Azure Data Scientist Associate Boot Camp
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.