Flask is a web application framework used to develop web applications. Getting started with Flask is easy, and its power lies in its ability to scale up to complex applications. In this course, you’ll learn the most effective ways to use Flask in order to create your own web application.
You’ll begin with an introduction to Flask and quickly dive into defining and training your model. You’ll perform various actions on this model to train it and ensure that it is of the best quality for your application. You’ll also create and test API endpoints so you can predict the model’s behavior over time.
By the end of this course, you’ll have the confidence to deploy this application to the web and learn how to fix any errors that may arise during this process.
What You Will Learn
- Define and train your model
- Test the model availability and make predictions
- Create a Flask endpoint and perform quality checks
- Use image classification in your Flask application
- Predict images on the Flask application
- Deploy your machine learning model to the web
This course is for developers who are looking to learn Flask. A basic understanding of Python is preferred here as this will help you grasp the Flask concepts easier. You also need to have a functioning knowledge of machine learning and neural networks.
About The Author
Dan We: Daniel Weikert is a 33-year-old entrepreneur, data enthusiast, consultant, and trainer. He is a master’s degree holder certified in Power BI, Tableau, Alteryx (Core and Advanced), and KNIME (L1–L3).
He is currently working in the business intelligence field and helps companies and individuals obtain vital insights from their data to deliver long-term strategic growth and outpace their competitors.
He has a passion for learning and teaching. He is committed to supporting other people by offering them educational services and helping them accomplish their goals, gain expertise in their profession, or explore new careers.
Table of contents
Chapter 1 : Deploying Machine Learning Models
- Hello and Welcome to the Course
- Introduction to Flask
- Defining and Training the Model
- Evaluating and Saving the Trained Model
- Testing the Model Availability and Making a First Prediction
- Defining the Boundaries of Your Input Data
- Creating Our Flask Application
- Creating the Flask Endpoint and Ensuring Data Quality Checks
- Testing Our API Endpoint
- Introduction to Image Classification
- Flask Application - an Endpoint for Your Image Classification API
- The HTML Template Explained
- Predicting Images on Your Hosted Flask Web Application
- Congratulations and Final Words of Wisdom
- Chapter 2 : Deploying Your Machine Learning (ML) Models to the Web
- Title: Deploying Machine Learning Models with Flask for Beginners
- Release date: April 2021
- Publisher(s): Packt Publishing
- ISBN: 9781801077187
You might also like
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to …
Turning petabytes of data from millions of vehicles into open data with Geotab
Geotab is a world-leading asset-tracking company with millions of vehicles under service every day. Felipe Hoffa …
OSCON Open Source Software Superstream Series: Infrastructure
Watch Part 1, OSCON Open Source Software Superstream Series: Live Coding—Go, Rust, and Python. Watch Part …
Interactive Visualization with D3.js
Tackle the steep learning curve of D3.js by going hands-on to build your own amazing interactive …