Machine Learning Real value comes from actually deploying a machine learning solution into production and the necessary monitoring and optimization work that comes after it.
Most of the problem nowadays as I have made a machine-learning model but what next.
How it is available to the end user, the answer is through API, but how it works?
How you can understand where the Docker stands and how the to monitor the build we created.
This Course have been design to keep these areas under consideration. The combination of industry standard build pipeline with some of the most common and important tools.
At the end of this course, you will be able to:
Learn about Building NLP model
Tuning the hyper-parameters and selecting the best model using cross validation
Using Flask and API building
Use of Docker and writing Docker file
Understanding the concept of GitLab and end-to-end integration of Jenkins
Basic programming in any language
Some exposure to Python (but not mandatory)