Learn Ai
Azure Machine Learning
With Techpratham, you can learn how to design, build, and deploy ML models on the cloud using Azure Machine Learning. Get real-world experience with automated ML, pipelines, and MLOps integration.
5/5(4,890 Reviews)
Level
Advanced
Duration
8 weeks
What is Azure Machine Learning ?
Techpratham’s Azure Machine Learning Training equips learners with cloud-based machine learning expertise. You’ll explore Azure ML Studio, data preparation, model training, deployment, and monitoring in real-time. The course covers AutoML, pipelines, responsible AI practices, and integration with MLOps. By the end, you’ll be able to build scalable ML solutions and deploy enterprise-ready AI applications on Azure.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks
The candidate will get access to the presentation taken-up during the session.
Notes
Consolidated notes in word document.
Assignments
Assignments for every module will be covered.
MCQs
MCQs for every module covered in the session would be provided.
Video Recording
Daily Session would be recorded and shared to the candidate.
Dumps for Certification
We will provide dumps for the certification exam, which will help you to prepare for the exam.
Updated Content
We provide Generative AI Driven content By experts.
Projects
3 Live Projects will be provided for practice.
Azure Machine Learning Course Curriculum
.
Introduction to Azure Machine Learning
Section 1
Get started with Azure ML concepts and platform overview.
Azure ML Studio Overview
Workspaces & Datasets
Compute Resources
Role of AI in Cloud
Key Features & Benefits
.
Azure ML Workspace & Setup
Section 2
Set up and configure an Azure ML workspace, understand its components, and manage compute resources effectively.
Workspace Creation
Resource Management
Compute Instances
Environment Setup
.
Data Preparation & Management
Section 3
Learn how to import, clean, and preprocess datasets using Azure ML Studio and SDK for better model training.
Data Import
Data Cleaning
Feature Engineering
Data Stores
.
Model Development with Azure ML
Section 4
Build machine learning models using Azure ML Designer, AutoML, and Jupyter Notebooks.
Azure ML Designer
AutoML Experiments
SDK for Python
Jupyter Integration
.
Training & Hyperparameter Tuning
Section 5
Train ML models at scale using Azure ML pipelines and optimize them with hyperparameter tuning techniques.
Training Pipelines
Distributed Training
Hyperparameter Tuning
Checkpoints
.
Model Deployment & Serving
Section 6
Deploy machine learning models as REST APIs or endpoints for real-time and batch inference.
Deployment Options
Real-time Endpoints
Batch Scoring
REST APIs
.
Monitoring & Model Management
Section 7
Set up monitoring for deployed models, track performance, and manage model versions in production.
Model Registry
Performance Metrics
Model Drift Detection
Logging & Alerts
.
MLOps with Azure ML
Section 8
Implement CI/CD pipelines for machine learning workflows using Azure DevOps and GitHub Actions.
CI/CD Pipelines
Model Versioning
Automated Deployment
GitHub Integration
.
: Azure Cognitive Services Integration
Section 9
Combine Azure ML with Cognitive Services for advanced AI applications like vision, NLP, and speech recognition.
Computer Vision API
Text Analytics
Speech Recognition
Custom Services
.
Security, Governance & Compliance
Section 10
Ensure responsible AI deployment with proper data governance, security, and compliance on Azure ML.
Access Control
Data Encryption
Responsible AI
Compliance Standards
Key Projects
Azure Machine Learning
Customer Churn Prediction using AutoML
Train a lot of models on a customer dataset with Azure ML AutoML. Set up the best model as a real-time endpoint and see how well it works at predicting customer churn in a telecom company.
AI-Powered Image Classification on Azure
Use Azure ML to make a deep learning model that can sort images, like medical scans or product photos. As a scalable API service, use pipelines to preprocess, train, and deploy the model.
MLOps Workflow for Fraud Detection
Create a way to find fraud that keeps getting better over time. Use GitHub and Azure DevOps to set up CI/CD, keep an eye on how well your model works, and make sure you get alerts for problems right away.
Azure Machine Learning – Associate Training Program
Category
Associate
Exam Name:
Azure Machine Learning – Associate
Exam Code:
N.A.
Exam Duration:
N.A.
Exam Format:
N.A.
Passing Score:
N.A.
Our Testimonials
Frequently Asked Questions (FAQs)
Why should I learn Azure Machine Learning when I already know Python ML libraries?
You can build models on your own computer with Python ML libraries, but Azure ML is a cloud-based, scalable, and enterprise-ready environment for training, deploying, and managing ML solutions. It has automation, MLOps, and integration features that local setups can't match.
What is Azure Machine Learning?
Azure Machine Learning is a cloud-based service from Microsoft that enables building, training, and deploying machine learning models. It supports automation and MLOps integration for enterprises. TechPratham provides step-by-step training on Azure ML, covering both basics and advanced applications.
Why should I use Azure Machine Learning?
Azure ML provides scalability, automated ML, drag-and-drop features, and strong integration with Azure services. It simplifies model deployment to production. TechPratham helps learners master Azure ML tools through real-world, cloud-based projects.
Do I need coding experience for Azure Machine Learning?
Not necessarily, as Azure ML supports low-code/no-code workflows along with advanced Python-based options. This flexibility suits both beginners and experts. TechPratham designs training modules for all levels, ensuring hands-on exposure to both approaches.
What are the main use cases of Azure ML?
It is used for predictive analytics, image recognition, NLP, fraud detection, and recommendation systems. Enterprises leverage it for AI-driven decision-making. TechPratham provides case-study-based learning to cover these use cases.
How does Azure ML support MLOps?
Azure ML integrates with pipelines, CI/CD, and monitoring tools to manage the full ML lifecycle. It ensures reproducibility and scalability in deployments. TechPratham includes MLOps training within its Azure ML curriculum to build end-to-end skills.
Can Azure Machine Learning integrate with other tools?
Yes, it integrates with Power BI, GitHub, Databricks, and Jupyter Notebooks. This makes it versatile for data science workflows. TechPratham teaches learners how to combine Azure ML with these tools in practical projects.
How secure is Azure Machine Learning?
Azure ML follows enterprise-grade security with role-based access, encryption, and compliance standards. It ensures safe handling of sensitive data. TechPratham guides learners in setting up secure environments for ML projects.
What programming languages does Azure ML support?
Azure ML primarily supports Python and R, with SDKs for additional integrations. It also allows REST API access for custom workflows. TechPratham ensures learners practice multi-language integration in labs.
What are the career opportunities with Azure ML?
Learning Azure ML opens roles like Data Scientist, ML Engineer, AI Consultant, and Cloud AI Specialist. Azure-certified professionals are in high demand globally. TechPratham provides career-focused training with projects and placement assistance.
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