This is comprehensive program with diverse range of topics
DevOps + MLOps Engineer Roadmap for Beginners
Following is the roadmap to learn DevOps and MLOps / AIOps Engineer skills for a total beginner. It includes technical skills (or tool skills) and soft (or core) skills.
7000+ happy learners
Requirements:
Instructed by Venkat G
No previous technical knowledge is required.
Students with some prior knowledge will improve their professional preparation
Learn Anytime, Anywhere
Course Duration
This course will make you financially literate
3 Months + 2 Months
DevOps + MLOps
5 Months
5 hours of study Every Day
Every Saturday Assignments
Highlights of DevOps+MLOps
AWS Core Services
CI/CD for Machine Learning
Git and GitHub
Working with Jenkins
Docker & K8s for ML
Monitoring and Debugging of ML
Python for MLOps
Continuous Monitoring with Prometheus
Packaging the ML Models
Deploy Apps with Docker Compose
MLflow
Continuous Monitoring of ML Apps
Build ML Apps
Post Production of ML Apps
This Course is designed for following enthusiasts:💡
- Data scientists seeking to extend their skills into the operational aspects of deploying and maintaining machine learning models.
- Software developers interested in mastering the tools and practices for integrating machine learning into real-world applications.
- DevOps professionals aiming to specialize in MLOps and enhance their proficiency in deploying and managing machine learning systems.
- Data engineers looking to broaden their skill set by incorporating MLOps practices into data pipelines.
- IT professionals wanting to understand the integration of machine learning models within operational workflows.
- Freshers / Entry Level Students and researchers in the fields of computer science, data science, and related disciplines looking to expand their knowledge in MLOps.
Once Course Completes, get
Certificate of Completion
Course curriculum
16 Module
•
16 Lessons
•
100+ h total length
Module 1
Week-0: About MLOps and It’s Importance
Introduction of DevOps Process
What is SDLC & Why it’s Important?
Types of SDLC
Waterfall Vs Agile Vs DevOps
DevOps Lifecycle & Tools
MLOps Fundamentals
Why DevOps alone is not Suitable for Machine Learning?Module 2
Week-1 and 2: AWS Cloud Essential Services
Topics
Introduction of Cloud Computing
Introduction of AWS
AWS IAM Service
AWS Computing Resources
AWS Database
AWS CLI
Project-1 – Static Website Hosting
Module 3
Week-3: Git / GitHub
Topics
Introduction to Version Control Systems
Getting Started with git.
Local Repo vs Remote Repo
Git Configurations
Getting Started with Local Repo
Concept of Working Directory - Staging Area - Commit
Git Workflow - Local Repo
Git Branching and Switching & Merging the Branches
Checking Out Commits
Working with Remote Repositories - GitHub / GitLab
Git Configurations
Cloning and Delete Branches
Module 4
Week 4: IAC – Terraform
IAC - Terraform Introduction
IAC - Terraform EC2 Instance Launching
IAC - Terraform Variables and Outputs
IAC - Terraform Provisioners
MODULE 5
Week 5 and 6: Docker & K8s
Topics
Introduction of Docker
Docker Deep Dive
Build Container Images - Manual
Build Container Images - Automation
Real time Use Cases
Run Containers in Docker Compose
Run Containers in Kubernetes
MODULE 6
Week 7 and 8: Working with CI/CD Pipeline - Jenkins
Topics
Introduction to Jenkins
How do we Use Jenkins
Installation of Jenkins on AWS EC2 Instances
Jenkins Free Style and Pipeline Jobs
CI/CD Pipeline using Git, Jenkins, and Maven
CI/CD Pipeline using Git, Jenkins, and Maven
MODULE 7
Week 9 and 10 – Project - DevOps CI and CD Process
Topics
SonarQube Integration with Jenkins
JFrog Artifactory Integration with Jenkins
Build Container Images - Manual
Docker Integration
Kubernetes Setup Using Terraform
Deploying Application on Kubernetes
Managing Application using Helm Charts
Monitoring - Prometheus and Grafana
Module 8
Week 11: GitOps Continuous Delivery tool for Kubernetes
Topics
Understand Argo CD core concepts.
