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Azure Data Scientist Associate Boot Camp

Azure Data Scientist Associate Boot Camp DreamsPlus provides a thorough Azure Data Scientist Associate Boot Camp in…

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.

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

  • Get ready to earn the Associate certification in Azure Data Science.
  • Develop your data science expertise in order to pass the Microsoft certification.
  • Gain more professional opportunities by developing your data science skills.
  • Obtain the Azure Data Scientist Associate certificate to stay ahead of the competition in the demanding work market.