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 problems 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 to monitor the build we created.
This course has been designed to keep these areas under consideration. The combination of industry-standard build pipeline with some of the most common and important tools.
This course has been designed into Following sections:
1) Configure and a quick walkthrough of each of the tools and technologies we used in this course.
2) Building our NLP Machine Learning model and tune the hyperparameters.
3) Creating flask API and running the WebAPI in our Browser.
4) Creating the Docker file, build our image and running our ML Model in Docker container.
5) Configure GitLab and push your code in GitLab.
6) Configure Jenkins and write Jenkins’s file and run end-to-end Integration.
This course is perfect for you to have a taste of industry-standard Data Science and deploying in the local server. Hope you enjoy the course as I enjoyed making it.
- Beginner Machine Learning Enthusiast want to deploy their model.
- Beginner python developer curious about data science.
- Any one wants to learn Devops and role of DevOps in Data Science.