Semos Education Semos Education
  • Monday - Friday 9:00AM - 10:00PM
  • Call us now +389 2 3130 900
  • Keep in touch info@semosedu.com
EN / МК / RS
EN / МК / RS
Reserve a seat
Кошничка
Вкупно ден0 MKD
Кон кошничка
reserve a seat

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

Reserve a seat
  • Description
  • Content
  • Intendent To
  • Certificates

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning.

This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.

LEARNING PATH 1
Design a machine learning solution

  • Module 1: Design a data ingestion strategy for machine learning projects
  • Module 2: Design a machine learning model training solution
  • Module 3: Design a model deployment solution
  • Module 4: Design a machine learning operations solution

LEARNING PATH 2
Explore and configure the Azure Machine Learning workspace

  • Module 1: Explore Azure Machine Learning workspace resources and assets
  • Module 2: Explore developer tools for workspace interaction
  • Module 3: Make data available in Azure Machine Learning
  • Module 4: Work with compute targets in Azure Machine Learning
  • Module 5: Work with environments in Azure Machine Learning

LEARNING PATH 3
Work with data in Azure Machine Learning

  • Module 1: Make data available in Azure Machine Learning

LEARNING PATH 4
Work with compute in Azure Machine Learning

  • Module 1: Work with compute targets in Azure Machine Learning
  • Module 2: Work with environments in Azure Machine Learning

LEARNING PATH 5
Experiment with Azure Machine Learning

  • Module 1: Find the best classification model with Automated Machine Learning
  • Module 2: Track model training in Jupyter notebooks with MLflow

LEARNING PATH 6
Use notebooks for experimentation in Azure Machine Learning

  • Module 1: Track model training in Jupyter notebooks with MLflow

LEARNING PATH 7
Train models with scripts in Azure Machine Learning

  • Module 1: Run a training script as a command job in Azure Machine Learning
  • Module 2: Track model training with MLflow in jobs
  • Module 3: Perform hyperparameter tuning with Azure Machine Learning

LEARNING PATH 8
Optimize model training with Azure Machine Learning

  • Module 1: Run a training script as a command job in Azure Machine Learning
  • Module 2: Track model training with MLflow in jobs
  • Module 3: Perform hyperparameter tuning with Azure Machine Learning
  • Module 4: Run pipelines in Azure Machine Learning

LEARNING PATH 9
Manage and review models in Azure Machine Learning

  • Module 1: Register an MLflow model in Azure Machine Learning
  • Module 2: Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

LEARNING PATH 10
Deploy and consume models with Azure Machine Learning

  • Module 1: Deploy a model to a managed online endpoint
  • Module 2: Deploy a model to a batch endpoint

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.

Microsoft Certified: Azure Data Scientist Associate after successful completion of the Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Description

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning.

This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.

Content

LEARNING PATH 1
Design a machine learning solution

  • Module 1: Design a data ingestion strategy for machine learning projects
  • Module 2: Design a machine learning model training solution
  • Module 3: Design a model deployment solution
  • Module 4: Design a machine learning operations solution

LEARNING PATH 2
Explore and configure the Azure Machine Learning workspace

  • Module 1: Explore Azure Machine Learning workspace resources and assets
  • Module 2: Explore developer tools for workspace interaction
  • Module 3: Make data available in Azure Machine Learning
  • Module 4: Work with compute targets in Azure Machine Learning
  • Module 5: Work with environments in Azure Machine Learning

LEARNING PATH 3
Work with data in Azure Machine Learning

  • Module 1: Make data available in Azure Machine Learning

LEARNING PATH 4
Work with compute in Azure Machine Learning

  • Module 1: Work with compute targets in Azure Machine Learning
  • Module 2: Work with environments in Azure Machine Learning

LEARNING PATH 5
Experiment with Azure Machine Learning

  • Module 1: Find the best classification model with Automated Machine Learning
  • Module 2: Track model training in Jupyter notebooks with MLflow

LEARNING PATH 6
Use notebooks for experimentation in Azure Machine Learning

  • Module 1: Track model training in Jupyter notebooks with MLflow

LEARNING PATH 7
Train models with scripts in Azure Machine Learning

  • Module 1: Run a training script as a command job in Azure Machine Learning
  • Module 2: Track model training with MLflow in jobs
  • Module 3: Perform hyperparameter tuning with Azure Machine Learning

LEARNING PATH 8
Optimize model training with Azure Machine Learning

  • Module 1: Run a training script as a command job in Azure Machine Learning
  • Module 2: Track model training with MLflow in jobs
  • Module 3: Perform hyperparameter tuning with Azure Machine Learning
  • Module 4: Run pipelines in Azure Machine Learning

LEARNING PATH 9
Manage and review models in Azure Machine Learning

  • Module 1: Register an MLflow model in Azure Machine Learning
  • Module 2: Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

LEARNING PATH 10
Deploy and consume models with Azure Machine Learning

  • Module 1: Deploy a model to a managed online endpoint
  • Module 2: Deploy a model to a batch endpoint
Intendent To

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.

Certificates

Microsoft Certified: Azure Data Scientist Associate after successful completion of the Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Past experiences

What people say about us

  • - Marko Krstevski Microsoft .NET Academy

    Seeking to expand my knowledge, I decided to enroll in Semos Education, where I am gaining the necessary knowledge and experience.

  • - Teodor Markovski Student

    The desire to become a Cloud architect led me to Semos Education. I am thrilled by the positive experiences of former students and the way in which the instructors and Career Center take care of the students.

  • - Viktorija Georgieva Summer Mentorship Program for Python Develope

    The reputation of Semos Education for quality training and the opportunity to learn from experienced instructors played an additional significant role in my decision.

  • - Borche Peltekovski Accredited Academy for Graphic Design

    After completing my studies at Semos Education, I envision myself working in a technology company, such as Samsung, Apple, or a company of similar caliber.

  • - Natasha Dimovska The Official Data Science Institute

    Constant and effective learning are key aspects if you want to ensure a secure path to success. 'Don't give up easily and face challenges with even greater enthusiasm to achieve your goals' became my life motto, which I applied even in changing my career.

  • - Petar Vasilev The Official Data Science Institute

    The Data Science Academy at Semos Education provided me with significant theoretical and practical experience, opening many new doors and allowing me to make numerous new acquaintances along the way.

  • - Aleksandra Mandikj The Official Data Science Institute

    The best investment is the investment in oneself.

Meet the instructors

  • Dejan Vakanski  

    Microsoft Certified Trainer

    Data Consultant,

    Data Scientist @Semos Education

     

    22+ years of experience
  • Verica Manevska  

    Microsoft Certified Trainer

    Data analyst/Power BI Developer @iborn.net

     

    12+ years of experience
  • Simka Janevska  

    Microsoft Certified Trainer

    Data and Analytics Engineer @Qinshift

     

    1+ years of experience

Related Courses

  • AZ-204: Developing Solutions for Microsoft Azure
    20.10.2024 40 ден680 EUR Read More
  • DP-203: Data Engineering on Microsoft Azure
    Not Found 32 ден680 EUR Read More
  • DP-300: Administering Relational Databases on Microsoft Azure
    06.05.2024 32 ден680 EUR Read More
Course Application
DP-100: Designing and Implementing a Data Science solution on Azure

Your Details

Who is the payment made for?

Company Informations

After submitting the application, you will be contacted by our sales persons.

Request a callback
DP-100: Designing and Implementing a Data Science solution on Azure

Your Details

Еден чекор поблиску до посакуваната цел
Име на курс
Payment Method
Payment with credit cards Payment via invoice