Course Description
Learn how to manage machine learning solutions at the cloud scale using Azure Machine Learning. In this training, you will learn how to use your existing Python and machine learning skills to manage data ingestion, model training, model deployment, and monitoring of machine learning solutions in Microsoft Azure.
This course is intended for data scientists who want to develop and manage machine learning solutions in the Cloud and who already have some familiarity with Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow.
Prerequisites
Successful Azure Data Scientists begin this position with a basic understanding of cloud computing concepts and experience with various machine learning and general data science tools and techniques.
Specifically:
- Making cloud resources in Microsoft Azure.
- Using Python to explore and visualizing data.
- Training and validating machine learning models using frameworks like Scikit-Learn, PyTorch, and TensorFlow.
- Working with containers, gain these prerequisite skills, and take before enrolling in the course, take the following free online training:
- Explore Microsoft cloud concepts.
- Create machine learning models.
Azure container management Please finish Microsoft Azure AI Fundamentals before continuing if you are entirely new to data science and machine learning.
Content Outline
- Provision of an Azure Machine Learning workspace.
- Use tools and interfaces to work with Azure Machine Learning.
- Run coding-based experiments in a workspace for Azure machine learning.
- Use Azure Machine Learning's automated machine learning techniques.
- Learn how to use Azure Machine Learning's automatic machine learning user interface.
- Train and publish a classification model with Azure Machine Learning designer
- Azure Machine Learning can be used to train a machine learning model.
- Run a model training script as part of an Azure Machine Learning experiment using ScriptRunConfig.
- Make reusable, parameterized training scripts.
- Register trained models.
- Create and use data stores in an Azure Machine Learning workspace.
- Create and use datasets in an Azure Machine Learning workspace.
- Work with environments
- Work with compute targets
- Create Pipeline steps
- Pass data between steps
- Publish and run a pipeline
- Schedule a pipeline
- Deploy a model as a real-time inferencing service.
- Consume a real-time inferencing service.
- Troubleshoot service deployment
Learn how to create, publish, and use batch inference pipelines with Azure Machine Learning.
Learn to use Azure Machine Learning hyperparameter tuning experiments to optimize model performance.
- Use Azure Machine Learning's automated machine learning capabilities to determine the best-performing algorithm for your data.
- Use automated machine learning to preprocess data for training.
- Run an automated machine learning experiment.
- Articulate the problem of data privacy
- Describe how differential privacy works
- Configure parameters for differential privacy
- Perform differentially private data analysis
Learn how to explain models by calculating and interpreting feature importance.
- How to assess the fairness of machine learning models.
- How to mitigate predictive disparity in a machine learning model.
Learn how to monitor data drift in Azure Machine Learning.
Learn how to use Azure Application Insights to monitor a deployed Azure Machine Learning model.
FAQs
Most recently, Microsoft announced new technology designed to accelerate machine learning algorithms to real-time, which is known as Project Brainwave, and it uses programmable processors known as FPGAs and use to run sophisticated and compute-hungry algorithms. Microsoft is also developing industry-specific AI applications.
Microsoft replaced this unsatisfactory handwork by humans with a piece of AI-enhanced technology called FastStart.
An AI Platform is a framework that is designed to function more efficiently and intelligently than traditional frameworks. An AI Platform can also provide Data Governance, ensuring the use of best practices by a team of AI scientists and ML engineers.
Azure is 4-12% cheaper than AWS and offers some different properties, making it better than AWS. PaaS Capabilities mainly include both Azure and AWS are similar in providing PaaS capabilities for virtual networking, storage, and machines.
To attend the training session, you should have operational Desktops or Laptops with the required specification and a good internet connection to access the labs.
We would always recommend you attend the live session to practice & clarify the doubts instantly and get more value from your investment. However, if, due to some contingency, you have to skip the class, Radiant Techlearning will help you with the recorded session of that particular day. However, those recorded sessions are not meant only for personal consumption and NOT for distribution or any commercial use.
Radiant Techlearning has a data center containing a Virtual Training environment for participant hand-on-practice.
Participants can easily access these labs over Cloud with the help of a remote desktop connection.
Radiant virtual labs allow you to learn from anywhere and in any time zone.
The learners will be enthralled as we engage them the real-world and industry Oriented projects during the training program. These projects will improve your skills and knowledge and give you a better experience. These real-time projects will help you greatly in future tasks and assignments.