Description
Course A to Z (NLP) Building and Deploying Machine Learning Models Machine Learning The real value comes from deploying a machine learning solution in production and monitoring and the necessary optimization work that results. Most of the problems today are because I’ve built a machine learning model, but what next? How is it available to the end user? The answer is API, but how does it work? How can you find out where Docker is and how can you monitor the build we’ve created? This course is designed to keep an eye on these areas. A combination of the industry standard build pipeline with some of the most common and important tools. This course is divided into the following sections:
1) Configuration and brief review of all the tools and technologies we used in this course.
2) Building the NLP machine learning model and setting metaparameters.
3) Create Flask API and run WebAPI in our browser.
4) Build your image and run your ML model in a Docker container by creating a Dockerfile.
5) Configure GitLab and push your code to GitLab.
6) Configure Jenkins, write the Jenkinsfile and perform end-to-end integration.
This course is for you to get familiar with industry-standard data science and local server deployment. I hope you enjoy the course as much as I did.
What is included in the A to Z (NLP) of Building and Deploying Machine Learning Models course? You will learn
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Developing an NLP model for sentiment analysis and deploying machine learning on a local server using Flask and Docker.
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Select the most efficient machine learning model, adjust the above parameters and use the cross-validation technique to select the best model.
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A quick brief discussion of the basics about DevOps tools like Docker, Git and GitLab, Jenkins, etc.
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Better understanding of software development and automation in real-world scenario and the concept of end-to-end integration.
This course is suitable for people who
- Beginners interested in machine learning want to implement their own model.
- Beginner Python developer, curious about data science.
- Everyone wants to learn DevOps and the role of DevOps in data science.
Specifications of Building and Deploying Machine Learning Models from A to Z (NLP) course.
- Editor: Udemy
- Teacher: Mohammad Rijwan
- Training level: beginner to advanced
- Training duration: 4 hours and 56 minutes
- Number of courses:
Course headings
Course requirements
- Basic programming in any language
- Some experience with Python (but not essential)
Course pictures
Sample video of the course
installation Guide
After extracting, you can watch it with your favorite player.
English subtitles
Quality: 720p
Download link
free download software
Size
2.1GB