MLOps with Azure or GCP Training Certification

Our MLOps course offers learners the flexibility to master machine learning operations on either Microsoft Azure or Google Cloud Platform (GCP).

This unified MLOps course allows students to select their cloud platform while providing uniform, high-quality instruction in machine learning operations. Whether you choose Azure or GCP, you will learn the fundamental skills required to execute MLOps in production environments.

24-hour intensive hands-on training in your chosen cloud platform (Azure/GCP)
Building & Executing a Multi-Step ML Pipeline on Azure Machine Learning Pipelines
GCP Console Navigation & AI Platform Setup
Build production-ready ML pipelines from scratch
The choice between Azure ML services or GCP’s Vertex AI platform
Get hands-on exercises with Labs, Quizzes and projects.

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Machine Learning Operations (MLOps) Training with Azure or GCP Course Overview

Our MLOps course combines concepts from theory with hands-on experience in your preferred cloud environment. You’ll learn how to create, deploy, and maintain machine learning models using Azure Machine Learning services or GCP’s Vertex AI while adhering to industry best practices.

This hands-on course walks you through the whole MLOps lifecycle, from creating cloud workspaces to delivering production-ready models. Whether you pick Azure ML Pipelines or Vertex AI Pipelines, you’ll learn how to log experiments, maintain models, and deploy securely using real-world scenarios. Whether you take Azure or GCP, the structure stays the same ensuring that you receive an equal high-quality learning experience.

MLOps with Azure or GCP Course Key Highlights

6 Labs with GCP, 5 Labs, 1 quiz and 1 capstone project with Azure
Hands-On: Challenge Exercise or Open Project Work
Be able to build, deploy and manage end-to-end ML pipelines on GCP/Azure using Vertex AI/Azure ML Pipelines.
Be proficient in tracking experiments, tuning hyperparameters, and managing models using GCP or Azure.
Be able to deploy, monitor, manage, and update ML models
Design and execute an end-to-end MLOps project on Vertex AI or Azure, solving a real-world problem.
Solidify understanding of MLOps concepts and gain further practical experience.

Curriculum

Introduction to MLOps and Cloud Fundamentals

Cloud Console Navigation (Azure Portal/GCP Console)
ML Workspace Setup (Azure ML/Vertex AI)

Managing ML Workflows with Cloud Pipelines

Building MultiStep ML Pipelines
Using Azure ML Pipelines or Vertex AI Pipelines

Experiment Tracking and Model Management

Experiment Tracking
Hyperparameter Tuning
Model Registry and Management

Model Deployment

Deployment Options (Azure Endpoints/Vertex AI Endpoints)
Realtime and Batch Predictions
Model Monitoring

Security and Scalability

Pipeline Security Implementation
Access Control (Azure RBAC/GCP IAM)
Scaling ML Workload

Capstone Project

Project Design
Implementation
Deployment and Monitoring

Review and Practice

Challenge Exercises
Open Project Work

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Who can attend?

This course is ideal for:

Data Scientists looking to learn MLOps
DevOps Engineers looking to move to ML operations
Software Engineers architecting ML systems
Cloud Engineers looking to manage ML deployments
IT Professionals working in companies that use ML

Data Scientists looking to learn MLOps
DevOps Engineers looking to move to ML operations
Software Engineers architecting ML systems
Cloud Engineers looking to manage ML deployments
IT Professionals working in companies that use ML

Data Scientists looking to learn MLOps
DevOps Engineers looking to move to ML operations
Software Engineers architecting ML systems
Cloud Engineers looking to manage ML deployments
IT Professionals working in companies that use ML

Training Schedule

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Enquiry for Corporate Training

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Exam & Certification

Exam Details

Name of Exam – AgileFever MLOPS with GCP or Azure Examination

Exam details are as follows:

Practical Exams, Lab Assessments, and Projects at the end of every completed module.
Capstone Project at the end of the 24-hour training.

Prerequisites

Anyone can register.

Benefits

Corporate Benefits

Standardize ML deployment processes across different teams
Reduce operational costs in ML implementations
Enhance time-to-market of ML solutions
Incorporate security and compliance in ML operations
Scalable ML workflows using cloud services

Individual Benefits

Gain proficiency in enterprise-level tools for MLOps
Increase career prospects in cloud ML engineering
Develop a portfolio of ML applications in the cloud
Acquire platform-specific skills in MLOps deployment
Master deployment practices that meet industry standards

Certification Process

Step 1: Register for the MLOPS with GCP or Azure with AgileFever

Step 2: Attend Agile Fever’s 24 hours of training

Step 3: Upon successful completion of the course, you will receive certification from AgileFever

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Testimonials

Great course with excellent content and knowledgeable instructors. The labs and case studies made it easy to apply what I learned. I passed my certification exam on the first attempt, and I owe it all to Agilefever!

I was looking for a structured way to learn MLOps with GCP, and Agilefever delivered exactly that. The course was well-organized, and the support team was amazing. Getting certified through this program boosted my confidence and career opportunities.

I had some basic ML knowledge, but this course helped me understand the full MLOps lifecycle with Azure. The real-world examples and step-by-step guidance were perfect. Thanks to Agilefever, I earned my certification and landed a new role as an MLOps Engineer!

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