This technical course enhances and supports one's current knowledge of machine learning and Python to manage data preparations and ingestions, model training, and launch machine learning solution inspection in Microsoft Azure.
Course Price : $1725 Per Participant
DP-100T01: Designing and Implementing a Data Science Solution on Azure Training teaches the techniques to work with machine learning solutions at the cloud scale with the help of Azure Machine Learning. This technical course enhances and supports one's current knowledge of machine learning and Python to manage data preparations and ingestions, model training, and launch machine learning solution inspection in Microsoft Azure.
Our enterprise training program is best for organizations and companies. It teaches automation, management, and monitoring of machine learning using Azure Machine Learning service. This training program is ideal for data scientists with experience in machine learning frameworks and Python, who want to develop and work with machine learning solutions in the cloud. It is also very useful for individuals preparing for the Microsoft Certified: Azure Data Scientist Associate certification exam.
This training is designed based on the objectives of the course variant DP-100T01-A.
Courses
Years of Experience
Global Learners
Doing Data Science on Azure
Doing Data Science with Azure Machine Learning service
Automating Machine Learning with Azure Machine Learning service
Managing and Monitoring Machine Learning Models with the Azure Machine Learning serviceData scientists who know Python and existing understanding of machine learning frameworks such as TensorFlow, PyTorch, and Scikit-Learn who want to create and manage machine learning solutions in the cloud are the primary audiences for this training.
Microsoft Learning PartnerMicrotek Learning is a Microsoft Certified Partner for Learning Solutions. This class uses official Microsoft courseware and will be delivered by a Microsoft Certified Trainer (MCT).
Dec 13, 2023
9:00 am - 5:00 pm EST
online
In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.
Lesson
Lab: Create an Azure Machine Learning Workspace
After completing this module, you will be able to
This module introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.
Lesson
Lab: Use Automated Machine Learning
Lab: Use Azure Machine Learning Designer
After completing this module, you will be able to
In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.
Lesson
Lab: Run Experiments
Lab: Train Models
After completing this module, you will be able to
Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
Lesson
Lab: Work with Data
After completing this module, you will be able to
One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.
Lesson
Lab: Work with Compute
After completing this module, you will be able to
Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module.
Lesson
Lab: Create a Pipeline
After completing this module, you will be able to
Create pipelines to automate machine learning workflows
Publish and run pipeline services
Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.
Lesson
Lab: Create a Real-time Inferencing Service
Lab: Create a Batch Inferencing Service
After completing this module, you will be able to
By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.
Lesson
Lab: Tune Hyperparameters
Lab: Use Automated Machine Learning from the SDK
After completing this module, you will be able to
Data scientists have a duty to ensure they analyze data and train machine learning models responsibly; respecting individual privacy, mitigating bias, and ensuring transparency. This module explores some considerations and techniques for applying responsible machine learning principles.
Lesson
Lab: Explore Differential provacy
Lab: Interpret Models
Lab: Detect and Mitigate Unfairness
After completing this module, you will be able to
After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.
Lesson
Lab: Monitor a Model with Application Insights
Lab: Monitor Data Drift
After completing this module, you will be able to
Microsoft Certified: Azure Data Scientist Associate Certification is a role-based certification for those who apply their skills of machine learning (ML) and data science to execute and run machine learning workloads on Azure, in specific, utilizing Azure ML (Machine learning) Service. This certification at Microtek Learning entails preparing and forming a suitable working environment for data science workloads on Azure, performing data experiments, and deploying ML models into production.
This course mainly covering the very latest concepts like Python Fundamentals, Introduction to Data Scientist, Data cleaning and collection, Visualizing data, Array Computations, Control flow and functions, and Web Scrapping. The DP-100 exam (Designing & Implementing a Data Science Solution on Azure) must be passed to obtain the certification.
Step 1: Review the skills and knowledge required to certify.
Step 2: Recommended training for certification:
Step 3: Take this exam and get certified.
Please fill the below details, and our consultant will help you in the enrollment process.
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