DP-100T01 : Designing and implementing a data science solution on Azure

About Course

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.

Course Prerequisites

To ensure a successful learning experience in this course, learners should meet the following prerequisites:

Basic understanding of data science and machine learning concepts
Familiarity with data science processes such as data exploration, cleaning, feature engineering, model training, and evaluation
Experience with Python programming for data manipulation and model training
Understanding of basic statistical principles used in machine learning algorithms
Knowledge of cloud computing fundamentals, especially within the Microsoft Azure ecosystem
Prior experience with Azure services is beneficial but not required

These prerequisites ensure students can actively engage with the course content and practical labs.

Target Audience

This course is designed for:

Data scientists who want to design and operate machine learning solutions using Azure
Professionals with experience using frameworks like Scikit-Learn, PyTorch, and TensorFlow
Practitioners aiming to learn how to run scalable ML workloads in the cloud
Python developers interested in expanding their skillset into cloud-based ML model deployment and operations

The course is ideal for those who want to advance from local development to enterprise-grade ML operations using Microsoft’s Azure Machine Learning service.

Why Choose us

⭢ Live Online Training (Duration : 32 Hours)

⭢ Guaranteed to run classes

⭢ Experienced & certified trainers

⭢ Query Handling session

Enquire About This Course

Learning Objectives

Gain a comprehensive understanding of Azure Machine Learning to build, train, deploy, and monitor machine learning models in scalable, production-ready environments.

Create and configure an Azure Machine Learning workspace for developing and managing ML assets
Utilize Azure ML Studio and Python SDK for data science workflows and experimentation
Run Automated Machine Learning (AutoML) experiments to discover high-performing models quickly
Build and publish models using Azure ML Designer, a no-code interface for visual model development
Execute, track, and evaluate experiments to monitor performance and register models
Optimize models with hyperparameter tuning to improve accuracy and generalizability
Deploy models for real-time and batch inferencing using managed endpoints
Develop end-to-end machine learning pipelines for reproducibility and scalability
Apply model interpretability and fairness tools to support responsible AI practices
Monitor deployed models and data drift to ensure consistent performance and reliability

This course enables data professionals to confidently implement and manage AI solutions on Azure, bridging the gap between data science and production-grade machine learning.

Benefits of the course

Build and Deploy End-to-End Data Science Solutions on Azure:
Learn how to design, build, train, and deploy machine learning models using Azure Machine Learning and MLOps best practices.
Industry-Relevant Skills:
Gain expertise in preparing data, selecting appropriate models, performing feature engineering, training and tuning models, and managing the full ML lifecycle in the Azure environment.
Real-World Skills:
Understand how to use tools like Azure Machine Learning Studio, Python SDKs, and pipelines to automate workflows, ensure reproducibility, and scale ML experiments effectively.
Hands-On Experience:
Work through labs that guide you in configuring compute targets, running experiments, registering models, deploying endpoints, and monitoring model performance in production.
Career Boost:
Prepares you for the DP-100: Microsoft Certified: Azure Data Scientist Associate certification and roles such as Data Scientist, Machine Learning Engineer, or AI Specialist in cloud-focused organizations.