The Oxford Training Centre‘s AI with AWS course provides a complete learning experience that equips learners with the skills and knowledge required to harness the power of artificial intelligence (AI) using Amazon Web Services (AWS). This course focusses on key AI services, tools, and technologies accessible in the AWS ecosystem, giving students hands-on experience building and deploying AI models. It provides a foundation for professionals who want to specialise in AI, machine learning, and deep learning with AWS, as well as prepares them for the AWS AI and ML Certification tests.
Throughout the course, participants will be exposed to a variety of AWS AI services and tools, such as Amazon SageMaker, AWS Deep Learning, and AWS AI solutions, and will learn how to design AI models, develop intelligent apps, and integrate machine learning into cloud environments. This AI with AWS program is intended for professionals who want to master AWS AI principles, deep learning, machine learning development, and AI architecture. By the end of the course, participants will be able to successfully deploy AI models and apply machine learning techniques to real-world scenarios.
Objectives and Target Group
Objectives
The objectives of the course AI with AWS are to enable participants with:
1. Deep Knowledge about AWS AI Services
- Understand core AWS AI services, including AWS SageMaker, AWS Deep Learning, and other AI tools such as AWS Lambda, Amazon Lex, and AWS Polly.
2. Extensive Knowledge of the Basics of AWS AI
- Gain foundational knowledge in AI, machine learning, and deep learning principles within the AWS Cloud environment, empowering the next generation of professionals to create scalable AI solutions.
3. Practical Experience with AWS AI Tools
- Get hands-on experience with AWS AI tools and technologies, enabling participants to design, develop, and deploy AI models for various applications like chatbots, predictive analytics, and more.
4. Preparation for AWS AI and Machine Learning Certification
- Equip yourself with the skills and knowledge needed to excel in the AWS AI certification exams and build a strong foundation for AI professionals.
5. AI Deployment on AWS
- Learn how to deploy and scale AI models on the AWS Cloud effectively, ensuring high availability, reliability, and efficient performance for AI-powered applications.
6. AI Architecture and Solution Design
- Understand how to apply the principles of AI architecture on AWS, including the creation of machine learning pipelines and deploying AI solutions across different AWS environments.
Target Group
This AI with AWS course is ideal for:
- AI and Machine Learning Professionals: Individuals currently working in AI, machine learning, or data science roles who want to enhance their skills by learning how to leverage AWS AI services for building AI models and deploying machine learning solutions.
- Developers and Engineers: Software developers, cloud engineers, and IT professionals looking to expand their expertise in AI and machine learning deployment using AWS tools and services, such as AWS SageMaker and AWS Lambda.
- Data Scientists and Analysts: Data scientists and analysts interested in gaining proficiency in AWS AI solutions, machine learning techniques, and data-driven decision-making processes with the power of cloud technologies.
- Technology Enthusiasts: Anyone with an interest in artificial intelligence and cloud computing who wants to get hands-on experience with AWS AI services and learn about cutting-edge AI applications in the cloud.
- Managers and Decision Makers: Business leaders and decision-makers who wish to gain a strategic understanding of how AWS AI tools and services can be utilized to drive innovation and efficiency within their organizations.
Course Content
The course content is structured to provide a step-by-step approach toward mastering AI with AWS. Key topics to be covered include:
1. Introductory Session on AI and AWS Cloud
- Overview of concepts in Artificial Intelligence and Machine Learning
- Understanding the AWS Cloud Platform and its relevance to AI
- Key AWS AI Services and Use Cases
- AWS AI Tools for Developers and Machine Learning Engineers
2. AWS AI Fundamentals
- Introduction to AWS Machine Learning tools: Amazon SageMaker
- How AWS AI architecture and its components work
- Overview of AWS AI services such as Amazon Polly, Amazon Rekognition, and Amazon Lex
3. Deep Learning and Neural Networks
- Introduction to concepts and algorithms in deep learning
- Setting up deep learning environments using AWS Deep Learning AMIs
- Using AWS Deep Learning services for model training and deployment
4. AWS SageMaker Training
- Getting started with Amazon SageMaker for building and training models
- SageMaker tools to tune and evaluate models
- Deploying Machine Learning Models using SageMaker for scalable solutions
5. Building AI Applications on AWS
- Business application development on AWS using AWS Lambda, Amazon Lex, and Amazon Polly
- Implementing real-time AI solutions such as chatbots, voice recognition, and more
- Integration with other AWS services: AWS IoT and AWS Kinesis
6. How to Deploy and Optimize AWS AI
- Best practices for deploying an AI model on AWS infrastructure
- Using AWS Elastic Beanstalk and AWS EC2 to scale AI applications
- Monitoring, logging, and optimizing AI models in production
7. AWS Machine Learning Developer Training
- Preparation for the AWS ML Developer exam
- Summary of key concepts for AWS AI and ML certification
- Hands-on exercises and case studies for practical experience
8. Advanced AI and Machine Learning Solutions on AWS
- Leveraging advanced AI tools and solutions for Big Data, Predictive Analytics, and AI-driven insights
- Deploying complex machine learning models with AWS AI services
- Customizing AI solutions for industry-specific applications