4.1 out of 5
4.1
270 reviews on Udemy

Deploy Machine Learning & NLP Models with Dockers (DevOps)

Learn on to build Machine Learning, Deep Learning & NLP Models & Deploy them with Docker Containers (DevOps) (in Python)
Instructor:
UNP United Network of Professionals
1,601 students enrolled
English [Auto-generated]
How to synchronize the versatility of DevOps & Machine Learning
Master Docker , Docker Files, Docker Applications & Docker Containers (DevOps)
Flask Basics & Application Program Interface (API)
Build & Deploy a Random Forest Model
Build a Text based (Natural Language Processing : NLP ) CLUSTERING (KMeans) Model and expose it as an API
Build an API which will run a Deep Learning Model (Convolutional Neural Network : CNN) Model for Image Recognition & Classification

Machine Learning, as we know it is the new buzz word in the Industry today. This is practiced in every sector of business imaginable to provide data driven solutions to complex business problems.This poses the challenge of deploying the solution, built by the Machine Learning technique so that it can be used across the intended Business Unit and not operated in silos.

This is a extensive and well thought course created & designed by UNP’s elite team of Data Scientists from around the world to focus on the challenges that are being faced by Data Scientists and Computational Solution Architects across the industry  which is summarized the below  sentence :

“I HAVE THE MACHINE LEARNING MODEL , IT IS WORKING AS EXPECTED !! NOW WHAT ?????” 

This course will help you create a solid foundation of the essential topics of data science along with a solid foundation of deploying those created solutions through Docker containers  which eventually will expose your model as a service (API) which can be used by all who wish for it.

At the end of this course, you will be able to:

  • Learn about Docker , Docker Files, Docker Containers

  • Learn Flask Basics & Application Program Interface (API)

  • Build a Random Forest Model and deploy it.

  • Build a Natural Language Processing based Test Clustering Model (K-Means) and visualize it.

  • Build an API for Image Processing and Recognition with an Deep Learning Model under the hood (Convolutional Neural Network : CNN)

 This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications and most importantly deploying them.

Course Overview

1
Introduction
2
I have a model. Now what?
3
Skills Checklist
4
Learning Goals

Docker basics

1
Why docker?
2
What are docker containers?
3
Importance of docker containers in machine learning
4
Where devops meets data science
5
Summary

Flask basics

1
Introduction
2
Setting up a Flask Project
3
Simple Flask API to add two numbers
4
Taking user input with GET requests
5
POST request with Flask
6
Using Flask in the context of Machine Learning

Exposing a Random Forest Machine Learning service as an API

1
Introduction
2
API & Dataset Overview
3
Training the Random Forest model
4
Pickling the Random Forest model
5
Exposing the Random Forest model as a Flask API
6
Testing the API model
7
Providing file input to Flask API
8
Flasgger for autogenerating UI
9
Summary

Writing and building the Dockerfile

1
Introduction
2
Base Image & FROM command
3
COPY and EXPOSE commands
4
WORKDIR, RUN and CMD commands
5
Preparing the flask scripts for dockerizing
6
Writing the Dockerfile
7
Building the docker image
8
Running the Random Forest model on Docker

Building a production grade Docker application

1
Introduction
2
Overall Architecture
3
Configuring the WSGI file
4
Writing a production grade Dockerfile
5
Running and debugging a docker container in production
6
Docker Quiz 1 – Basic Concepts, Commands

Building NLP based Text Clustering application

1
Introduction
2
Stemming & Lemmatization for cleaner text
3
Converting unstructured to structured data
4
KMeans Clustering
5
Preparing the excel output
6
Making the output Downloadable
7
Finding top keywords for kmeans clusters
8
Final output with charts
9
Summary
10
Dockerizing the text clustering app

API for image recognition with deep learning

1
Introduction
2
Visualizing the input images
3
Preparing the input images
4
Building the deep learning model
5
Training and saving the trained deep learning model
6
Generating test images
7
Flask API wrapper for making predictions
8
Summary
9
Dockerizing the deep learning app
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Includes

4 hours on-demand video
Full lifetime access
Access on mobile and TV
Certificate of Completion