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

What Is AI/ML?

In computer science, artificial intelligence (AI), is intelligence demonstrated by machines. AI techniques enable computers to mimic human intelligence, including the use of logic & decision making. Machine learning algorithms are applied upon data both structured and unstructured to produce these artificially intelligent (AI) systems.

  • Machine learning is a subset of artificial intelligence where statistical methods are used to help a computer improve at a task with training and experience. Machine learning algorithms are used in a wide variety of applications, such as email filtering, computer vision, natural language processing, and chat-bot creation.
  • Deep learning is a subset of machine learning including multi-layer neural networks to train models.

Why AI on Azure?

  • Advantage(Unfair): Two CVs with same experience but one with Certification
  • Better Job Prospects & Higher Salary
  • 70% Agree, Certification improved Earning
  • 83% Find more Productive in Jobs
  • 84% seen better Job Prospects
  • 87% Enhances Professional Credibility
  • Stand Out by Displaying Digital Badge on LinkedIn

Introducing: [DP-100] Designing and Implementing a Data Science Solution on Azure Training

Become expert in [DP-100] Designing and Implementing A Data Science Solution On Azure by mastering these 12 critical core skills…


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Azure ML Workspace
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Manage Data Objects
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Experiment Compute Contexts
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Create Models
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Run Training Scripts
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Generate Metrics
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Automate Model Training Process
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Use Automated ML
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Use Hyperdrive
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Use Model Explainers
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Manage Models
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Deploy a Model as a Service

COURSE BREAKDOWN


  1. GETTING STARTED WITH AZURE MACHINE LEARNING
  2. AZURE MACHINE LEARNING TOOLS
  3. ACTIVITY GUIDE: CREATING AN AZURE MACHINE LEARNING WORKSPACE
  4. ACTIVITY GUIDE: WORKING WITH AZURE MACHINE LEARNING TOOLS

  1. TRAINING MODELS WITH DESIGNER
  2. PUBLISHING MODELS WITH DESIGNER
  3. ACTIVITY GUIDE: CREATING A TRAINING PIPELINE WITH THE AZURE ML DESIGNER
  4. ACTIVITY GUIDE: DEPLOYING A SERVICE WITH THE AZURE ML DESIGNER
  5. ACTIVITY GUIDE: RUN AN AUTOMATED MACHINE LEARNING EXPERIMENT
  6. ACTIVITY GUIDE: DEPLOY AND TEST THE PREDICTIVE SERVICE (AUTOMATED ML)

  1. INTRODUCTION TO EXPERIMENTS
  2. TRAINING AND REGISTERING MODELS
  3. ACTIVITY GUIDE: RUNNING EXPERIMENTS
  4. ACTIVITY GUIDE: TRAINING AND REGISTERING MODELS

  1. WORKING WITH DATASTORES
  2. WORKING WITH DATASETS
  3. ACTIVITY GUIDE: WORKING WITH DATASTORES
  4. ACTIVITY GUIDE: WORKING WITH DATASETS

  1. WORKING WITH ENVIRONMENTS
  2. WORKING WITH COMPUTE TARGETS
  3. ACTIVITY GUIDE: WORKING WITH ENVIRONMENTS
  4. ACTIVITY GUIDE: WORKING WITH COMPUTE TARGETS

  1. INTRODUCTION TO PIPELINES
  2. PUBLISHING AND RUNNING PIPELINES
  3. ACTIVITY GUIDE: CREATING A PIPELINE
  4. ACTIVITY GUIDE: PUBLISHING A PIPELINE

  1. REAL-TIME INFERENCING
  2. BATCH INFERENCING
  3. ACTIVITY GUIDE: CREATING A REAL-TIME INFERENCING SERVICE
  4. ACTIVITY GUIDE: CREATING A BATCH INFERENCING SERVICE

  1. HYPERPARAMETER TUNING
  2. AUTOMATED MACHINE LEARNING
  3. ACTIVITY GUIDE: TUNING HYPERPARAMETERS
  4. ACTIVITY GUIDE: USING AUTOMATED MACHINE LEARNING

  1. INTRODUCTION TO MODEL INTERPRETATION
  2. USING MODEL EXPLAINERS
  3. ACTIVITY GUIDE: EXPLORE DIFFERENTIAL PRIVACY & INTERPRETING MODELS
  4. ACTIVITY GUIDE: DETECT & MITIGATE UNFAIRNESS

  1. MONITORING MODELS WITH APPLICATION INSIGHTS
  2. MONITORING DATA DRIFT
  3. ACTIVITY GUIDE: MONITORING A MODEL WITH APPLICATION INSIGHTS/span>
  4. ACTIVITY GUIDE: MONITORING DATA DRIFT