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
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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.
We will deploy the multi-container Flask app (Nginx, uWSGI, Redis and PostgreSQL) on AWS Elastic Beanstalk
We will deploy the Flask app on AWS Elastic Beanstalk using Docker, RDS(PostgreSQL),ElastiCache(Redis) and Travis CI.
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.
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.
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.
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.
Churn the mail activity from various individuals in an open source project development team.
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.
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.
Build a model that takes a noisy image as an input and outputs the clean image.
Build a model that takes a noisy image as an input and outputs the clean image.
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.
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.
Learn how to deploy a machine learning model as a web application using the Flask framework.
Learn how to build and train a dense neural network on the Fashion MNIST dataset and evaluate its performance with some test samples.
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.
Complete at least 75% of the topics of the course along with projects - Analyze emails from Python, any 3 projects from Machine Learning and any 3 projects from DevOps. All the above requirements need to be met within 180 days from the course enrollment date to be eligible for the certificate.
Highlight your skills on your resume, LinkedIn, Facebook and Twitter. Tell your friends and colleagues about it.
“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!”
“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.”
“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”
“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.”
“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.”
“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.”
“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!”
Senior Software Developer at Decision Resources Group
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