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

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 jаvascript
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 Models using various techniques.

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 sentiment analysis

Deploying models using TensorFlow.js and jаvascript

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.

Who this course is for:
Machine Learning beginners

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