Machine Learning Deep Learning model deployment – Freecoursesite
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 – Freecoursesite
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Machine Learning Deep Learning Model Deployment techniques
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Simple Model building with Scikit-Learn, TensorFlow, and PyTorch
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Deploying Machine Learning Models on cloud instances
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TensorFlow Serving and extracting weights from PyTorch Models
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Creating Serverless REST API for Machine Learning models
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Deploying tf-idf and text classifier models for Twitter sentiment analysis
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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
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
- Learn 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 JavaScript
- 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 the cloud environment.
Who this course is for:
- Machine Learning beginners
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