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 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:
Creating a Classification Model using Scikit-learn
Saving the Model and the standard Scaler
Exporting the Model to another environment - Local and Google Colab
Creating a REST API using Python Flask 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
Building and Deploying TensorFlow and Keras models using TensorFlow Serving
Building and 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 JavaScript
Tracking Model training experiments and deployment with MLFLow
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/
