AWS AI for Business Leaders
This course empowers business leaders to understand, evaluate, and strategically leverage AI technologies, including machine learning and generative AI, to drive innovation and achieve business objectives. It emphasizes practical applications, ethical considerations, and governance, aligning with the perspective of individuals who use AI/ML solutions rather than build them.
Enroll NowDuration (Total 6 Hours)
Weekdays
Monday to Friday · 1 Hour per Day
+ 1 Bonus hour on Friday
Weekends
Saturday & Sunday · 3 Hours per Day
Training Agenda
Module 1: Fundamentals of AI and Machine Learning
- Understanding Core AI/ML Concepts:
- Define key terms: AI, ML, deep learning, neural networks, computer vision, NLP, model, algorithm, training, and inferencing.
- Differentiate between AI, ML, and deep learning.
- Identify various data types in AI models (labeled, unlabeled, tabular, image, text, structured, unstructured).
- Explain supervised, unsupervised, and reinforcement learning.
- Identifying Practical AI Use Cases for Business:
- Recognize applications where AI/ML can add value (decision-making, scalability, automation).
- Determine when AI/ML is not appropriate (cost-benefit, specific outcomes).
- Select ML techniques for business use cases (regression, classification, clustering).
- Review real-world AI applications (quality control, customer service, recommendations, fraud detection).
- Understand managed AI/ML services (Amazon SageMaker, Transcribe, Translate, Comprehend).
- Overview of the ML Development Lifecycle:
- Describe ML pipeline components (data collection, preparation, training, evaluation, deployment, monitoring).
- Understand sources of ML models (pre-trained, custom).
- Grasp MLOps concepts for repeatable processes and production readiness.
- Evaluate ML models using performance and business metrics (accuracy, ROI, costs, feedback).
Module 2: Fundamentals of Generative AI
- Basic Concepts of Generative AI:
- Tokens, embeddings, prompt engineering, transformer-based LLMs, foundation models.
- Business use cases (content generation, chatbots, code generation, summarization).
- Lifecycle of a foundation model (data selection, model selection, pre-training, fine-tuning, evaluation, deployment, feedback).
- Capabilities and Limitations of Generative AI for Business:
- Advantages (adaptability, responsiveness, simplicity).
- Disadvantages/risks (hallucinations, interpretability, inaccuracy, non-determinism).
- Factors for selecting models (type, performance, compliance).
- Business value and metrics (efficiency, conversion, customer lifetime value).
- Cloud Infrastructure for Generative AI Applications:
- Cloud services (SageMaker JumpStart, Bedrock, Amazon Q).
- Advantages of cloud generative AI (accessibility, cost, speed to market).
- Cost tradeoffs (token-based pricing, provisioned throughput).
Module 3: Applications of Foundation Models
- Designing Applications with Foundation Models:
- Criteria for choosing pre-trained models (cost, modality, latency, support, customization).
- Effect of inference parameters (temperature, input/output length).
- Define Retrieval Augmented Generation (RAG) and business applications (e.g., Bedrock).
- Cost tradeoffs for customizing models (pre-training, fine-tuning, in-context learning, RAG).
- Role of "agents" in automating tasks.
- Effective Prompt Engineering Techniques:
- Concepts: context, instruction, negative prompts.
- Techniques: chain-of-thought, few-shot prompting.
- Benefits and best practices (quality, experimentation, guardrails).
- Risks and limitations (prompt injection, jailbreaking).
- Training and Fine-tuning Foundation Models (High-Level):
- Key elements: pre-training, fine-tuning, continuous pre-training.
- Preparing data for fine-tuning (curation, labeling, representativeness).
- Evaluating Foundation Model Performance:
- Approaches: human evaluation, benchmark datasets.
- Relevant metrics to assess performance.
- Determine if model meets business objectives (productivity, engagement).
Module 4: Guidelines for Responsible AI
- Developing Responsible AI Systems:
- Key features: bias, fairness, inclusivity, robustness, safety, veracity.
- Tools for responsible AI (e.g., Guardrails for Bedrock).
- Responsible practices for model selection (environmental, sustainability).
- Legal risks (IP, bias, trust, hallucinations).
- Effects of bias and variance (demographics, inaccuracy, over/underfitting).
- Tools/methods to detect bias, trustworthiness (audits, SageMaker Clarify, Model Monitor).
- Importance of Transparent and Explainable Models:
- Transparent/explainable vs. opaque models.
- Tools for transparency (SageMaker Model Cards).
- Tradeoffs between safety and transparency.
- Human-centered design for explainable AI.
Module 5: Security, Compliance, and Governance for AI Solutions
- Securing AI Systems:
- Cloud services/features for security (IAM, encryption, shared responsibility).
- Source citation and data origins (lineage, cataloging).
- Best practices for secure data engineering (quality, privacy, access control).
- Security/privacy for AI (prompt injection, encryption).
- Recognizing Governance and Compliance Regulations:
- Compliance standards (ISO, SOC).
- Cloud services for governance (AWS Config, Audit Manager, CloudTrail).
- Data governance strategies (lifecycles, logging, residency, retention).
- Processes for governance protocols (policies, reviews, training).
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Your Instructor
Meet Your Instructor
Stephen SIMON, is a cloud and AI expert with hands-on experience in building and scaling developer communities. He has organized over 50 virtual conferences, hosted 600+ guests in his developer shows, and led 30+ national hackathons through HackIndia — India's largest Web3 and AI hackathon initiative. He works with technologies like Azure, Gemini, LangChain, Python, React, GitHub, and VS Code, and frequently speaks on topics such as cloud architecture, AI in real-world development, and emerging tech trends. His global speaking engagements include Experts Live Europe and WOW Summit Hong Kong, and he is the host of "Azure for Sure" and "A Dash of .NET."
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Frequently Asked Questions
Is this training for me?
Yes, if you're a business leader or decision-maker looking to understand and leverage AI for your organization. No prior technical background required.
Do I need to know how to code?
Not at all! This course is designed for non-coders and focuses on using AI tools and services without writing any code.
What will I be able to do after this?
You'll be able to identify AI opportunities, lead AI initiatives, and make informed decisions about integrating AI into your business.
What if I miss a class?
No problem. Every class is recorded, and you'll get lifetime access to all recordings to watch anytime.
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