Udemy – Deployment of Machine Learning Models 2023-2

Udemy – Deployment of Machine Learning Models 2023-2 Downloadly IRSpace

Apr 17, 2025 - 11:15 Updated: Apr 20, 2025 - 21:16

0 0

Udemy – Deployment of Machine Learning Models 2023-2

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: 151
Duration: 10 hours and 26 minutes

Content

Deployment of Machine Learning Models

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

Deployment of Machine Learning Models

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.

Udemy – Deployment of Machine Learning Models 2023-2