Designing And Implementing A Data Science Solution On Azure in Edmonton

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

Module 1: Explore Azure Machine Learning Workspace Resources and Assets

• Introduction
• Create a Workspace
• Identify Resources
• Identify Assets
• Train Models
• Explore Workspace

Module 2: Explore Developer Tools for Workspace Interaction

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

Module 3: Make Data Available in Azure Machine Learning

• Introduction
• Understand URIs
• Create a Datastore
• Create a Data Asset
• Make Data Available

Module 4: Work with Compute Targets in Azure Machine Learning

• Introduction
• Choose a Compute Target
• Use a Compute Instance
• Use a Compute Cluster
• Work with Compute Resources

Module 5: Work with Environments in Azure Machine Learning

• Introduction
• Understand Environments
• Use Curated Environments
• Create Custom Environments
• Work with Environments

Module 6: Find the Best Classification Model with Automated Machine Learning

• Introduction
• Preprocess Data
• Run an Experiment
• Evaluate Models
• Find the Best Model

Module 7: Track Model Training in Jupyter Notebooks with MLflow

• Introduction
• Configure MLflow
• Track Models in Notebooks
• Track Model Training

Module 8: Run a Training Script as a Command Job in Azure Machine Learning

• Introduction
• Convert to Script
• Run a Command Job
• Use Parameters
• Run a Training Script

Module 9: Track Model Training with MLflow in Jobs

• Introduction
• Track Metrics
• View Metrics
• Use MLflow to Track Training Jobs

Module 10: Perform Hyperparameter Tuning with Azure Machine Learning

• Introduction
• Define a Search Space
• Configure Sampling
• Configure Termination
• Run a Sweep Job

Module 11: Run Pipelines in Azure Machine Learning

• Introduction
• Create Components
• Create a Pipeline
• Run a Pipeline Job

Module 12: Register an MLflow Model in Azure Machine Learning

• Introduction
• Log Models
• Understand Model Format
• Register a Model
• Log and Register Models

Module 13: Create and Explore the Responsible AI Dashboard for a Model in Azure Machine Learning

• Introduction
• Understand Responsible AI
• Create the Dashboard
• Evaluate the Dashboard
• Explore the Dashboard

Module 14: Deploy a Model to a Managed Online Endpoint

• Introduction
• Explore Online Endpoints
• Deploy to Online Endpoint
• Test Online Endpoints
• Deploy an Online Model

Module 15: Deploy a Model to a Batch Endpoint

• Introduction
• Understand Batch Endpoints
• Deploy to Batch Endpoint
• Invoke Batch Endpoints
• Deploy a Batch Model

Module 16: Plan and Prepare to Develop AI Solutions on Azure

• Introduction
• What is AI?
• Azure AI Services
• Azure AI Foundry
• Developer Tools and SDKs
• Responsible AI
• Explore AI Foundry Portal

Module 17: Choose and Deploy Models from the Model Catalog in Azure AI Foundry Portal

• Introduction
• Explore Language Models
• Deploy a Model
• Improve Performance
• Explore and Deploy Models

Module 18: Develop an AI App with the Azure AI Foundry SDK

• Introduction
• What is Azure AI Foundry SDK?
• Work with Connections
• Create a Chat Client
• Create an AI Chat App

Module 19: Get Started with Prompt Flow to Develop Language Model Apps in the Azure AI Foundry

• Introduction
• Understand LLM App Lifecycle
• Explore Core Components
• Explore Connections
• Explore Variants and Monitoring Options
• Get Started with Prompt Flow

Module 20: Build a RAG-Based Agent with Your Own Data Using Azure AI Foundry

• Introduction
• Ground Your Language Model
• Make Your Data Searchable
• Build an Agent with Prompt Flow
• Create a Custom Agent

Module 21: Fine-Tune a Language Model with Azure AI Foundry

• Introduction
• When to Fine-Tune
• Prepare Your Data
• Explore Fine-Tuning
• Fine-Tune a Model

Module 22: Evaluate the Performance of Generative AI Apps with Azure AI Foundry

• Introduction
• Assess Model Performance
• Manually Evaluate
• Assess App Performance
• Evaluate Your AI App

Module 23: Responsible Generative AI

• Introduction
• Plan a Solution
• Identify Harms
• Measure Harms
• Mitigate Harms
• Operate a Solution
• Explore Content Filters

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 Microsoft Azure Certification 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 Microsoft Azure Certification 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 DP-100 Azure Data Scientist Associate 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.

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