Artificial Intelligence on Amazon Web Services
Learn AI online with the Artificial Intelligence on Amazon Web Services course and lab. The lab is cloud-based, device-enabled, and can easily be integrated with an LMS. The AWS training course and lab cover some important topics in AI, such as image recognition, natural language processing, and speech recognition, and also provide a high-level understanding of AWS’s AI and machine learning services and platforms. The course will guide you through the process of setting up Python, the AWS SDK, and web development tools.
- Price: $279.99
- Delivery method: eLearning
- DIR Discount: 20%
Submit form to obtain discount
Test Prep
50+ Pre Assessment Questions |
50+ Post Assessment Questions |
Features
18+ LiveLab |
00+ Minutes
Why choose TOPTALENT?
- Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
- Access an expansive range of over 3,000 training courses with a strong focus on Information Technology, Business Applications, and Leadership Development.
- Have confidence in an exceptional 95% approval rating from our students, reflecting outstanding satisfaction with our course content, program support, and overall customer service.
- Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.
Outline
Lessons 1:
Preface
- Who this course is for
- What this course covers
- Conventions used
Lessons 2:
Introduction to Artificial Intelligence on Amazon Web Services
- What is AI?
- Overview of AWS AI offerings
- Getting familiar with the AWS CLI
- Using Python for AI applications
- First project with the AWS SDK
- Summary
- References
Lessons 3:
Anatomy of a Modern AI Application
- Understanding the success factors of artificial intelligence applications
- Understanding the architecture design principles for AI applications
- Understanding the architecture of modern AI applications
- Creation of custom AI capabilities
- Working with a hands-on AI application architecture
- Developing an AI application locally using AWS Chalice
- Developing a demo application web user interface
- Summary
- Further reading
Lessons 4:
Detecting and Translating Text with Amazon Rekognition and Translate
- Making the world smaller
- Understanding the architecture of Pictorial Translator
- Setting up the project structure
- Implementing services
- Implementing RESTful endpoints
- Implementing the web user interface
- Deploying Pictorial Translator to AWS
- Discussing project enhancement ideas
- Summary
- Further reading
Lessons 5:
Performing Speech-to-Text and Vice Versa with Amazon Transcribe and Polly
- Technologies from science fiction
- Understanding the architecture of Universal Translator
- Setting up the project structure
- Implementing services
- Implementing RESTful endpoints
- Implementing the Web User Interface
- Deploying the Universal Translator to AWS
- Discussing the project enhancement ideas
- Summary
- References
Lessons 6:
Extracting Information from Text with Amazon Comprehend
- Working with your Artificial Intelligence coworker
- Understanding the Contact Organizer architecture
- Setting up the project structure
- Implementing services
- Implementing RESTful endpoints
- Implementing the web user interface
- Deploying the Contact Organizer to AWS
- Discussing the project enhancement ideas
- Summary
- Further reading
Lessons 7:
Building a Voice Chatbot with Amazon Lex
- Understanding the friendly human-computer interface
- Contact assistant architecture
- Understanding the Amazon Lex development paradigm
- Setting up the contact assistant bot
- Integrating the contact assistant into applications
- Summary
- Further reading
Lessons 8:
Working with Amazon SageMaker
- Technical requirements
- Preprocessing big data through Spark EMR
- Conducting training in Amazon SageMaker
- Deploying the trained Object2Vec and running inference
- Running hyperparameter optimization (HPO)
- Understanding the SageMaker experimentation service
- Bring your own model – SageMaker, MXNet, and Gluon
- Bring your own container – R model
- Summary
- Further reading
Lessons 9:
Creating Machine Learning Inference Pipelines
- Technical requirements
- Understanding the architecture of the inference pipeline in SageMaker
- Creating features using Amazon Glue and SparkML
- Identifying topics by training NTM in SageMaker
- Running online versus batch inferences in SageMaker
- Summary
- Further reading
Lessons 10:
Discovering Topics in Text Collection
- Technical requirements
- Reviewing topic modeling techniques
- Understanding how the Neural Topic Model works
- Training NTM in SageMaker
- Deploying the trained NTM model and running the inference
- Summary
- Further reading
Lessons 11:
Classifying Images Using Amazon SageMaker
- Walking through convolutional neural and residual networks
- Classifying images through transfer learning in Amazon SageMaker
- Performing inference through Batch Transform
- Summary
- Further reading
Lessons 12:
Sales Forecasting with Deep Learning and Auto Regression
- Technical requirements
- Understanding traditional time series forecasting
- How the DeepAR model works
- Understanding model sales through DeepAR
- Predicting and evaluating sales
- Summary
- Further reading
Lessons 13:
Model Accuracy Degradation and Feedback Loops
- Monitoring models for degraded performance
- Developing a use case for evolving training data – ad-click conversion
- Creating a machine learning feedback loop
- Summary
- Further reading
Lessons 14:
What Is Next?
- Summarizing the concepts we learned in Part I
- Summarizing the concepts we learned in Part II
- Summarizing the concepts we learned in Part III
- Summarizing the concepts we learned in Part IV
- What’s next?
- Summary
