Practical Deep Learning on the Cloud | Packt

Practical Deep Learning on the Cloud | Packt
English | Size: 1.44 GB
Genre: eLearning

Training, exporting, and deploying deep learning models on the cloud (TensorFlow)
Using pre-trained models for your computer vision task
Working with cluster infrastructures on AWS (AWS Batch and Fargate)
Creating deep learning pipeline for training models using AWS Batch
Creating deep learning pipelines to deploy a model into production with AWS Lambda and AWS Step functions
Creating a data pipeline using AWS Fargate
Deep learning and machine learning applications are becoming the backbone of many businesses in both technological and traditional companies. Once organizations have achieved their first success in using ML/AI algorithms, the main issue they often face is how to automate and scale up their ML/AI workflows. This course will help you to design, develop, and train deep learning applications faster on the cloud without spending undue time and money.

This course will heavily utilize contemporary public cloud services such as AWS Lambda, Step functions, Batch and Fargate. Serverless infrastructures can process thousands of requests in parallel at scale. You will learn how to solve problems that ML and data engineers encounter when training many models in a cost-effective way and building data pipelines to enable high scalability. We walk through some techniques that involve using pre-trained convolutional neural network models to solve computer vision tasks. You’ll make a deep learning training pipeline; address issues such as multiple frameworks, parallel training, and cost optimization; and save time by importing a pre-trained convolutional neural network model and using it for your project.

By the end of the course, you’ll be able to build scalable and maintainable production-ready deep learning applications directly on the cloud.

The code files and related files are uploaded on GitHub at

Easily train and deploy scalable deep learning models on the cloud
Master AWS services while working with computer vision tasks and neural networks
Automate and scale your workflow with limited resources to gain maximum efficiency

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