Machine Learning Deep Learning Model Deployment

[LeeAndro]

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz
Language: English | Size: 1.62 GB | Duration: 4h 1m

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

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 sennt analysis

Deploying models using TensorFlow js and javascript

Machine Learning expent 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

Course Structure:

Creating a Model

Saving a Model

Exporting the Model to another environment

Creating a REST API and using it locally

Creating a Machine Learning REST API on a Cloud virtual server

Creating a Serverless Machine Learning REST API using Cloud Functions

Deploying TensorFlow and Keras models using TensorFlow Serving

Deploying PyTorch Models

Converting a PyTorch model to TensorFlow format using ONNX

Creating REST API for Pytorch and TensorFlow Models

Deploying tf-idf and text classifier models for Twitter sennt analysis

Deploying models using TensorFlow.js and javascript

Tracking Model training expents 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.

Who this course is for:

Machine Learning bners



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