PG Certificate Course in Data Science, AI & Machine Learning by IIT Roorkee. Apply Now & Get up to Rs. 75,000 OFF! Offer Ends in:

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6 Months

Course Duration




Lab Days



About the Course

Machine Learning Operations (MLOps) refers to the tools, techniques and practical experiences required to train your machine learning models and deploy and monitor them in production. After we have trained our machine learning model, the next big task is to deploy the model to production and scale it so that more users can use it. In this course, you will learn how to use various tools and methodologies to do all this effectively.

While knowing machine learning and deep learning concepts is essential, but for building a successful career in Artificial Intelligence, you need to have good experience with production engineering capabilities. This course deep-dives into machine learning and deep learning algorithms along with building expertise in DevOps technologies.

By the end of this program, you will be ready to

  1. Design a machine learning system end-to-end starting from project scoping, data needs, modeling and deployment
  2. Build pipelines for optimizing the model training process
  3. Apply various machine learning and deep learning algorithms to solve your business problems
  4. Use Spark MLlib for distributed model training
  5. Deploy your machine learning models to production using CI/CD pipelines
  6. Monitor and visualize the performance of your system
  7. Gain practical knowledge in TensorFlow, Keras, Linux, Git, Python, Docker, Kubernetes, Graffana, Prometheus and Jenkins

Program Highlights

  • Certificate of Completion by CloudxLab

  • Work on about 17+ projects to get hands-on experience

  • Timely Doubt Resolution

  • Best In Class Curriculum

  • Cloud Lab Access


What is the certificate like?

  • Why Cloudxlab?

    CloudxLab is a team of developers, engineers, and educators passionate about building innovative products to make learning fun, engaging, and for life. We are a highly motivated team who build fresh and lasting learning experiences for our users. Powered by our innovation processes, we provide a gamified environment where learning is fun and constructive. From creative design to intuitive apps we create a seamless learning experience for our users. We upskill engineers in deep tech - make them employable & future-ready.

Hands-on Learning

hands-on lab
  • Gamified Learning Platform

  • Auto-assessment Tests

  • No Installation Required

Course Creators

Instructor Sandeep Giri

Sandeep Giri

Founder at CloudxLab

Past: Amazon, InMobi, D.E.Shaw

Instructor Abhinav Singh

Abhinav Singh

Co-Founder at CloudxLab

Past: Byjus

Instructor Praveen

Praveen Pavithran

Co-Founder at Yatis

Past: YourCabs, Cypress Semiconductor


Hours of Online Training
Days of Lab Access

Foundation Courses

1. Programming Tools and Foundational Concepts
1. Linux for Data Science
2. Getting Started with Git
3. Python Foundations
4. Machine Learning Prerequisites(Including Numpy, Pandas and Linear Algebra)
5. Getting Started with SQL
6. Analytics and Data Sciences

Course on Machine Learning

1. Machine Learning Applications & Landscape
1. Introduction to Machine Learning
2. Machine Learning Application
3. Introduction to AI
4. Different types of Machine Learning - Supervised, Unsupervised
2. Building end-to-end Machine Learning Project
1. Machine Learning Projects Checklist
2. Get the data
3. Launch, monitor, and maintain the system
4. Explore the data to gain insights
5. Prepare the data for Machine Learning algorithms
6. Explore many different models and short-list the best ones
7. Fine-tune model
3. Classification
1. Training a Binary classification
2. Multiclass,Multilabel and Multioutput Classification
3. Performance Measures
4. Confusion Matrix
5. Precision and Recall
6. Precision/Recall Tradeoff
7. The ROC Curve
4. Machine Learning Algorithms
1. Underpinnings of Machine Learning
2. Design and Construct
3. Challenges in Machine Learning Project
5. Introduction to Artificial Neural Networks
1. From Biological to Artificial Neurons
2. Implementing MLPs using Keras with TensorFlow Backend
3. Fine-Tuning Neural Network Hyperparameters

