Designing and Implementing a Data Science Solution With Azure Online Training Course
What Is AI/ML?
Designing and implementing data science is a need of mothe dern world. The algorithms used in Data science whether structured or unstructured helps in creating AI systems. In this course, we are off to designing and implementing a data science solution with Microsoft Azure. Under professional supervision, ion students are taught to prepare design deployment, model training, and monitor solutions in Microsoft Azure. This course is not for those wdooes not have any background knowledge about data science and Azure but still starts with quite basic training and ends oan n expert level.
Why AI on Azure?
- Advantage (Unfair): Two CVs with the same experience but one with certification
- Better Job Prospects & Higher Salary
- 70% Agree, Certification improved Earning
- 83% Find more productive jobs.
- 84% saw better job prospects.
- 87% Enhances Professional Credibility
- Stand Out by Displaying Your Digital Badge on LinkedIn
Course Outline
MODULE 1: GETTING STARTED WITH AZURE MACHINE LEARNING
- GETTING STARTED WITH AZURE MACHINE LEARNING
- AZURE MACHINE LEARNING TOOLS
- ACTIVITY GUIDE: CREATING AN AZURE MACHINE LEARNING WORKSPACE
- ACTIVITY GUIDE: WORKING WITH AZURE MACHINE LEARNING TOOLS
MODULE 2: VISUAL TOOLS FOR MACHINE LEARNING
- TRAINING MODELS WITH DESIGNER
- PUBLISHING MODELS WITH DESIGNER
- ACTIVITY GUIDE: CREATING A TRAINING PIPELINE WITH THE AZURE ML DESIGNER
- ACTIVITY GUIDE: DEPLOYING A SERVICE WITH THE AZURE ML DESIGNER
- ACTIVITY GUIDE: RUN AN AUTOMATED MACHINE LEARNING EXPERIMENT
- ACTIVITY GUIDE: DEPLOY AND TEST THE PREDICTIVE SERVICE (AUTOMATED ML)
MODULE 3: RUNNING EXPERIMENTS AND TRAINING MODELS
- INTRODUCTION TO EXPERIMENTS
- TRAINING AND REGISTERING MODELS
- ACTIVITY GUIDE: RUNNING EXPERIMENTS
- ACTIVITY GUIDE: TRAINING AND REGISTERING MODELS
MODULE 4: WORKING WITH DATA
- WORKING WITH DATASTORES
- WORKING WITH DATASETS
- ACTIVITY GUIDE: WORKING WITH DATASTORES
- ACTIVITY GUIDE: WORKING WITH DATASETS
MODULE 5: WORKING WITH COMPUTE
- WORKING WITH ENVIRONMENTS
- WORKING WITH COMPUTE TARGETS
- ACTIVITY GUIDE: WORKING WITH ENVIRONMENTS
- ACTIVITY GUIDE: WORKING WITH COMPUTE TARGETS
MODULE 6: ORCHESTRATING OPERATIONS WITH PIPELINES
- INTRODUCTION TO PIPELINES
- PUBLISHING AND RUNNING PIPELINES
- ACTIVITY GUIDE: CREATING A PIPELINE
- ACTIVITY GUIDE: PUBLISHING A PIPELINE
MODULE 7: DEPLOYING AND CONSUMING MODELS
- REAL-TIME INFERENCING
- BATCH INFERENCING
- ACTIVITY GUIDE: CREATING A REAL-TIME INFERENCING SERVICE
- ACTIVITY GUIDE: CREATING A BATCH INFERENCING SERVICE
MODULE 8: TRAINING OPTIMAL MODELS
- HYPERPARAMETER TUNING
- AUTOMATED MACHINE LEARNING
- ACTIVITY GUIDE: TUNING HYPERPARAMETERS
- ACTIVITY GUIDE: USING AUTOMATED MACHINE LEARNING
MODULE 9: RESPONSIBLE MACHINE LEARNING
- INTRODUCTION TO MODEL INTERPRETATION
- USING MODEL EXPLAINERS
- ACTIVITY GUIDE: EXPLORE DIFFERENTIAL PRIVACY & INTERPRETING MODELS
- ACTIVITY GUIDE: DETECT & MITIGATE UNFAIRNESS
MODULE 10: MONITORING MODELS
- MONITORING MODELS WITH APPLICATION INSIGHTS
- MONITORING DATA DRIFT
- ACTIVITY GUIDE: MONITORING A MODEL WITH APPLICATION INSIGHTS
- ACTIVITY GUIDE: MONITORING DATA DRIFT
Flexible Class Options
- Corporate Group Training | Fast-Track
- Week End Classes For Professionals SAT | SUN
- Online Classes – Live Virtual Class (L.V.C), Online Training
