Descriptions
Deployment of Machine Learning Models, This course will show you how to take your machine learning models from the research environment to a fully integrated production environment. Deployment of machine learning models, or simply, putting models into production, means making your models available to other systems within the organization or the web, so that they can receive data and return their predictions. Through the deployment of machine learning models, you can begin to take full advantage of the model you built.
We’ll take you step-by-step through engaging video tutorials and teach you everything you need to know to start creating a model in the research environment, and then transform the Jupyter notebooks into production code, package the code and deploy to an API, and add continuous integration and continuous delivery. We will discuss the concept of reproducibility, why it matters, and how to maximize reproducibility during deployment, through versioning, code repositories and the use of docker. And we will also discuss the tools and platforms available to deploy machine learning models.
What you’ll learn
- Build machine learning model APIs and deploy models into the cloud
- Send and receive requests from deployed machine learning models
- Design testable, version controlled and reproducible production code for model deployment
- Create continuous and automated integrations to deploy your models
- Understand the optimal machine learning architecture
- Understand the different resources available to productionise your models
- Identify and mitigate the challenges of putting models in production
Who this course is for
- Data scientists who want to deploy their first machine learning model
- Data scientists who want to learn best practices model deployment
- Software developers who want to transition into machine learning
Specificatoin of Deployment of Machine Learning Models
- Publisher: Udemy
- Teacher: Soledad Galli , Christopher Samiullah
- Language: English
- Level: Intermediate
- Number of Course: 150
- Duration: 10 hours and 26 minutes
Content
Requirements
- A Python installation
- A Git installation
- Confidence in Python programming, including familiarity with Numpy, Pandas and Scikit-learn
- Familiarity with the use of IDEs, like Pycharm, Sublime, Spyder or similar
- Familiarity with writing Python scripts and running them from the command line interface
- Knowledge of basic git commands, including clone, fork, branch creation and branch checkout
- Knowledge of basic git commands, including git status, git add, git commit, git pull, git push
- Knowledge of basic CLI commands, including navigating folders and using Git and Python from the CLI
- Knowledge of Linear Regression and model evaluation metrics like the MSE and R2
Pictures
Sample Clip
Installation Guide
Extract the files and watch with your favorite player
Subtitle: English
Quality: 1080p
Changes:
Version 2023/2 compared to 2021/5 has increased the number of 11 lessons and the duration of 50 minutes. Also, the Quality of the course has increased from 720p to 1080p.
The 2025/9 version has a decrease of 1 lesson compared to the 2023/2 version.
Download Links
Password file(s): www.downloadly.ir
File size
5.23 GB