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

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Description
Content
Target Audience
Certificates

This course explores how professionals can design, build, and deploy machine learning solutions using Azure Machine Learning to accelerate AI adoption across industries. Participants will learn how to architect scalable ML systems by planning data ingestion, training models, deploying them to production, and managing operations. Through hands-on modules, learners will configure Azure ML workspaces, prepare data and compute resources, and experiment with models using tools like AutoML, Jupyter notebooks, and MLflow.

The course also introduces advanced capabilities such as hyperparameter tuning, pipeline orchestration, and Responsible AI dashboards to ensure fairness and transparency in model development. By the end, learners will be equipped to operationalise machine learning through managed endpoints and batch deployments, gaining the skills needed to streamline workflows and deliver impactful AI solutions. Successful participants will be prepared to earn the Azure Data Scientist Associate certification from Microsoft, validating their expertise in enterprise-grade machine learning.

Contents:

Designing Machine Learning Solutions to plan data ingestion, model training, deployment, and operational strategies for scalable ML systems.
Configuring Azure ML Workspaces to explore resources, developer tools, compute targets, environments, and data access for streamlined experimentation.
Working with Data and Compute to prepare datasets and manage compute infrastructure essential for training and deploying models in Azure ML.
Experimenting with Models using Automated ML, Jupyter notebooks, and MLflow to track performance and refine training workflows.
Training and Optimising Models with command jobs, hyperparameter tuning, and pipelines to enhance accuracy and efficiency.
Managing and Reviewing Models by registering MLflow models and applying Responsible AI dashboards to ensure transparency and fairness.
Deploying and Consuming Models through managed online and batch endpoints to operationalise machine learning solutions in real-world applications.

This course is intended for professionals and technical enthusiasts who want to design, build, and deploy machine learning solutions using Azure Machine Learning. It is suitable for individuals across roles—data scientists, engineers, and solution architects—who are looking to streamline ML workflows, optimise model performance, and operationalise AI in real-world applications. Learners will gain hands-on experience in configuring workspaces, managing data and compute resources, experimenting with models, and deploying them responsibly using tools like MLflow, AutoML, and Azure pipelines.

Azure Data Scientist Associate, issued by Microsoft upon successful completion of the required certification exam.

Description

Content

Contents:

Designing Machine Learning Solutions to plan data ingestion, model training, deployment, and operational strategies for scalable ML systems.
Configuring Azure ML Workspaces to explore resources, developer tools, compute targets, environments, and data access for streamlined experimentation.
Working with Data and Compute to prepare datasets and manage compute infrastructure essential for training and deploying models in Azure ML.
Experimenting with Models using Automated ML, Jupyter notebooks, and MLflow to track performance and refine training workflows.
Training and Optimising Models with command jobs, hyperparameter tuning, and pipelines to enhance accuracy and efficiency.
Managing and Reviewing Models by registering MLflow models and applying Responsible AI dashboards to ensure transparency and fairness.
Deploying and Consuming Models through managed online and batch endpoints to operationalise machine learning solutions in real-world applications.

Target Audience

Certificates

Azure Data Scientist Associate, issued by Microsoft upon successful completion of the required certification exam.

Our students for 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