Machine Learning Deep Learning model deployment

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

Machine Learning Deep Learning model deployment
Machine Learning Deep Learning model deployment

Machine Learning Deep Learning model deployment udemy course

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 examples


Machine Learning Deep Learning model deployment Udemy

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

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.

Who this course is for:

Course Details:

  • 5,5 ч видео по запросу
  • 39 ресурсов для скачивания
  • Доступ через мобильные устройства и телевизор
  • Сертификат об окончании

Machine Learning Deep Learning model deployment udemy free download

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

Demo Link: https://www.udemy.com/course/machine-learning-deep-learning-model-deployment/