AI with AWS Training
Welcome to the AI with AWS Training program, designed to equip participants with essential skills and knowledge at the intersection of artificial intelligence and cloud computing. This comprehensive Corporate training offers a deep dive into leveraging AWS services to build and deploy AI solutions efficiently and effectively. Participants will learn how to harness the power of AWS’s scalable infrastructure and advanced AI services to solve complex business challenges.
Description
Through hands-on labs and real-world case studies, this training empowers participants to explore machine learning algorithms, deploy models using AWS SageMaker, and integrate AI capabilities into applications seamlessly. Whether you are an AI practitioner, developer, or IT professional aiming to leverage AWS for AI innovation, this training will provide you with the expertise to navigate the AI landscape confidently and drive impactful business outcomes.
Benefits of Attending This Training
- Gain practical experience in deploying AI models using AWS services.
- Understand how to leverage AWS infrastructure for scalable AI applications.
- Learn best practices for optimizing cost, security, and performance.
- Certification of completion to validate skills in AI with AWS.
Course Content
Introduction to AWS and AI Services
- Introduction to Amazon Web Services (AWS)
- Overview of AWS cloud computing services
- Benefits of using AWS for AI applications
- AWS AI and Machine Learning Services Overview
- Amazon SageMaker, Comprehend, Rekognition, Polly, and Lex
- Use cases and applications of AWS AI services
- Hands-on Workshop: Setting Up AWS Environment
- Creating an AWS account
- Configuring IAM roles and permissions
Building AI Models with Amazon SageMaker
- Introduction to Amazon SageMaker
- Managed platform for building, training, and deploying ML models
- Components of Amazon SageMaker: notebooks, training jobs, and endpoints
- Model Development with SageMaker
- Data preprocessing and feature engineering
- Training and tuning ML models using SageMaker
- Hands-on Workshop: Developing ML Models with SageMaker
- Building a simple classification or regression model
- Evaluating and deploying the model using SageMaker
Deep Learning on AWS with TensorFlow and MXNet
- Deep Learning Frameworks on AWS
- Overview of TensorFlow and MXNet integration with AWS
- Benefits of using AWS for scalable deep learning
- Amazon EC2 for Deep Learning
- Configuring GPU instances for deep learning tasks
- Parallelizing training jobs with Amazon EC2
- Hands-on Workshop: Deep Learning Projects on AWS
- Implementing a deep learning model using TensorFlow or MXNet
- Optimizing training performance with AWS GPU instances
Natural Language Processing and Computer Vision with AWS
- AWS AI Services for NLP and Computer Vision
- Using Amazon Comprehend for NLP tasks
- Implementing image analysis with Amazon Rekognition
- Custom AI Applications with AWS
- Integrating AWS AI services into custom applications
- Real-time processing and scalability considerations
- Hands-on Workshop: Building NLP and Computer Vision Applications
- Developing a sentiment analysis or image recognition application using AWS services
- Deploying and testing the application on AWS infrastructure
Advanced Topics and Practical Applications
- AWS AI Best Practices and Optimization Techniques
- Cost optimization strategies for AI workloads on AWS
- Monitoring and debugging AI applications on AWS
- Security and Compliance in AWS AI
- Ensuring data security and regulatory compliance
- Implementing encryption and access controls
- Hands-on Project: AI Deployment and Scaling on AWS
- Capstone project integrating AI services and deploying on AWS
- Presentation and review of project outcomes
Contact us
This outline covers foundational to advanced topics in AI using AWS, providing participants with comprehensive knowledge and hands-on experience necessary to deploy AI solutions effectively on the AWS cloud platform. Adjustments can be made based on specific audience needs and expertise levels.
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