Machine Learning Deep Learning Model Deployment

Machine Learning Deep Learning Model Deployment - Printable Version

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Machine Learning Deep Learning Model Deployment - AD-TEAM - 11-13-2024

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Machine Learning Deep Learning Model Deployment
Last updated 3/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.79 GB | Duration: 5h 36m

Serving TensorFlow Keras PyTorch Python model Flask Serverless REST API MLOps MLflow Cloud GCP NLP tensorflow.js deploy

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.

Who this course is for:
Machine Learning beginners

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