Deployment of Machine Learning Models
- Offered byUDEMY
Deployment of Machine Learning Models at UDEMY Overview
Duration | 10 hours |
Total fee | ₹649 |
Mode of learning | Online |
Credential | Certificate |
Deployment of Machine Learning Models at UDEMY Highlights
- Earn a certificate of completion from Udemy
- Learn from 36 downloadable resources & 33 articles
- Get full lifetime access of the course material
- Comes with 30 days money back guarantee
Deployment of Machine Learning Models at UDEMY Course details
- For Data scientists who want to deploy their first machine learning model
- For Data scientists who want to learn best practices model deployment
- For Software developers who want to transition into machine learning
- 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
- This course will show you how to take your machine learning models from the research environment to a fully integrated production environment
- Through the deployment of machine learning models, you can begin to take full advantage of the model you built
- 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
- 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
Deployment of Machine Learning Models at UDEMY Curriculum
Introduction
Introduction to the course
Course curriculum overview
Course requirements
Setting up your computer
Course Material
The code
Presentations
Download Dataset
Additional Resources for the required skills
How to approach the course
Overview of Model Deployment
Deployments of Machine Learning Models
Deployment of Machine Learning Pipelines
Research and Production Environment
Building Reproducible Machine Learning Pipelines
Challenges to Reproducibility
Streamlining Model Deployment with Open-Source
Machine Learning System Architecture
Machine Learning System Architecture and Why it Matters
Specific Challenges of Machine Learning Systems
Principles for Machine Learning Systems
Machine Learning System Architecture Approaches
Machine Learning System Component Breakdown
Research Environment - Developing a Machine Learning Model
Research Environment - Process Overview
Machine Learning Pipeline Overview
Feature Engineering - Variable Characteristics
Feature Engineering Techniques
Feature Selection
Training a Machine Learning Model
Data analysis demo - missing data
Data analysis demo - temporal variables
Data analysis demo - numerical variables
Data analysis demo - categorical variables
Feature engineering demo 1
Feature engineering demo 2
Feature selection demo
Model training demo
Scoring new data with our model
Python Open Source for Machine Learning
Open Source Libraries for Feature Engineering
Feature engineering with open source demo
Intro to Object Oriented Programing
Inheritance and the Scikit-learn API
Create Scikit-Learn compatible transformers
Create transformers that learn parameters
Feature engineering pipeline demo
Should feature selection be part of the pipeline?
Getting Ready for Deployment - Final Pipeline
Packaging The model for production
Introduction to Production Code
Code Overview
Understanding the Reasoning Behind the Prod Code Structure
Package Requirements Files
Working with tox [Do NOT skip - important]
Package Config
The Model Training Script & Pipeline
Introduction to Pytest [Optional]
Feature Engineering Code in the Package
Making Predictions with the Package
Building the Package
Tooling
Serving and deploying the model via Reset API
Running the API Locally
Understanding the Architecture of the API
Introduction to FastAPI
The API Endpoints
Using Schemas in our API
Logging in our Application
The Uvicorn Web Server
Introducing Heroku and Platform as a Service (PaaS)
Deploying our Application to Heroku
Understanding the Heroku-Specific Project Files
Deployment of Machine Learning Models at UDEMY Faculty details
Deployment of Machine Learning Models at UDEMY Entry Requirements
- Prerequisite: 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
- Not mentioned
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