Designing and Implementing a Data Science Solution on Azure DP-100

The DP-100 Designing and Implementing a Data Science Solution on Azure Course is a training program that focuses on acquiring the skills needed to design, develop, and implement data science solutions on Microsoft Azure. Participants will learn how to use the tools and services offered by the Azure platform to manage, analyze, and gain insights from large amounts of data. In the course, participants will be introduced to a variety of cloud services and technologies, including Azure Machine Learning, Azure Databricks, and Azure Synapse Analytics. They will learn how to create machine learning models using the Python programming language and use data science libraries such as scikit-learn, TensorFlow, and PyTorch. Additionally, participants will gain skills in creating and managing data science pipelines, tuning and monitoring models, and integrating solutions with other Azure applications and services. The course is structured to include both theoretical and practical lectures, allowing participants to apply the knowledge learned through exercises, projects, and case studies. This combined approach ensures that participants have a solid understanding of theoretical concepts and are able to apply the acquired skills in practice. The course contributes to the preparation of the Azure Data Scientist Associate Certification exam .

Submit a request

Course Objectives

Below is a summary of the main objectives of the Designing and Implementing a Data Science Solution on Azure DP-100 Course :

Use Azure Machine Learning and Azure Databricks.
Building Machine Learning Models with Python.
Manage data science pipelines.
Optimize and monitor data science models.
Integrate data science solutions with other Azure applications and services.
Implement data preparation and feature engineering techniques.
Deploy machine learning models to production environments.
Use Azure Data Factory for data ingestion and preparation.

Course Certification

This course helps you prepare to take the:
DP-100 Azure Data Scientist Associate Exam ;

Course Outline

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
Create and manage registries

Manage data in an Azure Machine Learning workspace

Select Azure Storage resources
Register and maintain datastores
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 Apache Spark pools
Monitor compute utilization

Explore data by using data assets and data stores

Access and wrangle data during interactive development
Wrangle interactive data with Apache Spark

Create models by using the Azure Machine Learning designer

Create a training pipeline
Consume data assets from the designer
Use custom code components in 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
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
Track model training by using MLflow
Evaluate a model
Train a model by using Python SDKv2
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

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 a model

Configure settings for online deployment
Configure compute for a batch deployment
Deploy a model to an online endpoint
Deploy a model to a batch endpoint
Test an online deployed service
Invoke the batch endpoint to start a batch scoring job

Apply machine learning operations (MLOps) practices

Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub
Automate model retraining based on new data additions or data changes
Define event-based retraining triggers

Course Mode

Instructor-Led Remote Live Classroom Training;

Trainers

Trainers are authorized Instructors in Microsoft and certified in other IT technologies, with years of hands-on experience in the industry and in Training.

Lab Topology

For all types of delivery, the participant can access the equipment and actual systems in our laboratories or directly in international data centers remotely, 24/7. Each participant has access to implement various configurations, Thus immediately applying the theory learned. Below are some scenarios drawn from laboratory activities.

Course Details

Course Prerequisites

Participants will need to have a basic understanding of Machine Learning theories through one of the following frameworks: Scikit-Learn, PyTorch, or TensorFlow, and basic Python programming.

Course Duration

Intensive duration 3 days;

Course Frequency

Course Duration: 3 days (9.00 to 17.00) - Ask for other types of attendance.

Course Date

Designing and Implementing a Data Science Solution on Azure Course (Intensive Formula) – On request – 09:00 – 17:00

Steps to Enroll

Registration takes place by asking to be contacted from the following link, or by contacting the office at the international number +355 45 301 313 or by sending a request to the email info@hadartraining.com