This blog post will cover some quick tips including FAQs on the topics that we covered in the Day 5 live session which will help to clear Certification [DP-100] & get a better-paid job.
In the previous week’s sessions, in Day 4 session we got an overview of Orchestrating Operations with Pipelines. And in this week’s Day 5 Live Session of the AI/ML & Azure Data Scientist Certification [DP-100] training program, we covered the concepts of Deploy and Consuming Models Training Optimal Models. We also covered hands-on Lab 13, Lab 14, Lab 15 and Lab 16 out of our 15+ extensive labs.
So, here are some of the Q & As asked during the Live session from Module 7: Deploy and Consuming Models
Training Optimal Models & Hands-on Lab on Inferencing Services and Hyperparameters of Microsoft Azure Data Scientist [DP-100].Deploy and Consuming Models
Hyperparameters
Q6: What are the methods of Hyperparameter Optimization?
A: The parameters, called hyperparameters, that define the performance of the machine learning algorithm (model), depends on the problem we are trying to solve. Thus, they need to be configured accordingly. This process of finding the best set of parameters is called hyperparameter optimization. For example, in support vector machines (SVM), the regularization constant, kernel coefficient needs to be optimized. The tuning of optimal hyperparameters can be done in a number of ways.
- Grid Search
- Random Search
- Bayesian optimization
- Gradient-based optimization
- Evolutionary optimization
Q7: What is hyperparameter tuning in machine learning?
A: Hyperparameter tuning is that the method of finding the configuration of hyperparameters that will lead to the most effective performance. the method is computationally costly and loads of manual work has got to be done. it’s accomplished by coaching the multiple models, victimisation an equivalent formula and coaching knowledge however totally different hyperparameter values. The ensuing model from every coaching run is then evaluated to work out the performance metric that you wish to optimize (for example, accuracy), and also the best-performing model is chosen.
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Quiz Time (Sample Exam Questions)!
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Related/References
- [DP-100] Design & Implement a Data Science Solution on Azure QnA Day 2 Live Session Review
- [DP-100] Design & Implement a Data Science Solution on Azure QnA Day 3 Live Session Review
- Azure Machine Learning Service Workflow: Overview for Beginners
- Azure ML Model
- Automated ML In Azure
- [AI-900] Microsoft Certified Azure AI Fundamentals Course: Everything you must know
- Azure Machine Learning Service Workflow: Overview for Beginners
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