Designing and Implementing a Data Science Solution on Azure DP100 Overview

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

Module 1: Design a Data Ingestion Strategy for Machine Learning Projects

  • Introduction
  • Identify Your Data Source and Format
  • Choose How to Serve Data to Machine Learning Workflows
  • Design a Data Ingestion Solution
  • Exercise: Design a Data Ingestion Strategy

Module 2: Design a Machine Learning Model Training Solution

  • Introduction
  • Identify Machine Learning Tasks
  • Choose a Service to Train a Machine Learning Model
  • Decide Between Compute Options
  • Exercise: Design a Model Training Strategy

Module 3: Design a Model Deployment Solution

  • Introduction
  • Understand How Model Will Be Consumed
  • Decide on Real-Time or Batch Deployment
  • Exercise - Design a Deployment Solution

Module 4: Azure Machine Learning Workspace Resources and Assets

  • Introduction
  • Video - Explore the Azure Machine Learning Workspace
  • Create an Azure Machine Learning Workspace
  • Identify Azure Machine Learning Resources
  • Identify Azure Machine Learning Assets
  • Train Models in the Workspace
  • Exercise - Explore the Workspace

Module 5: Developer Tools for Workspace Interaction

  • Introduction
  • Studio
  • Python SDK
  • CLI
  • Exercise-Explore the Developer Tools

Module 6: Make Data Available in Azure Machine Learning

  • Introduction
  • Video - Make Data Available in Azure Machine Learning
  • Understand URIs
  • Create a Datastore
  • Create a Data Asset
  • Exercise - Make Data Available in Azure Machine Learning

Module 7: Work with Compute Targets in Azure Machine Learning

  • Introduction
  • Choose the Appropriate Compute Target
  • Create and Use a Compute Instance
  • Create and Use a Compute Cluster
  • Exercise - Work with Compute Resources

Module 8: Work with Environments in Azure Machine Learning

  • Introduction
  • Understand Environments
  • Use Curated Environments
  • Create and Use Custom Environments
  • Exercise - Work with Environments

Module 9: Classification Model with Automated Machine Learning

  • Introduction
  • Video - Find the Best Classification Model with Automated Machine Learning
  • Preprocess Data and Configure Featurisation
  • Run an Automated Machine Learning Experiment
  • Evaluate and Compare Models
  • Exercise - Find the Best Classification Model with Automated Machine Learning

Module 10: Track Model Training in Jupyter Notebooks with MLflow

  • Introduction
  • Configure MLflow For Model Tracking in Notebooks
  • Train and Track Models in Notebooks
  • Exercise - Track Model Training

Module 11: Run Training Script as a Command Job in Azure Machine Learning

  • Introduction
  • Video - Run a Training Script as a Command Job in Azure Machine Learning
  • Convert a Notebook to a Script
  • Run a Script as a Command Job
  • Use Parameters in a Command Job
  • Exercise - Run a Training Script as a Command Job

Module 12: Track Model Training with MLflow in Jobs

  • Introduction
  • Video - Track Model Training with MLFlow in Jobs
  • Track Metrics with MLflow
  • View Metrics and Evaluate Models
  • Exercise - Use MLflow to Track Training Jobs

Module 13: Run Pipelines in Azure Machine Learning

  • Introduction
  • Video - Run Pipelines in Azure Machine Learning
  • Create Components
  • Create a Pipeline
  • Run a Pipeline Job
  • Exercise - Run a Pipeline Job

Module 14: Perform Hyperparameter Tuning with Azure Machine Learning

  • Introduction
  • Define a Search Space
  • Configure a Sampling Method
  • Configure Early Termination
  • Use a Sweep Job for Hyperparameter Tuning
  • Exercise - Run a Sweep Job

Module 15: Deploy a Model to a Managed Online Endpoint

  • Introduction
  • Managed Online Endpoints
  • Deploy the MLflow Model to a Managed Online Endpoint
  • Deploy a Model to a Managed Online Endpoint
  • Test Managed Online Endpoints
  • Exercise - Deploy an MLflow Model to an Online Endpoint

Module 16: Deploy a Model to a Batch Endpoint

  • Introduction
  • Video - Deploy a Model to a Batch Endpoint
  • Understand and Create Batch Endpoints
  • Deploy Your MLflow Model to a Batch Endpoint
  • Deploy a Custom Model to a Batch Endpoint
  • Invoke and Troubleshoot Batch Endpoints
  • Exercise - Deploy an MLflow Model to a Batch Endpoint

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Who should attend this Designing and Implementing a Data Science Solution on Azure DP-100 Course?

This Designing and Implementing a Data Science Solution on Azure (DP-100) Course is designed to introduce professionals to Azure's Data Science and Machine Learning solutions. This course can be beneficial to a wide range of professionals, including:

  • Data Scientists
  • Azure Data Engineers
  • Data Analysts
  • AI Engineers
  • Cloud Solution Architects
  • Machine Learning Developers
  • Big Data Engineers
  • Research Scientists

Prerequisites of the Designing and Implementing a Data Science Solution on Azure DP-100 Course

For attending this Designing and Implementing a Data Science Solution on Azure DP-100 Course, the required prerequisites are proficiency in one of the programming languages like Python, R, or SQL and a basic knowledge about Machine Learning and Azure Machine Learning.