Course on DevOps

1. Introduction to DevOps
1.1 What is DevOps ?
1.2 10,000 foot view
1.3 Why DevOps ?
1.4 Dev-Test-Deploy
1.5 DevOps Principles
1.6 DevOps Toolchain
1.7 Overview of DevOps Tools
1.8 Co-relation between Agile and DevOps
1.9 Categories of DevOps Tools
2. Getting Started with AWS
2.1 Account Registration
2.2 Regions and AZ
2.3 Instance types
2.4 Security Group
2.5 Launching EC2 Instance
2.6 Connecting to EC2 instance
3. Containers
3.1 Containers Concepts
3.2 Container Vs Virtual Machine
3.3 Installing docker on CentOS, Debian and Windows
3.4 Managing Container with Docker Commands
3.5 Building your own docker images
3.6 Docker Compose
3.7 Docker registry - Docker Hub
3.8 Networking inside single docker container
3.9 Lab - Running Python Web App in docker container
3.10 Lab - Create a docker image from git repo
3.11 Lab - Deploying flask app using docker-compose
3.12 Lab - Complex deployment using docker-compose
4. Docker Swarm
4.1 What is Docker Swarm?
4.2 Creating Swarm
4.3 Deploy Service on Swarm
4.4 Deploy Service on Swarm Service scaling
4.5 Applying rolling update
4.6 Managing Swarm
4.7 Draining node
4.8 Lab - Create your own swarm cluster
4.9 Lab - Install Docker Machine
4.10 Lab - Deploy Flask app as Highly available service
4.11 Lab - Apply Rolling update for flask app
4.12 Lab - Deploy Voting app in Docker Swarm
5. Automate Docker Swarm on AWS
5.1 Install AWSCLI
5.2 Configure AWSCLI
5.3 Create Swarm on AWS
5.4 Deploy service on Swarm
6. Kubernetes
6.1 Introduction to Kubernetes
6.2 Architecture
6.3 Kubernetes cluster installation
6.4 Raft Consensus Algorith
6.5 Networking in Kubernetes
6.6 Installing Minikube
6.7 Objects in Kubernetes - Pod, Deployment
6.8 Services - Service Discovery, Service Object, Headless Services, Service Types
6.9 Role based Access
6.10 Volumes - Persistent Volumes, Persistent Volume Claim, Storage Class
6.11 Config Map and Secrets
6.12 Ingress - Virtual Host, Types, Fanout, Virtual Host, Fanout Ingress configuration, Virtual Host Ingress configuration
6.13 Lab - Installing Minikube on EC2
6.14 Lab - Enable and access Dashboard Addon
6.15 Lab - Deploy flask webapp on Minikube
6.16 Lab - Deploy Nginx app on Minikube
6.17 Lab - Deploy application with host type volumes
6.18 Lab - Create Elastic File system on AWS
6.19 Lab - Deploy nginx using PersistentVolume from AWS EFS
6.20 Lab - Create AWS Storage class backed by EBS storage
6.21 Lab - Deploy Flask app as daemon set
6.22 Lab - Deploy Flask app with different labels
6.23 Lab - Run Kuard pod to view secret
6.24 Lab - Access Flask app without service
6.25 Lab - Access Flask app through service
6.26 Lab - Deploy and access Headless service
7. Continuous Integration using Jenkins
7.1 Introduction to Jenkins
7.2 Continuous Integration and Continuous Integration with Jenkins
7.3 Jenkins Architecture
7.4 Installing Jenkins on EC2
7.5 User management
7.6 Set up Jenkins Master and Slave
7.7 Setup CI-CD pipeline for sample project
7.8 Lab - Setup Role based access
7.9 Lab - Master/Slave Setup
7.10 Lab - Configure SCM in Jenkins
8. Continuous Monitoring with Prometheus and Graffana
8.1 Introduction to Prometheus
8.2 Prometheus installation
8.3 Introduction to Grafana
8.4 Grafana Installation
8.5 Integration of Prometheus and Grafana
8.6 Adding customised dashboard in Grafana
8.7 Introduction to node exporter
8.8 Integrating node exporter for monitoring
8.9 Monitoring docker and containers
8.10 Lab. - Scrape metric from Grafana
8.11 Lab - View Node exporter metric in Grafana
8.12 Lab - View Docker metric in Grafana
8.13 Lab - Import AWS EC2 dashboard in Grafana


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Basic knowledge of any programming language and Linux will help you in understanding the concepts faster. We will provide access to our self-paced courses on Python and Linux once you sign up for this course.

Note: In case of a coupon code, discounts will be applicable only on the first EMI

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Frequently Asked Questions

I have some more questions. Can I talk to someone?

Absolutely! Please contact us here

Will I get support?

Yes! Please feel free to ask your questions on CloudxLab forum and our community and team of experts will answer your questions. We believe forum will add better perspectives, ideas, and solutions to your questions.

Do I need to install any software before starting this course?

No, we will provide you with the access to our online lab and BootML so that you do not have to install anything on your local machine

What is the validity of course material?

We understand that you might need course material for a longer duration to make most out of your subscription. You will get lifetime access to the course material so that you can refer to the course material anytime.

What do I need to fulfill to get the CloudxLab certificate for the course?

You should complete 100% of the course along with all the given projects in order to be eligible for the certificate.

Kindly note that there is no deadline for CloudxLab courses.

Is it an online course?

It is a self-paced course. You will get access to videos, quizzes, hands-on assessments and projects. If you have any doubts during your learning journey, you can post it on the discussion forum. Our experts and community will assist you over there.

Is there any prerequisites for this course?

No, this course is for everyone. The complimentary access to CloudxLab courses will help you in learning the required foundations to make the most out of this certificate course.

What options do i have if i don't complete the course within the deadline

If you are not able to complete the mandatory requirements to earn the certificate before the deadline, you have two options

  1. You can send a mail to requesting for an extension of the deadline which will be chargeable. Once approved, you can complete the remaining within the new deadline.

  2. You can complete 100% of the course at your convenience and earn the certificate from CloudxLab. Click here to see a sample certificate

What is your refund policy?

If you are unhappy with the product for any reason, let us know within 7 days of purchasing or upgrading your account, and we'll cancel your account and issue a full refund. Please contact us at to request a refund within the stipulated time. We will be sorry to see you go though!