Machine Learning Deep Learning model deployment

Machine Learning Deep Learning model deployment

What you’ll learn

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

Requirements

Prior Machine Learning and Deep Learning background required but not a must have as we are covering Model building process also

Description

In this course you will learn how to deploy Machine Learning Deep Learning Models using various techniques.  This course takes you beyond model development and explains how the model can be consumed by different applications with hands-on examplesCourse Structure:Creating a Classification Model using Scikit-learnSaving the Model and the standard Scaler Exporting the Model to another environment – Local and Google ColabCreating a REST API using Python Flask and using it locallyCreating a Machine Learning REST API on a Cloud virtual serverCreating a Serverless Machine Learning REST API using Cloud FunctionsBuilding and Deploying TensorFlow and Keras models using TensorFlow ServingBuilding and Deploying  PyTorch ModelsConverting a PyTorch model to TensorFlow format using ONNXCreating REST API for Pytorch and TensorFlow ModelsDeploying tf-idf and text classifier models for Twitter sentiment analysisDeploying models using TensorFlow.js and JavaScriptTracking Model training experiments and deployment with MLFLowRunning MLFlow on Colab and DatabricksPython basics and Machine Learning model building with Scikit-learn will be covered in this course.  This course is designed for beginners with no prior experience in Machine Learning and Deep LearningYou 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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 What is a Model?

Lecture 3 How do we create a Model?

Lecture 4 Types of Machine Learning

Section 2: Building, evaluating and saving a Model

Lecture 5 Creating a Spyder development environment

Lecture 6 Python NumPy Pandas Matplotlib crash course

Lecture 7 Building and evaluating a Classification Model

Lecture 8 Saving the Model and the Scaler

Section 3: Deploying the Model in other environments

Lecture 9 Predicting locally with deserialized Pickle objects

Lecture 10 Using the Model in Google Colab environment

Section 4: Creating a REST API for the Machine Learning Model

Lecture 11 Flask REST API Hello World

Lecture 12 Creating a REST API for the Model

Lecture 13 Signing up for a Google Cloud free trial

Lecture 14 Hosting the Machine Learning REST API on the Cloud

Lecture 15 Deleting the VM instance

Lecture 16 Serverless Machine Learning API using Cloud Functions

Lecture 17 Creating a REST API on Google Colab

Lecture 18 Postman REST client

Section 5: Deploying Deep Learning Models

Lecture 19 Understanding Deep Learning Neural Network

Lecture 20 Building and deploying PyTorch models

Lecture 21 Creating a REST API for the PyTorch Model

Lecture 22 Saving & loading TensorFlow Keras models

Lecture 23 Understanding Docker containers

Lecture 24 Creating a REST API using TensorFlow Model Server

Lecture 25 Converting a PyTorch model to TensorFlow format using ONNX

Section 6: Deploying NLP models for Twitter sentiment analysis

Lecture 26 Converting text to numeric values using bag-of-words model

Lecture 27 tf-idf model for converting text to numeric values

Lecture 28 Creating and saving text classifier and tf-idf models

Lecture 29 Creating a Twitter developer account

Lecture 30 Deploying tf-idf and text classifier models for Twitter sentiment analysis

Lecture 31 Creating a text classifier using PyTorch

Lecture 32 Creating a REST API for the PyTorch NLP model

Lecture 33 Twitter sentiment analysis with PyTorch REST API

Lecture 34 Creating a text classifier using TensorFlow

Lecture 35 Creating a REST API for TensforFlow models using Flask

Lecture 36 Serving TensorFlow models serverless

Lecture 37 Serving PyTorch models serverless

Section 7: Deploying models on browser using JavaScript and TensorFlow.js

Lecture 38 TensorFlow.js introduction

Lecture 39 Installing Visual Studio Code and Live Server

Lecture 40 JavaScript crash course (optional)

Lecture 41 Adding TensforFlow.js to a web page

Lecture 42 Basic tensor operations using TensorFlow.js

Lecture 43 Deploying Keras model on a web page using TensorFlow.js

Section 8: Model as a mathematical formula & Model as code

Lecture 44 Deriving formula from a Linear Regression Model

Lecture 45 Model as code

Section 9: Models in Database

Lecture 46 Storing and retrieving models from a database using Colab, Postgres and psycopg2

Lecture 47 Creating a local model store with PostgreSQL

Section 10: MLOps and MLflow

Lecture 48 Machine Learning Operations (MLOps)

Lecture 49 MLflow Introduction

Lecture 50 MLflow tracking concepts

Lecture 51 Installing MLflow on Windows and Mac

Lecture 52 Tracking Model training experiments with MLfLow

Lecture 53 MLflow auto-logging

Lecture 54 MLflow REST APIs

Lecture 55 Running MLflow on Colab

Lecture 56 Running MLFlow on Databricks

Lecture 57 Tracking PyTorch experiments with MLflow

Lecture 58 Deploying Models with MLflow

Machine Learning beginners

Course Information:

Udemy | English | 5h 0m | 2.54 GB
Created by: FutureX Skills

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