Designing and Implementing a Data Science Solution on Azure

Designing and Implementing a Data Science Solution on Azure

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Course Description
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.


Prerequisites:
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.

Specifically:
  • 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.
This course has the following prerequisites:
  • 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
If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.

The schedule for this course will be announced when the selection process is over.

Marcin Szeliga

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
  • Azure
  • 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.

Instructor's Certifications:
  • 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.

Certification Exam

Exam: DP-100: Designing and Implementing a Data Science Solution on Azure

After passing this exam, student will become Microsoft Certified: Azure Data Scientist Associate.

Certification Guide

What Our Students Say

„He had a fun manner of communicating different aspects of the course with the entire class, which I think in turn increased the general productivity of the training.“
Beka Modebadze
„Marcin is really great instructor!“
Natia Kukhilava
„Way beyond my expectations! He was simply amazing.“
Tsotne Tevzadze
„Thank you very much for this opportunity and wonderful training experience. I am sure that this program will have a significant impact on the development of IT professionals in Georgia.“
Giorgi Chakvetadze
„I am writing to inform you that I have successfully passed the DP-100 exam. I am very grateful to GITA & New Horizons BG for an amazing course. Special thanks to my Instructor Marcin for explaining course material with such care and attention.“
Tina Sikharulidze
„Great instructor, It's been a pleasure to learn with him.“
Guja Lomsadze

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The trainings and the certification exams are completely free of charge for the participants.

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