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    Machine Learning Deep Learning Model Deployment

    Posted By: Sigha
    Machine Learning Deep Learning Model Deployment

    Machine Learning Deep Learning Model Deployment
    Last updated 1/2024
    MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English (US) | Size: 1.78 GB | Duration: 6h 18m

    Serving TensorFlow Keras PyTorch Python model Flask Serverless REST API MLOps MLflow NLP tensorflow.js deplo OpenAI GPT

    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 DatabricksAppendix - Generative AI - Miscellaneous Topics.OpenAI and the history of GPT modelsCreating an OpenAI account and invoking a text-to-speech model from Python codeInvoking OpenAI Chat Completion, Text Generation, Image Generation models from Python codeCreating a Chatbot with OpenAI API and ChatGPT Model using Python on Google ColabChatGPT, Large Language Models (LLM) and prompt engineeringPython 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


    Machine Learning Deep Learning Model Deployment


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