If you are serious about machine learning and want to break into real -world ML engineering, learning MLOPs is a great job you can do. This is the one that turns experiences into reliable systems. You can train an excellent model, but without the right pipeline to deploy, monitor and update it, this model will not be useful in any real application. The MLOPS is how companies send a scale learning machine, and even if you are working on solo or small projects, knowing how to build the appropriate ML pipelines will save you tonnes of time and headaches. In addition, it is one of the most demanding skills in the industry right now.
We have just released a new video course on the Free Codecamp.com on the YouTube channel that teaches you how to work on a real, practical project to finally build an MLOPS pipeline. You will create a system that will analyze the feelings of YouTube comments in real time using the Chrome extension. This is not just another toy example. This is a complete production pipeline that covers everything from data collection to deployment, and uses real tools that are used in modern ML workflows: ML Flow, DVC, Dokar, AWS, Flask and more. This course has been taught by Bapi Ahmed, which takes every step in a clear, practical way, so you really understand what is happening.
Here is the course covered:
Introduction and Planning Plan – Understand the problem and design the entire pipeline architecture.
Collecting data – Learn how to scratch YouTube comments and how to produce data you will use to train the emotion model.
Data Pre -processing and EDA – Clean the data, discover samples, and prepare it for training.
Set up MLFlow Server on AWS – Use MLFlow to track experiences and manage your models.
Creating a baseline model – Start easy with a basic model to set the performance benchmark.
Improve the model – Experience with words bags, TFIDF, adjusting the size of the feature, handling class imbalance, and techniques such as hyper parameter tuning.
The stacking model – Use combining different models to combine different models.
Create a full ML pipeline using DVC – Break your code into modular components (data injury, pre -processing, model building, etc.) and version of everything using DVC.
Model diagnosis and registration with ML flu – Assess the performance and track the best models.
Deploy with flask and doer – Wrap your model in flask API, contain it with a doer, and prepare it prepare.
Make a chrome plugin – Extend a browser to communicate with your deployment model in real time.
CI/CD deployment on AWS – Make your deployment automatic so that refreshing is ease and reliable.
Finally, you will have a working, deployed MLOPS project that shows you understand the full ML Life Cycle. This course is best for everyone who already knows a little machine learning and wants to equalize his engineering skills.
You can FreeCodecamp.org View full course on YouTube channel (3 hours clock)