Generate applications using Argo CD
How to integrate with CI systems
Best Practices and Recommendations
Module 9
Week 12 and 13: Python Programming
Topics
Introduction to Python Programming
Install Anaconda
Jupiter Lab
Variables in Python
Python Literals - Hands On
Operators in Python
Collection - Strings
Python String - Built-in Functions - Hands On
Data Structures
Casting in Python Programming
String Formatting
Conditional Statements in Python
Looping Statements
Functions
Exception Handling in Python
Modules in Python
Classes in Python
File Handling in Python
Working with Python Scripts
Module 10
Week 14: Packaging ML Models
Topics
Understanding the Modular Programming
Creating Folder Hierarchy for ML Project
Create Config Module
Data Handling Module
Data Preprocessing
Perform Training and Predictions
Packaging the ML Model & testing
Module 11
Week 15: MLFlow
Topics
Introduction to MLFlow
Logging Functions of MLFlow Tracking
Exploration of MLFlow
Machine Learning Experiment on MLFlow
MLFlow Project & Models
Register the Model & Serve the Model
Dockerize the ML Model
Packaging the ML Model in Docker Environment
Module 12
Week 16 and 17: Build ML Apps
Topics
What is API, REST and REST API
How REST API Works?
What is FastAPI?
Data Validation with Pydantic
Deploying the Machine Learning Model with FastAPI
Introduction to Streamlit
Hands On Working with Streamlit
Building the ML Model with Streamlit
What is Flask?
Hands On Learning of Flask Library
Build ML Model App with Flask
Module 13
Week 18: Using Jenkins in MLOps
Topics
How do we Use Jenkins in MLOps
Working with Jenkins Project
Introduction to CI CT CD Pipeline
Create CI CT CD Pipeline
Perform Test of Pipeline
Module 14
Week 19: Continuous Monitoring
Topics
Introduction to Continuous Monitoring
Use case on Continuous Monitoring
Introduction and Architecture Prometheus
Metric Types of Prometheus
Introduction and Installation of Grafana
Prometheus Configuration file
Exploring the Basic Querying Prometheus
Monitor the Infrastructure with Prometheus
Create Visualization with Grafana
Architecture of ML Application Monitoring
Hands On Monitoring of ML Application using Prometheus.
Introduction to ML Monitoring
Setting Up WhyLabs
Whylogs - Drift Detection, Input, Output, Bias Monitoring
WhyLogs - Constraints and Drift Reports
Module 15
Week 20: Postproduction of ML Models
Topics
Model Security
Adversarial Attack
Data Poisoning Attack
Distributed Denial of Service Attack (DDOS)
Data Privacy Attack
How to Mitigate Risk of Model Attacks
A/B Testing
Module 16
Bonus Courses
Linux and Shell Scripting for DevOps | MLOps | AI Engineers (Parallel Classes)
Linux Topics
Introducing Operating System
Launching Linux System using Vagrant and Virtual Box
Understand Linux Command Line
Getting Server Information using Linux Commands
File and Directory management
Using VI/VIM Editor
Linux cli utilities for downloading software
User Management
Package Management
Service Management
File Permissions
Network Management
Shell Scripting Topics
Basics of Programming Language
Shell Types and Variables
Decision Making & Operators
Shell Scripting Operators & Exit Statuses
Shell - Iterations with for loop
Shell - Iterations with while loop
Shell Scripting – Functions
Shell Script - Debugging Approaches
Scripting – Real Time Scenarios
AWS Solution Architect Associate Engineer (Parallel / Self Study Classes)
ChatGPT for Developers (Parallel / Self Study Classes)
What you will get?
