Machine Learning Deep Learning model deployment, Serving TensorFlow Keras PyTorch Python model Flask Serverless REST API MLOps MLflow Cloud GCP NLP tensorflow.js deploy
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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
Description
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
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 cloud environment.
Who this course is for:
- Machine Learning beginners