- 1. What is the Azure Data Scientist Associate Bootcamp?The Azure Data Scientist Associate Bootcamp is an immersive training program offered by DreamsPlus, designed to provide hands-on experience with data science tools and techniques on Azure. It prepares you for the Microsoft Azure Data Scientist Associate Certification (DP-100) and equips you with practical skills for machine learning solutions.
- 2. Who should attend this bootcamp?This course is ideal for:
- Aspiring data scientists looking to specialize in Azure.
- IT professionals, analysts, and machine learning engineers.
- Those aiming to earn the Azure Data Scientist Associate Certification.
- 3. What topics are covered in the bootcamp?The syllabus includes:
- Designing and preparing machine learning solutions.
- Exploring data and training models.
- Preparing models for deployment.
- Deploying and retraining models.
- Applying MLOps practices for efficient workflows.
- 4. What are the prerequisites for joining?Participants should have:
- Basic knowledge of Python programming.
- Familiarity with data science concepts such as data preparation, model training, and evaluation.
- Understanding of Azure fundamentals is recommended but not mandatory.
- 5. How is the course structured?The course combines theory with hands-on labs:
- In-person classes in Chennai for practical, face-to-face learning.
- Online sessions for flexible, interactive training accessible worldwide.
- 6. Will this bootcamp prepare me for the DP-100 certification exam?Yes! The curriculum is aligned with the DP-100: Designing and Implementing a Data Science Solution on Azure certification exam. It provides practical experience and exam-focused training to ensure your success.
- 7. What practical skills will I gain?You will learn to:
- Manage Azure Machine Learning workspaces and compute resources.
- Use Azure ML Designer and automated ML for building models.
- Train, evaluate, and deploy machine learning models.
- Apply ethical AI principles and MLOps practices.
- 8. How long does the bootcamp last, and are flexible schedules available?The bootcamp typically spans 4–8 weeks. Flexible weekday and weekend schedules are offered to accommodate professionals and students.
- 9. Will I receive a certificate upon completion?Yes, you’ll receive a DreamsPlus Bootcamp Completion Certificate. Additionally, the program prepares you to achieve the Azure Data Scientist Associate Certification (DP-100).
- 10. How do I enroll or get more details about the program?You can enroll or inquire through: 📞 Phone: +91 95000 56713 📧 Email: support@dreamsplus.in Visit dreamsplus.in for detailed information and registration!
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Azure Data Scientist Associate Boot Camp
Microsoft Azure Data Scientist Associate Certification Course DreamsPlus provides a thorough Azure Data Scientist Associate Boot Camp…
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Microsoft Azure Data Scientist Associate Certification Course
DreamsPlus provides a thorough Azure Data Scientist Associate Boot Camp in Chennai and online that is intended to give you practical experience and get you ready for the Microsoft data science certification.
Syllabus
- Design and prepare a machine learning solution (20–25%)
- Explore data, and train models (35–40%)
- Prepare a model for deployment (20–25%)
- Deploy and retrain a model (10–15%)
Design and prepare a machine learning solution (20–25%)
Design a machine learning solution
- Ascertain which compute specs are suitable for a training workload.
- Describe the needs for model deployment.
- Decide the model-building or model-training approach to employ.
Manage an Azure Machine Learning workspace
- Establish a workspace for Azure Machine Learning.
- Use developer tools to manage a workspace and facilitate workplace interaction.
- Set up source control with Git integration.
- Establish and oversee registries
Manage data in an Azure Machine Learning workspace
- Choose Azure Storage options.
- register and keep up datastores
- Construct and oversee data assets.
Manage compute for experiments in Azure Machine Learning
- Establish computational targets for training and experimentation.
- Choose a setting for a use case involving machine learning.
- Set up the serverless Spark compute and Azure Synapse Spark pools, among other connected compute resources.
- Track the use of computing resources.
Explore data, and train models (35–40%)
Explore data by using data assets and data stores
- Obtain and manage data when developing interactive applications.
- Manage interactive data using serverless Spark computing and connected Synapse Spark pools.
Create models by using the Azure Machine Learning designer
- Establish a pipeline for training.
- Consume the designer’s data assets.
- Utilize unique code segments in the designer.
- Review the model and incorporate ethical AI principles.
Use automated machine learning to explore optimal models
- For tabular data, use automated machine learning.
- For computer vision, use automated machine learning.
- For natural language processing, employ automated machine learning.
- Choose and comprehend training options, including algorithms and preprocessing.
- Analyze a machine learning run that is automated and consider appropriate AI practices.
Use notebooks for custom model training
- Develop code by using a compute instance.
- Monitor model training using MLflow.
- Assess a model
- Use the Python SDK v2 to train a model.
- To configure a compute instance, use the terminal.
Tune hyperparameters with Azure Machine Learning
- Choose a technique for sampling.
- Specify the area of interest.
- Describe the main metric.
- Describe your choices for an early termination.
Prepare a model for deployment (20–25%)
Run model training scripts
- Set the parameters for a script’s task run.
- Set up the computer to execute a job.
- Use information from a data asset for a task.
- Create a job using Azure Machine Learning to run a script.
- Log metrics from a task run using MLflow.
- To troubleshoot job run failures, use logs.
- Set up the environment in which a job will operate.
- Set limits for a task.
Implement training pipelines
- Build a pipeline.
- Transfer data between pipeline phases.
- Execute and plan a pipeline.
- Keep an eye on pipeline runs.
- Make unique parts
- Employ pipelines that are based on components.
Manage models in Azure Machine Learning
- Explain the MLflow model’s output.
- Choose a suitable framework for encapsulating a model.
- Evaluate a model using ethical AI concepts.
Deploy and retrain a model (10–15%)
Deploy a model
- Set up the environment for the online deployment.
- Set up the computing for a deployment in batches.
- Install a model on a web page.
- Install a model on a batch destination.
- Examine a deployed online service.
- To begin a batch scoring job, invoke the batch endpoint.
Apply machine learning operations (MLOps) practices
- Start an Azure Machine Learning job from GitHub or Azure DevOps, for example.
- Retrain the model automatically in response to modifications or additions of fresh data.
- Explain triggers for event-based retraining.
FAQs for Azure Data Scientist Associate Bootcamp
What Will You Learn?
- Implement Security Best Practices
- Configure Compliance Settings
- Manage Identity and Access
- Monitor and Respond to Threats
Course Curriculum
Azure Security Hands-on Experience
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Get ready to earn the Associate certification in Azure Data Science.
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Develop your data science expertise in order to pass the Microsoft certification.
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Gain more professional opportunities by developing your data science skills.
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Obtain the Azure Data Scientist Associate certificate to stay ahead of the competition in the demanding work market.
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