Easy payment method
Lifetime Access of the Course
Learn Anytime, Anywhere
Guaranteed Best Pricing from the Market
WhatsApp Support
Not sure if you’re ready? Talk to us
Learning Path Modules:
Linux Admin For MLOps | AI Engineers
Shell Scripting for MLOps | AI Engineers
AWS for MLOps | AI Engineers
Git & GitHub for MLOps | AI Engineer
IAC - Terraform for MLOps | AI Engineers
Docker for MLOps | AI Engineer
Kubernetes for MLOps | AI Engineer
Jenkins for MLOps | AI Engineer
Project - DevOps CI and CD Process
GitOps - CD tool for Kubernetes
Master Python for MLOps | AI Engineers
MLOps - Packaging ML Models
MLOps - MLFlow
MLOps - Build ML Apps
Using Jenkins in MLOps
MLOps - Continuous Monitoring
Postproduction of ML Models
Meet the Instructor
I'm Venkat, and I've spent more than 17 years in the IT industry, gaining valuable insights and expertise.
With over 12 years of training experience, I've had the privilege of guiding and mentoring over 7000+ software engineers.
My mission is simple:
I'm dedicated to empowering IT freshers and professionals like you to secure high-paying jobs and salary hikes. Together, we'll leverage your professional skills and portfolio in DevOps, AWS Cloud, Kubernetes, Python, AI, and Machine Learning to build your wealth in the tech world.
200+
Hours of Videos
12+
Years of Mentor Experience
4.7/5
Average Rating
7000+
Trained Professional
A Thriving Community ⭐
We’ve helped 7,500+ students completely. Change their lives. Don’t just take our word for it
YOUTUBE
I was stuck in a dead-end job until I found Rise n Shine Technologies. Their real-world projects and certifications helped me level up my skills in a competitive job market. Now I'm a sought-after DevOps professional with a significant salary increase.
Ashok G
Rise n Shine Technologies transformed my career. With their guidance and training, I transitioned from a non-IT background to becoming a successful DevOps Engineer. I secured a job I had only dreamt of before. Their support and resources are unmatched.
Harsha D
The journey to my dream IT job seemed daunting, but Rise n Shine Technologies made it achievable. Their flexible training schedules allowed me to balance learning with my job. Their guidance and career support helped me secure a Cloud Architect role that I'm truly excited about..
Harsha D
YOUTUBE
I can't thank Rise n Shine Technologies enough for helping me land my dream job in AWS Cloud engineer. Their comprehensive training and personalized coaching prepared me for success. I'm now enjoying a rewarding career I'm passionate about. ♥️
Vijay K
Frequently Asked Questions
What is the mission of Rise n Shine Technologies?
Our mission is to empower IT freshers and professionals to build their wealth by leveraging their professional portfolio in DevOps, AWS Cloud, Kubernetes, Python, and Machine Learning to secure high-paying jobs and salary hikes.
How can Rise n Shine Technologies help me achieve my dream IT job?
We offer comprehensive training and resources in key IT areas, along with guidance and support to help you acquire the skills and knowledge needed to land your dream job.We offer comprehensive training and resources in key IT areas, along with guidance and support to help you acquire the skills and knowledge needed to land your dream job.
What makes your training programs unique?
Our programs are designed to bridge the gap between theory and practical skills. We provide hands-on experience through real-world projects, industry-relevant certifications, and personalized coaching to ensure your success.
Do I need prior IT experience to benefit from your programs?
No, our programs cater to both IT freshers and professionals. We have courses suitable for all skill levels, and our experienced trainers will guide you regardless of your starting point.
How long does it typically take to complete your training programs?
The duration varies depending on the specific program, but we offer flexible options to accommodate your schedule, whether you're looking for intensive training or a more gradual learning path.
Are there any job placement services or assistance after completing your programs?
Yes, we provide job placement support, including resume reviews, interview preparation, and access to job postings within our network of partner companies.
What industries or roles can I pursue after completing your training?
Our training prepares you for a wide range of roles, including DevOps Engineer, Cloud Architect, Python Developer, Machine Learning Engineer, and more, across various industries.
What is the cost of your training programs?
Our pricing varies depending on the specific program and the level of support you require. We offer flexible payment options, including installment plans, to make it accessible to all.
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