Description
Course Deploying Machine Learning Models on GCP + AWS Lambda (Docker). Hello everyone, welcome to one of the most practical courses on Machine Learning and Deep Learning Model Generation. What is Model Deployment: Suppose you have a model after doing some rigorous training on your dataset. But now what to do with this model. You have tested your model with test data set which is good. You got very good accuracy from this model. But the real test is when live data reaches your model. So this course is about how to serialize your model and deploy it to the server. After attending this course:
- You will be able to deploy a model on a cloud server.
- You will be one step ahead in your machine learning journey.
- You can add another machine learning skill to your resume.
What will be covered in this course?
1. Introduction to the Course
In this section, I will teach you the design workflow of a Machine Learning system and the basic idea of deploying a model about the various cloud deployment options available.
2. Flask Crash Course
In this section you will learn about Flask crash course for those who are not familiar with Flask framework as we are going to implement the model with the help of this Flask web development framework available in Python.
3. Deployment Model with Flask
In this section you will learn how to serialize and deserialize Scikit-Learn models and how to deploy a proprietary Flask-based web service. To test the web API, we will use the Postman API testing tool and the Python requests module.
4. Serialize Tensorflow deep learning models
In this section you will learn how to serialize and serialize Keras models in the Fashion MNIST dataset.
5. Deploy to the Heroku Cloud
In this section, we will deploy the flower classification dataset model that we created in the previous section to the Heroku-Paas cloud solution.
6. Deploy to Google Cloud
In this section, you will learn how to deploy the model to various Google Cloud services such as Google Cloud Performance, Google Application Engine, and Google Managed Artificial Intelligence Cloud.
7. Deploy to Amazon AWS Lambda
In this section, you will learn how to deploy a flower classification model on an AWS Lambda function.
8. Deploying on Amazon AWS ECS with Docker Containers
In this section, we will see how to put an application inside a Docker container and deploy it on Amazon ECS (Elastic Container Service).
What you will learn in the Deploy Machine Learning Models on GCP + AWS Lambda (Docker) course
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Various options are available to deploy the model
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Run Tensorflow 2.0 models with Scikit-Learn, Flask web framework
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Google Cloud Performance, Model Deployment in App Engine
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Rendering models through Google’s artificial intelligence platform
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Run the Prediction API on Heroku Cloud
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Serialize and deserialize models via scikit-learn and tensorflow
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Deploying the model to Amazon AWS Lambda
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Install the flowering prediction model with Docker
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Docker Container Deployment in Amazon Container Services (ECS)
This course is suitable for those who
- Anyone who knows ML and wants to move towards model deployment
- Anyone who wants to know how to put a machine learning application into production
Course Specification Deploy Machine Learning Models on GCP + AWS Lambda (Docker)
- Publisher: Udemy
- Lecturer: ankit mistry
- Training level: Beginner to advanced
- Training duration: 4 hours 17 minutes
October 2021 highlights of the Deploy Machine Learning Models on GCP + AWS Lambda (Docker) course
Course Prerequisites Deploy Machine Learning Models on GCP + AWS Lambda (Docker)
- Basics of Python Programming
- Basic knowledge of web development
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