines
- Create a pipeline
- Pass data between steps in a pipeline
- Run and schedule a pipeline
- Monitor pipeline runs
- Create custom components
- Use component-based pipelines
Manage models in Azure Machine Learning
- Describe MLflow model output
- Identify an appropriate framework to package a model
- Assess a model by using responsible AI guidelines
Deploy and retrain a model (10-15%)
Deploy a model - Configure settings for real-time deployment
- Configure compute for a batch deployment
- Deploy a model to a real-time endpoint
- Deploy a model to a batch endpoint
- Test a real-time deployed service
- Invoke the batch endpoint to start a batch scoring job
Apply machine learning operations (MLOps) practices
- Trigger an Azure Machine Learning pipeline, including from Azure DevOps or GitHub
- Automate model retraining based on new data additions or data changes
- Define event-based retraining triggers
To ensure success in Microsoft Designing and Implementing a Data Science Solution on Azure certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Designing and Implementing a Data Science Solution on Microsoft Azure (DP-100) exam.