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

reserve a seat

Description
Content
Target Audience
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.

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

Target Audience

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