Oreilly – AI as a Service, Video Edition 2020-9

AI as a Service Course, Video Edition. This course shows you how to use cloud-based AI services to automate business tasks. Using out-of-the-box tools like Amazon Rekognition and AWS Comprehend, you can build AI-driven applications like Chatbots and Text-to-Speech services without the need to build expensive custom software. This course helps you scale from small projects to large, data-driven applications. Key takeaways:

• Using AI in various fields such as customer service, data analysis, and financial reporting
• Leveraging ready-made cloud-based AI tools
• Build AI-driven applications without the need for data science expertise
• Using Serverless Computing for Rapid Application Development
• Learn to use Amazon Rekognition and AWS Comprehend services.

What you will learn:

• Using Cloud-Based AI Services on Existing Platforms
• Design and build scalable data pipelines
• Troubleshooting AI services
• Quick Start with Serverless Templates

Who is this course suitable for?

• This course is suitable for software developers who are familiar with the basics of Cloud.

AI as a Service, Video Edition Course Details

• Publisher: Oreilly
• Lecturer: Eóin Shanaghy , Peter Elger
• Training level: Beginner to advanced
• Training duration: 6 hours and 36 minutes

Course headings

• Part 1. First steps
• Chapter 1. A tale of two technologies
• Chapter 1. What is Serverless?
• Chapter 1. The need for speed
• Chapter 1. What is AI?
• Chapter 1. The democratization of computing power and artificial intelligence
• Chapter 1. Canonical AI as a Service architecture
• Chapter 1. Realization on Amazon Web Services
• Chapter 1. Summary
• Chapter 2. Building a serverless image recognition system, part 1
• Chapter 2. Architecture
• Chapter 2. Getting ready
• Chapter 2. Implementing the asynchronous services
• Chapter 2. Summary
• Chapter 3. Building a serverless image recognition system, part 2
• Chapter 3. Implementing the synchronous services
• Chapter 3. Running the system
• Chapter 3. Removing the system
• Chapter 3. Summary
• Part 2. Tools of the trade
• Chapter 4. Building and securing a web application the serverless way
• Chapter 4. Architecture
• Chapter 4. Getting ready
• Chapter 4. Step 1: The basic application
• Chapter 4. Step 2: Securing with Cognito
• Chapter 4. Summary
• Chapter 5. Adding AI interfaces to a web application
• Chapter 5. Step 4: Adding text-to-speech
• Chapter 5. Step 5: Adding a conversational chatbot interface
• Chapter 5. Removing the system
• Chapter 5. Summary
• Chapter 6. How to be effective with AI as a Service
• Chapter 6. Establishing a project structure
• Chapter 6. Continuous deployment
• Chapter 6. Observability and monitoring
• Chapter 6. Logs
• Chapter 6. Monitoring service and application metrics
• Chapter 6. Using traces to make sense of distributed applications
• Chapter 6. Summary
• Chapter 7. Applying AI to existing platforms
• Chapter 7. Improving identity verification with Textract
• Chapter 7. An AI-enabled data processing pipeline with Kinesis
• Chapter 7. On-the-fly translation with Translate
• Chapter 7. Testing the pipeline
• Chapter 7. Sentiment analysis with Comprehend
• Chapter 7. Training a custom document classifier
• Chapter 7. Using the custom classifier
• Chapter 7. Testing the pipeline end to end
• Chapter 7. Removing the pipeline
• Chapter 7. Benefits of automation
• Chapter 7. Summary
• Part 3. Bringing it all together
• Chapter 8. Gathering data at scale for real-world AI
• Chapter 8. Gathering data from the web
• Chapter 8. Introduction to web crawling
• Chapter 8. Implementing an item store
• Chapter 8. Creating a frontier to store and manage URLs
• Chapter 8. Building the fetcher to retrieve and parse web pages
• Chapter 8. Determining the crawl space in a service strategy
• Chapter 8. Orchestrating the crawler with a scheduler
• Chapter 8. Summary
• Chapter 9. Extracting value from large data sets with AI
• Chapter 9. Understanding Comprehend’s entity recognition APIs
• Chapter 9. Preparing data for information extraction
• Chapter 9. Managing throughput with text batches
• Chapter 9. Asynchronous named entity abstraction
• Chapter 9. Checking entity recognition progress
• Chapter 9. Deploying and testing batch entity recognition
• Chapter 9. Persisting recognition results
• Chapter 9. Tying it all together
• Chapter 9. Wrapping up
• Chapter 9. Summary
• Appendix A. AWS account setup and configuration
• Appendix A. Signing in
• Appendix A. Best practices
• Appendix A. AWS Command Line Interface
• Appendix B. Data requirements for AWS managed AI services
• Appendix C. Data sources for AI applications
• Appendix C. Software analytics and logs
• Appendix C. Human data gathering
• Appendix C. Device data
• Appendix D. Setting up a DNS domain and certificate
• Appendix D. Setting up a certificate
• Appendix E. Serverless Framework under the hood
• Appendix E. Cleanup

Course images

Sample course video

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