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Machine Learning Deep Learning model deployment

Serving TensorFlow Keras PyTorch Python model Flask Serverless REST API MLOps MLflow Cloud GCP NLP tensorflow.js deploy
Instructor:
FutureX Skill
6,716 students enrolled
English [Auto]
Machine Learning Deep Learning Model Deployment techniques
Simple Model building with Scikit-Learn , TensorFlow and PyTorch
Deploying Machine Learning Models on cloud instances
TensorFlow Serving and extracting weights from PyTorch Models
Creating Serverless REST API for Machine Learning models
Deploying tf-idf and text classifier models for Twitter sentiment analysis
Deploying models using TensorFlow js and JavaScript
Machine Learning experiment and deployment using MLflow

In this course you will learn how to deploy Machine Learning Models using various techniques.

Course Structure:

  1. Creating a Model

  2. Saving a Model

  3. Exporting the Model to another environment

  4. Creating a REST API and using it locally

  5. Creating a Machine Learning REST API on a Cloud virtual server

  6. Creating a Serverless Machine Learning REST API using Cloud Functions

  7. Deploying TensorFlow and Keras models using TensorFlow Serving

  8. Deploying PyTorch Models

  9. Converting a PyTorch model to TensorFlow format using ONNX

  10. Creating REST API for Pytorch and TensorFlow Models

  11. Deploying tf-idf and text classifier models for Twitter sentiment analysis

  12. Deploying models using TensorFlow.js and JavaScript

  13. Tracking Model training experiments and deployment with MLfLow

Python basics and Machine Learning model building with Scikit-learn will be covered in this course. You will also learn how to build and deploy a Neural Network using TensorFlow Keras and PyTorch. Google Cloud (GCP) free trial account is required to try out some of the labs designed for cloud environment.

Introduction

1
Introduction
2
What is a Model?
3
How do we create a Model?
4
Types of Machine Learning

Building, evaluating and saving a Model

1
Creating a Spyder development environment
2
Python NumPy Pandas Matplotlib crash course
3
Building and evaluating a Classification Model
4
Saving the Model and the Scaler

Deploying the Model in other environments

1
Predicting locally with deserialized Pickle objects
2
Using the Model in Google Colab environment

Creating a REST API for the Machine Learning Model

1
Flask REST API Hello World
2
Creating a REST API for the Model
3
Signing up for a Google Cloud free trial
4
Hosting the Machine Learning REST API on the Cloud
5
Deleting the VM instance
6
Serverless Machine Learning API using Cloud Functions
7
Creating a REST API on Google Colab
8
Postman REST client

Deploying Deep Learning Models

1
Understanding Deep Learning Neural Network
2
Building and deploying PyTorch models
3
Creating a REST API for the PyTorch Model
4
Saving & loading TensorFlow Keras models
5
Understanding Docker containers
6
Creating a REST API using TensorFlow Model Server
7
Converting a PyTorch model to TensorFlow format using ONNX

Deploying NLP models for Twitter sentiment analysis

1
Converting text to numeric values using bag-of-words model
2
tf-idf model for converting text to numeric values
3
Creating and saving text classifier and tf-idf models
4
Creating a Twitter developer account
5
Deploying tf-idf and text classifier models for Twitter sentiment analysis
6
Creating a text classifier using PyTorch
7
Creating a REST API for the PyTorch NLP model
8
Twitter sentiment analysis with PyTorch REST API
9
Creating a text classifier using TensorFlow
10
Creating a REST API for TensforFlow models using Flask
11
Serving TensorFlow models serverless
12
Serving PyTorch models serverless

Deploying models on browser using JavaScript and TensorFlow.js

1
TensorFlow.js introduction
2
Installing Visual Studio Code and Live Server
3
JavaScript crash course (optional)
4
Adding TensforFlow.js to a web page
5
Basic tensor operations using TensorFlow.js
6
Deploying Keras model on a web page using TensorFlow.js

Model as a mathematical formula & Model as code

1
Deriving formula from a Linear Regression Model
2
Model as code

Models in Database

1
Storing and retrieving models from a database using Colab, Postgres and psycopg2
2
Creating a local model store with PostgreSQL

MLOps and MLflow

1
Machine Learning Operations (MLOps)
2
MLflow Introduction
3
Tracking Model training experiments with MLfLow
4
Why track ML experiments?
5
Running MLflow on Colab
6
Tracking PyTorch experiments with MLflow
7
Deploying Models with MLflow
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Machine Learning Deep Learning model deployment
Category:
Data Science
Teacher
Daniel W
Price:
$29.98 $24
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