A to Z (NLP) Machine Learning Model building and Deployment
A to Z (NLP) Machine Learning Model building and Deployment
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 1.76 GB
Genre: eLearning Video | Duration: 27 lectures (3 hour, 50 mins) | Language: English
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 1.76 GB
Genre: eLearning Video | Duration: 27 lectures (3 hour, 50 mins) | Language: English
Python, Docker, Flask, GitLab, Jenkins tools and technology used for deploy model in your Local server. A complete Guide.
What you'll learn
Developing the NLP Model for Sentiment analysis and Machine learning deployment on local server using flask and docker.
Select the most efficient Machine Learning Model,Tune the hyper-parameters and selecting the best model using cross-validation technique
A quick discussion from the basic in nutshell about DevOps tools like docker, Git and GitLab, Jenkins etc.
A better understanding about software development and automation in real scenario and concept of end-to-end Integration.
Requirements
Basic programming in any language
Some exposure to Python (but not mandatory)
Description
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.
Who this course is for:
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.
A to Z (NLP) Machine Learning Model building and Deployment
