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Course Skill Level:

Foundational

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    AIAASEL21E09

Who should attend & recommended skills:

Software developers wanting to build and implement serverless AI applications

Who should attend & recommended skills

  • This course is geared for software developers who want to build and implement serverless AI applications, without bogging you down with a lot of theory. Instead, you’ll find easy-to-digest instruction and two complete hands-on serverless AI builds in this must-have guide!
  • Skill-level: Foundation-level AI as a Service for Intermediate skilled team members. This is not a basic class.
  • Software developing: Basic (1-2 years’ experience)
  • Programming Language (any): Intermediate (3-5 years’ experience
  • IP networking and HTTP protocol: Basic (1-2 years’ experience)
  • Control Systems Cloud-based Version such as GitHub: Basic (1-2 years) helpful
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • AI: Not required

About this course

AI as a Service teaches you how to quickly harness the power of serverless computing and cloud-based AI services. After an introduction to the basics of this dynamic technology duo, you will dive right into your first hands on serverless AI project: a system that can recognize images from arbitrary web pages. In it you will explore tools like Amazon Recognition for image analysis and techniques like deployment of cloud infrastructure, a crawler service, and a simple API. When you have mastered the concepts and skills in that fun and interesting project, you will move on to building a serverless to-do application that employs cloud-based AI tools like AWS Transcribe and Polly for speech-to-text and text-to-speech functionality and Lex for creating interactive chatbots. When you are finished with this essential course, you will have the skills to quickly build end-to-end serverless AI systems, making you indispensable as this rapidly emerging paradigm becomes the business standard!

Skills acquired & topics covered

  • Tools like Amazon Recognition for image analysis and techniques like deployment of cloud infrastructure, a crawler service, and a simple API
  • Building a serverless to-do application that employs cloud-based AI tools like AWS Transcribe and Polly for speech-to-text and text-to-speech functionality and Lex for creating interactive chatbots
  • Cloud AI from development to production
  • Applying cloud AI services to your existing platform
  • Understanding orchestration patterns for cloud AI systems
  • How to architect and build scalable, resilient data pipelines
  • Debugging and troubleshooting cloud AI services
  • Getting started immediately with serverless templates

Course breakdown / modules

  • Cloud Landscape
  • What is Serverless?
  • The Need for Speed
  • What is AI?
  • The Democratization of Compute Power and Artificial Intelligence
  • Canonical Serverless AI architecture
  • Realization on Amazon Web Services

  • Our First System
  • Architecture
  • Getting ready
  • Implementing the Asynchronous services
  • Implementing the Synchronous Services
  • Running the System
  • Removing the System

  • The To Do List
  • Architecture
  • Getting ready
  • Step 1 The Basic Application
  • Step 2 Securing with Cognito

  • Step 3 Adding a Speech to Text Interface
  • Step 4 Adding Text to Speech
  • Step 5 Adding a Conversational Chat Bot Interface
  • Removing the System

  • Integration Patterns for Serverless AI
  • Improving identity verification with Textract
  • An AI Enabled Data Processing Pipeline with Kinesis
  • Deploying the API
  • On the fly translation with Translate
  • Testing the Pipeline
  • Sentiment Analysis with Comprehend
  • Training a Custom Document Classifier
  • Using the Custom Classifier
  • Testing the Pipeline End to End
  • Removing the Pipeline
  • Benefits of Automation

  • Scenario: Finding Events and Speakers
  • Gathering Data from the Web
  • Introduction to Web Crawling
  • Implementing an Item Store
  • Creating a Frontier to Store and Manage URLs
  • Building the Fetcher to Retrieve and Parse Web Pages
  • Determining the Crawl Space in a Strategy Service
  • Orchestrating the Crawler with a Scheduler
  • Extracting Value from Large Data Sets with AI
  • Using AI to Extract Significant Information from Web Pages
  • Understanding Comprehend Entity Recognition APIs
  • Preparing Data for Information Extraction
  • Managing Throughput with Text Batches
  • Asynchronous Named Entity Abstraction
  • Checking Entity Recognition Progress
  • Deploying and Testing Batch Entity Recognition
  • Persisting Recognition Results
  • Tying it all Together
  • Wrapping Up