Machine Learning Operations (MLOps) Certification Training
Learn to design a machine learning system end-to-end. Build expertise in training, deploying, scaling and monitoring the performance of your machine learning model in production.
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
Design a machine learning system end-to-end starting from project scoping, data needs, modeling and deployment
Build pipelines for optimizing the model training process
Apply various machine learning and deep learning algorithms to solve your business problems
Use Spark MLlib for distributed model training
Deploy your machine learning models to production using CI/CD pipelines
Monitor and visualize the performance of your system
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
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.
Project 1. Deploying Single Container Static App with Docker & Travis CI on AWS Elastic Beanstalk
We will see how to create, dockerize and automate the hosting of a simple static website. In the process, we will learn how to push the code to GitHub, enable Travis to track changes in that repository, deploy on the AWS Elastic Beanstalk using S3 and IAM, host the app on a public domain bought from Google Domains, and configure it with the help of Amazon Route 53.
Project 2. Deploying Multi Container Flask App with Docker & Travis CI on AWS Elastic Beanstalk
We will deploy the multi-container Flask app (Nginx, uWSGI, Redis and PostgreSQL) on AWS Elastic Beanstalk
Project 3. Deploying Single Container Flask app with Docker, Travis CI, AWS RDS & AWS ElastiCache on AWS Elastic Beanstalk
We will deploy the Flask app on AWS Elastic Beanstalk using Docker, RDS(PostgreSQL),ElastiCache(Redis) and Travis CI.
Project 4. Testing Single Container Static App Locally on Minikube
We will understand what is Kubernetes and what is Minikube. As part of the hands-on, we will learn to set up Minikube with VirtualBox in Windows 10 Home system. We will learn various concepts of Kubernetes like pods, deployments, services, and ingress, and have a look at how we could create them in various ways using different commands. We will also deploy the single container static web application - which we have dockerized as part of the Docker, Travis, and AWS project series - and access it using Kubernetes ingress.
Project 5. Deploying Single Container Static App on Google Kubernetes Engine
We will learn how to deploy a static website on the Google Cloud Platform (GCP). It is very highly recommended to go through the project Testing App Locally on MiniKube, as the current project is dependent on that.
Project 6. Automating Deployment of Single Container Static App on Google Kubernetes Engine with Docker & Travis CI
We will see how to automate the process of deploying a static web app onto GKE with the help of a shell executable and Travis-CI.
Project 7. Deploying Multi-Container App on Minikube and GKE
We will understand how to deploy a multi-container application on Minikube and GKE. We will learn about Kubernetes Secrets and Kubernetes Persistent Volume Claim. By the end of this project, we will be able to appreciate the use of MiniKube before deploying an application onto production, like onto Google Kubernetes Engine.
Project 8. Churn Email Inbox with Python
Churn the mail activity from various individuals in an open source project development team.
Project 9. Predicting the median housing prices in California
In this project we will build a machine learning model to predict housing prices. We will learn various data manipulation, visualization and cleaning techniques using various libraries of Python like Pandas, Scikit-Learn and Matplotlib.
Project 10. Forecast Bike Rentals
Forecasting Bike Rentals with DecisionTreeRegressor, LinearRegression, RandomForestRegressor using scikit-learn. In this project, you will use Python and scikit-learn to build models using the above-mentioned algorithms, and apply them to forecast the bike rentals.
Project 11. Noise removal from the images
Build a model that takes a noisy image as an input and outputs the clean image.
Project 12. Deploy Machine Learning models to production
Build a model that takes a noisy image as an input and outputs the clean image.
Project 13. Predict which passengers survived in the Titanic shipwreck
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. In this project, you build a model to predict which passengers survived the tragedy.
Project 14. Build a spam classifier
Train the MNIST model, save the model to the file, load the model from the file in the flask app and predict the digit for the new images.
Project 15. Deploy Machine Learning models to Production using Flask
Learn how to deploy a machine learning model as a web application using the Flask framework.
Project 16. uild a Neural Network for Image Classification with TensorFlow
Learn how to build and train a dense neural network on the Fashion MNIST dataset and evaluate its performance with some test samples.
Project 17. Image Classification with Pre-trained InceptionV3 Network
This project aims to impart the knowledge of how to access the pre-trained models from TensorFlow 2, and appreciate its powerful classification capacity by making the model predict the class of an input image.
Apply Now
Prerequisites
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
Subscription | CloudxLab
Start Learning
58
29
MLOps Certification Training (lifetime course access) + 30 days Cloud Lab
“Sessions were great, pace was also very good. Each of the steps were explained well multiple times to ensure everyone understands the concepts. Thanks Sandeep!”
Nitin Nigam
“Thanks a lot,it was great course! I'm happy that you lead in this path to AI/ML/DL.I hope to continue to collaborate with you in future.”
Domenico Flioravanti
“Thank you so much Sandeep for all your great sessions. It will help in our career a lot. Your session is very much explanatory and understandable. Kudos to you.Thanks for all your hard work and time. Definitely, we will recommend all our friends and colleagues to attend your different course.Thanks a ton”
Hemanta Lenka
“I have been using CloudxLab for a while now, and they are amazing! The best part about using CloudxLab is that you do not need to wait for someone to tell you whether what you did was right or not, it is done automatically on the go. The training materials are of top notch quality. If you get stuck, they have a huge community of trainers and learners to help you out with all your doubts. They have a course structure for everyone, whether you are new to programming or are a seasoned programmer, they have something to offer you. And they are affordable too! I would recommend CloudxLab all the time.”
Rajtilak Bhattacharjee
“This course is suitable for everyone. Me being a product manager had not done hands-on coding since quite some time. Python was completely new to me. However, Sandeep Giri gave us a crash course to Python and then introduced us to Machine Learning. Also, the CloudxLab’s environment was very useful to just log in and start practising coding and playing with things learnt. A good mix of theory and practical exercises and specifically the sequence of starting straight away with a project and then going deeper was a very good way of teaching. I would recommend this course to all.”
Kamal Upadhyay
“It has been a wonderful learning experience with CXL. This is one of the courses that will probably stay with me for a significant amount of time. The platform provides a unique opportunity to try hands-on simultaneously with the coursework in an almost real-life coding example. Besides, learning to use algebra, tech system and Git is a good refresher for anyone planning to start or stay in technology. The course covers the depth and breadth of ML topics. I specifically like the MNIST example and the depth to which it goes in explaining each and every line of code. Would definitely recommend the instructor-led course.”
Pratik Sonthalia
“This is one of the best-designed course, very informative and well paced. The killer feature of machine/deep learning coursed from CloudxLab is the live session with access to labs for hands-on practices! With that, it becomes easy following any discourse, even if one misses the live sessions(Read that as me!). Sandeep(course instructor) has loads of patience and his way of explaining things are just remarkable. I might have better comments to add here, once I learn more! Great Jobs guys!”
Dhyan Prem
Senior Software Developer at Decision Resources Group
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.
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.
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.
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.
If you are not able to complete the mandatory requirements to earn the certificate before the deadline, you have two options
You can send a mail to reachus@cloudxlab.com requesting for an extension of the deadline which will be chargeable. Once approved, you can complete the remaining within the new deadline.
You can complete 100% of the course at your convenience and earn the certificate from CloudxLab. Click here to see a sample certificate
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 reachus@cloudxlab.com to request a refund within the stipulated time. We will be sorry to see you go though!
