Course Overview
- Introduction
- I have a model. Now what?
- Skills Checklist
- Learning Goals
Docker basics
- Why docker?
- What are docker containers?
- Importance of docker containers in machine learning
- Where devops meets data science
- Summary
Flask basics
- Introduction
- Setting up a Flask Project
- Simple Flask API to add two numbers
- Taking user input with GET requests
- POST request with Flask
- Using Flask in the context of Machine Learning
Exposing a Random Forest Machine Learning service as an API
- Introduction
- API & Dataset Overview
- Training the Random Forest model
- Pickling the Random Forest model
- Exposing the Random Forest model as a Flask API
- Testing the API model
- Providing file input to Flask API
- Flasgger for autogenerating UI
- Summary
Writing and building the Dockerfile
- Introduction
- Base Image & FROM command
- COPY and EXPOSE commands
- WORKDIR, RUN and CMD commands
- Preparing the flask scripts for dockerizing
- Writing the Dockerfile
- Building the docker image
- Running the Random Forest model on Docker
Building a production grade Docker application
- Introduction
- Overall Architecture
- Configuring the WSGI file
- Writing a production grade Dockerfile
- Running and debugging a docker container in production
- Docker Quiz 1 – Basic Concepts, Commands
Building NLP based Text Clustering application
- Introduction
- Stemming & Lemmatization for cleaner text
- Converting unstructured to structured data
- KMeans Clustering
- Preparing the excel output
- Making the output Downloadable
- Finding top keywords for kmeans clusters
- Final output with charts
- Summary
- Dockerizing the text clustering app
API for image recognition with deep learning
- Introduction
- Visualizing the input images
- Preparing the input images
- Building the deep learning model
- Training and saving the trained deep learning model
- Generating test images
- Flask API wrapper for making predictions
- Summary
- Dockerizing the deep learning app
