This topic will guide you through the entire process of Microsoft Certified: Azure Data Scientist Associates Certification. It will start with where you can choose the courses and the topics that you will be covering in the exam. We also discuss the preparation strategy for this certification.
Azure Data Scientist Associate Certification
Microsoft certified: Azure data scientist associate is the certification offered by Microsoft Azure in machine learning. They recommend that you have another certification name before you take this certification. This certification is for designing and implementing data science solutions on Azure. It is the easiest or lowest level version of data science.
This exam tests your ability to set up an Azure Machine Learning workspace, run experiments, train, optimize, manage and deploy models, and use them.
Continue reading to find out which machine learning certification is best for you.
Career in Azure Data Scientist Associate
One reason for Azure’s incomprehensible popularity is its widespread acceptance. The demand for data science jobs in Azure is steadily growing. Data is the lifeblood for every business today, and the demand is likely to continue.
Data scientists are able to play an important role in business decisions. There are many career options for IT professionals in Microsoft Azure, particularly in data science. It is a good idea to keep an eye out for long-term opportunities in Azure data scientists’ activities.
Today, 2.5 Quintilian bytes worth of data are processed each day. Data scientists can organize and analyze this massive amount of data to help them run a profitable business. An organization might use data science to remind customers about their standard purchases. If you order shampoo every month, you might be able to get a contract that is the same each month. This will allow you to buy more.
Data scientists are not only responsible for business analysis, but also for building data products. Data science is, in fact, a combination of mathematics and computer science.
Jobs available
Analyst in Business Intelligence
Data Mining Engineer
Data Architect
Data Scientist etc.
Companies using Azure ML
365mc
Aggreko
Apollo
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This certification measures skills
1. Set up of an Azure Machine learning Workspace (30-35%). Creation of an Azure Machine Learning Workspace1. Creation of an Azure Machine Learning workspace2. configuration workspace settings3. Azure Machine Learning studiob allows you to manage workspaces. Azure Machine Learning workspace: Managing data objects Registering and maintaining datastores2. Creating and managing datasets Managing experiment compute contexts1. Creating a compute instance2. Choosing the right compute specifications for a training load Creating compute targets for experiments and training
2. Training models and running experiments (25-30%) Azure Machine Learning Designer1: Creating models Azure Machine Learning Designer allows you to create training pipelines. Ingesting data in a designer pipeline using designer modules to define pipeline data flow4. using custom code modules in designerb. Azure Machine Learning workspace1. Training scripts Azure Machine Learning SDK allows you to create and run experiments. Configuring run settings for a script3. Azure Machine Learning SDKc allows you to consume data from the experiments. An experiment run can be used to generate metrics. Logging metrics from an experiment run2. Retrieve and view experiment outputs3. Logs can be used to troubleshoot errors in experiment runs. Automating the model training process1. The SDK allows you to create a pipeline. Data transfer between steps of a pipeline Running a pipeline4. monitoring pipeline runs
3. Optimize and Manage Models (20-25%) To create optimal models, use AutomatedML1. using an automated ML interface in Azure Machine Learning studio2. An automated ML interface for Azure Machine Learning SDK3. Pre-processing options How to search algorithms Determining a primary metric6. Data for an Automated Machine Learning run7. Finding the best modelb. Hyperdrive is used to tune hyperparameters1. Selecting a sampling method2. Determining the search space Determining the primary metric Identifying early termination options5. Finding the model with optimal hyperparameter valuesc To interpret models, use model explainers Selecting a model interpreter2. generating feature importance datad. Managing models1. Registering a model who is trained2. monitoring model usage3. monitoring data drift
4. Implementing and Consuming Models (20-25%) Production compute targets1. Consider s
