Machine Learning Deep Learning model deployment

Machine Learning Deep Learning model deployment, available at $64.99, has an average rating of 4.45, with 64 lectures, based on 776 reviews, and has 11559 subscribers.

You will learn about 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 This course is ideal for individuals who are Machine Learning beginners It is particularly useful for Machine Learning beginners.

Enroll now: Machine Learning Deep Learning model deployment

Summary

Title: Machine Learning Deep Learning model deployment

Price: $64.99

Average Rating: 4.45

Number of Lectures: 64

Number of Published Lectures: 64

Number of Curriculum Items: 64

Number of Published Curriculum Objects: 64

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Machine Learning beginners

Target Audiences

Machine Learning beginners

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 examples

Course Structure:

1. Creating a Classification Model using Scikit-learn

2. Saving the Model and the standard Scaler

3. Exporting the Model to another environment – Local and Google Colab

4. Creating a REST API using Python Flask 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. Building and Deploying TensorFlow and Keras models using TensorFlow Serving

8. Building and 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

14. Running MLFlow on Colab and Databricks

Appendix – Generative AI – Miscellaneous Topics.

OpenAI and the history of GPT models

Creating an OpenAI account and invoking a text-to-speech model from Python code

Invoking OpenAI Chat Completion, Text Generation, Image Generation models from Python code

Creating a Chatbot with OpenAI API and ChatGPT Model using Python on Google Colab

ChatGPT, Large Language Models (LLM) and prompt engineering

Python 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 Learning

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.

Course Curriculum

Chapter 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

Chapter 2: Building, evaluating and saving a Model

Lecture 1: Creating a Spyder development environment

Lecture 2: Python NumPy Pandas Matplotlib crash course

Lecture 3: Building and evaluating a Classification Model

Lecture 4: Saving the Model and the Scaler

Chapter 3: Deploying the Model in other environments

Lecture 1: Predicting locally with deserialized Pickle objects

Lecture 2: Using the Model in Google Colab environment

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

Lecture 1: Flask REST API Hello World

Lecture 2: Creating a REST API for the Model

Lecture 3: Signing up for a Google Cloud free trial

Lecture 4: Hosting the Machine Learning REST API on the Cloud

Lecture 5: Deleting the VM instance

Lecture 6: Serverless Machine Learning API using Cloud Functions

Lecture 7: Creating a REST API on Google Colab

Lecture 8: Postman REST client

Chapter 5: Deploying Deep Learning Models

Lecture 1: Understanding Deep Learning Neural Network

Lecture 2: Building and deploying PyTorch models

Lecture 3: Creating a REST API for the PyTorch Model

Lecture 4: Saving & loading TensorFlow Keras models

Lecture 5: Understanding Docker containers

Lecture 6: Creating a REST API using TensorFlow Model Server

Lecture 7: Converting a PyTorch model to TensorFlow format using ONNX

Chapter 6: Deploying NLP models for Twitter sentiment analysis

Lecture 1: Converting text to numeric values using bag-of-words model

Lecture 2: tf-idf model for converting text to numeric values

Lecture 3: Creating and saving text classifier and tf-idf models

Lecture 4: Creating a Twitter developer account

Lecture 5: Deploying tf-idf and text classifier models for Twitter sentiment analysis

Lecture 6: Creating a text classifier using PyTorch

Lecture 7: Creating a REST API for the PyTorch NLP model

Lecture 8: Twitter sentiment analysis with PyTorch REST API

Lecture 9: Creating a text classifier using TensorFlow

Lecture 10: Creating a REST API for TensforFlow models using Flask

Lecture 11: Serving TensorFlow models serverless

Lecture 12: Serving PyTorch models serverless

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

Lecture 1: TensorFlow.js introduction

Lecture 2: Installing Visual Studio Code and Live Server

Lecture 3: JavaScript crash course (optional)

Lecture 4: Adding TensforFlow.js to a web page

Lecture 5: Basic tensor operations using TensorFlow.js

Lecture 6: Deploying Keras model on a web page using TensorFlow.js

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

Lecture 1: Deriving formula from a Linear Regression Model

Lecture 2: Model as code

Chapter 9: Models in Database

Lecture 1: Storing and retrieving models from a database using Colab, Postgres and psycopg2

Lecture 2: Creating a local model store with PostgreSQL

Chapter 10: MLOps and MLflow

Lecture 1: Machine Learning Operations (MLOps)

Lecture 2: MLflow Introduction

Lecture 3: MLflow tracking concepts

Lecture 4: Installing MLflow on Windows and Mac

Lecture 5: Tracking Model training experiments with MLfLow

Lecture 6: MLflow auto-logging

Lecture 7: MLflow REST APIs

Lecture 8: Running MLflow on Colab

Lecture 9: Running MLFlow on Databricks

Lecture 10: Tracking PyTorch experiments with MLflow

Lecture 11: Deploying Models with MLflow

Lecture 12: Congratulations and Thank You

Chapter 11: Appendix – Generative AI – Miscellaneous Topics.

Lecture 1: OpenAI and the history of GPT models

Lecture 2: Creating an OpenAI account and invoking a text-to-speech model from Python code

Lecture 3: Invoking OpenAI Text Generation, Image Generation models from Python code

Lecture 4: Creating a Chatbot with OpenAI API and ChatGPT Model using Python

Lecture 5: Unlocking the Power of ChatGPT with prompt engineering

Instructors

FutureX Skills
Empowering Data Engineers and Data Scientists

Rating Distribution

1 stars: 15 votes

2 stars: 28 votes

3 stars: 93 votes

4 stars: 289 votes

5 stars: 351 votes

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