Designing and Implementing a Data Science Solution on Azure
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In this course you will learn how to apply data science and machine learning to implement and run machine learning workloads on Azure. Also, planning and creating a suitable working environment for data science workloads on Azure. You will run data experiments and train predictive models. In addition, you will manage, optimize, and deploy machine learning models into production. You will get knowledge and experience in data science and using Azure Machine Learning, Deep Learning, Big Data. After finishing this course and passing your exam you will become a Data Scientist.
Watch this video presentation from the pilot phase of the program:
What will you learn?
After completing of the course, you'll 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.
- 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.
- Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.
- experiments that encapsulate data processing and model training code, and use them to train machine learning models.
- how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
- how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.
- how to define and run pipelines in the module.
- how to deploy models for real-time inferencing, and for batch inferencing.
- 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.
- some considerations and techniques for applying responsible machine learning principles.
Who is this course for?
This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.
Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.
- Creating cloud resources in Microsoft Azure.
- Using Python to explore and visualize data.
- Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
- Working with containers.
- Knowledge of Microsoft Azure and ability to navigate the Azure portal (or course Azure fundamentals AZ900)
- Knowledge of either C# or Python
- Familiarity with JSON and REST programming semantics
The schedule for this course will be announced when the selection process is over.
Marcin Szeliga Machine Learning & Data Science Trainer
Marcin is a Machine Learning and Data Science trainer with more than 10 years of experience in the field. He trained students in different universities and academies both in Poland and abroad.
He was a speaker at many European conferences (including pre-confs), such as: Machine Learning Prague, Data Science Summit, SQL Day, 4 Developers, SQL Nexus, SQL Saturday, Sphere.it, Global AI on Tour, Azure Day, Power Platform Saturday, Cloudyna, 4Developers, Global AI Night, Cancel, and Microsoft Technology Summit.Since 2010 till now, he trained over 1000 students. Subject taught:
- Microsoft Data Platform
- Machine Learning
- Business Intelligence
- Data science
- Artificial Intelligence
He is an author of more than 100 articles about Machine Learning, AI, and Microsoft Data Platform published by IT Professional (http://www.it-professional.pl/).Also, he is a member of associations like:
- Data Community Poland
- Polish Information Processing Society
Marcin Szeliga has delivered 3 groups for GITA ICT500 project and 33 of 51 students were certified successfully.
- Microsoft® Most Valuable Professional (MVP) for 7/1/2020 – 7/1/2021
- Microsoft® Certified Trainer
- Microsoft® Certified Solutions Associate
- Microsoft® Certified Solutions Expert
- Microsoft® Certified Azure Data Scientist Associate and etc.
After passing this exam, student will become Microsoft Certified: Azure Data Scientist Associate.
What Our Students Say
- Deadline: June 5, 2022
- Duration: 40 hours
- Language: English
- Price: Free of charge
- Detailed Course Outline
- Admission guide for CURRENT students
- Admission guide for NOT SELECTED applicants in Round1000
- Admission guide for completely NEW applicants
- Fill Application
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The trainings and the certification exams are completely free of charge for the participants.