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

This Designing and Implementing a Data Science Solution on Azure DP-100 Course plays a vital role in transforming raw data into actionable insights, making it more relevant than ever. As organisations harness the power of data to drive decision-making, this course provides essential knowledge for professionals looking to navigate the dynamic landscape of data science on Microsoft Azure.

Proficiency in Data Science on Microsoft Azure is crucial for professionals in various domains, including Data Analysts, Data Engineers, Machine Learning Engineers, and aspiring Data Scientists. This knowledge empowers them to harness the extensive capabilities of Microsoft Azure, allowing them to make informed decisions, build innovative solutions, and stay competitive in the evolving digital world.

In this 4-day Designing and Implementing a Data Science Solution on Azure DP-100 Training Course, delegates will learn essential skills and knowledge to design and implement data science solutions on Azure. They will gain expertise in data ingestion strategies, model training, model deployment, workspace resources, developer tools environments, and various aspects of Machine Learning and data pipelines. The Knowledge Academy’s highly skilled and professional trainers, with 9+ years of experience teaching Microsoft Courses, will conduct this training.

Course Objectives

  • To understand the principles of designing Data Science solutions on Azure
  • To gain proficiency in data ingestion, model training, and model deployment
  • To learn to utilise Azure Machine Learning workspace resources and tools
  • To acquire skills to work with compute targets and create data pipelines
  • To master the art of performing model evaluation and deployment
  • To develop expertise in tracking and managing models using MLflow

After completing this course, delegates become proficient in designing and implementing data science solutions on Microsoft Azure. They will be armed with comprehensive knowledge and practical skills. Graduates are prepared to tackle real-world data challenges. They can confidently apply their expertise in various domains, enabling businesses to harness the full potential of their data.

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What’s included in this Designing and Implementing a Data Science Solution on Azure DP-100 Course?

  • World-Class Training Sessions from Experienced Instructors
  • Designing and Implementing a Data Science Solution on Azure DP-100 Certificate
  • Digital Delegate Pack

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Why choose us

Ways to take this course

Experience live, interactive learning from home with The Knowledge Academy's Online Instructor-led Designing and Implementing a Data Science Solution on Azure DP100. Engage directly with expert instructors, mirroring the classroom schedule for a comprehensive learning journey. Enjoy the convenience of virtual learning without compromising on the quality of interaction.

Unlock your potential with The Knowledge Academy's Designing and Implementing a Data Science Solution on Azure DP100, accessible anytime, anywhere on any device. Enjoy 90 days of online course access, extendable upon request, and benefit from the support of our expert trainers. Elevate your skills at your own pace with our Online Self-paced sessions.

Experience the most sought-after learning style with The Knowledge Academy's Designing and Implementing a Data Science Solution on Azure DP100. Available in 490+ locations across 190+ countries, our hand-picked Classroom venues offer an invaluable human touch. Immerse yourself in a comprehensive, interactive experience with our expert-led Designing and Implementing a Data Science Solution on Azure DP100 sessions.

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Achieve certification without breaking the bank. Find a lower price elsewhere? We'll match it to guarantee you the best value

Streamline large-scale training requirements with The Knowledge Academy’s In-house/Onsite Designing and Implementing a Data Science Solution on Azure DP100 at your business premises. Experience expert-led classroom learning from the comfort of your workplace and engage professional development.

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Cut unnecessary costs and focus your entire budget on what really matters, the training.

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Team building opportunity

Our Designing and Implementing a Data Science Solution on Azure DP100 offers a unique chance for your team to bond and engage in discussions, enriching the learning experience beyond traditional classroom settings

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Monitor employees progress

The course know-how will help you track and evaluate your employees' progression and performance with relative ease

What our customers are saying

Designing and Implementing a Data Science Solution on Azure DP100 FAQs

Please arrive at the venue at 8:45am.
Azure Machine Learning is a cloud-based service that accelerates and manages the lifecycle of machine learning projects as well as deploys models and manages MLOps.
Azure Machine Learning models are developed and used to retrain, monitor, and deploy models with the help of open-source platforms, including TensorFlow, Pytorch, and Scikit-learn.
Understanding how models will be consumed, whether in real-time or batch deployment scenarios, is crucial for organisations to make their models accessible and valuable for decision-making processes.
Delegates can enhance their data science skills, expand career prospects, and stay at the forefront of the field by mastering the design and implementation of data science solutions on Azure. It provides hands-on experience with cutting-edge tools and technologies.
In this Designing and Implementing a Data Science Solution on Azure DP100 course, you will learn essential skills to design and implement end-to-end data science solutions on Azure. Topics covered include data ingestion strategies, model training, deployment, workspace resources, developer tools, compute targets, environments, and more.
The price for Designing and Implementing a Data Science Solution on Azure DP100 certification in Philippines starts from $3995
The Knowledge Academy is the Leading global training provider for Designing and Implementing a Data Science Solution on Azure DP100.
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"Really good course and well organised. Trainer was great with a sense of humour - his experience allowed a free flowing course, structured to help you gain as much information & relevant experience whilst helping prepare you for the exam"

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