English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 27m | 1.45 GB
Build deep learning applications from scratch and deploy them on the cloud in a simple and cost-effective way
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.
Learn
- 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
Table of Contents
Introduction to Deep Learning in the Cloud
1 The Course overview
2 Introduction to Cloud Platform AWS
3 Deep Learning and Deep Learning Algorithms
4 Deep Learning on Cloud
5 Course Projects
Introduction to the Serverless Cluster AWS Services
6 What Is Serverless Cluster Processing
7 Introduction to Lambda, Step Functions, Batch, and Fargate
8 Creating an AWS Account
9 Where to Use Serverless Deep Learning Pipelines
10 Installation of Serverless Framework and Deploying AWS Step Functions
Training a Model Using TensorFlow
11 Working with TensorFlow
12 Training and Exporting a Model
13 Using Different Repositories of Models
14 Importing a Pretrained CNN Model
Train Deep Learning Models for Computer Vision Tasks
15 Deep Learning Solutions for Computer Vision Tasks
16 How to Use Pretrained CNN Models for Your Computer Vision Task
17 Using a Pretrained CNN Model for the New Dataset
18 Using AWS SageMaker to Train Deep Learning CNN Models
Image Data Pipeline on the Cloud
19 Introduction to Data Pipelines and How AWS Fargate Can Be Used to Implement One
20 Deploying AWS Step Functions with AWS Fargate Using Serverless Framework
21 Image Data Pipeline Project
Deep Learning Training Pipelines on the Cloud
22 Building Deep Learning Training Pipelines
23 Deploying AWS Step Functions with AWS Batch Using Serverless Framework
24 Project – Deep Learning Training Pipeline for the CNN
Deep Learning Inference Pipelines for Production
25 Introduction to Using AWS Lambda for Deep Learning Inference
26 Deploying AWS Step Functions with AWS Lambda Using Serverless Framework
27 Project – Deep Learning Inference Pipeline for CNN
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