Alex Delaney
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Generative AI & RAG Patterns
Explain the concept of Retrieval Augmented Generation (RAG) in the context of Generative AI. The slide should focus on how RAG addresses the limitations of Large Language Models (LLMs) by incorporating external knowledge sources. Explain how the RAG loop works, emphasizing the role of the retriever and its connection to indexed knowledge. Highlight the benefits of freshness and grounding that RAG brings. Visually represent the flow of information in a RAG system, including the user, retriever, model, and the final answer.
Cloud vs. Edge Deployment: A Comparative Overview
Create a slide that compares and contrasts deploying machine learning models in the cloud versus at the edge. Highlight the advantages and disadvantages of each approach, and suggest scenarios where one might be preferred over the other. The slide should be visually appealing and easy to understand, using clear language and concise bullet points. Consider using icons or visuals to represent cloud and edge deployments. The target audience is technical professionals and business stakeholders who are involved in making decisions about ML model deployment.
AI Safety, Ethics, and Compliance in Product Development
This slide is for a presentation about our commitment to safety, ethics, and compliance in developing and deploying AI-enabled systems. It visually represents our core principles: Privacy, Bias Mitigation, Explainability, and Alignment. The presentation emphasizes these are not mere add-ons but fundamental to our product development process. It also highlights our adherence to industry standards and regulations like GDPR, SOC 2, and ISO/IEC 27001, showcasing our dedication to responsible AI practices. The target audience includes potential clients, partners, and internal stakeholders interested in understanding our approach to AI governance and ethical considerations.
Evaluation Metrics and Visualization
This slide visually represents the evaluation metrics used for different machine learning tasks, including classification, regression, and LLMs. It showcases how to visualize progress and performance using metrics like F1, ROC-AUC, RMSE, MAE, hallucination rate, toxicity, and latency. The slide includes a bar chart comparing accuracy across different models and a gauge visualizing latency against a target. The purpose is to emphasize the importance of selecting appropriate metrics for each task, visualizing them effectively, and focusing on metrics that directly impact user outcomes.
MLOps Pipeline: From Code to Production
Create a slide visualizing the MLOps pipeline from code to production. Include stages like code, data, train, register, deploy, and monitor. Show a visual representation of the pipeline flow. Mention key MLOps enablers like automation, versioning, and reproducibility. The slide should have a modern, clean design. Target audience is technical professionals interested in learning about MLOps.
Training Infrastructure: A Unified Approach
This slide visually represents a training infrastructure, emphasizing the interconnectedness of its components. It focuses on the key elements required for efficient and scalable training, from the underlying hardware to the software frameworks and optimization techniques. The central visual element, a pulsing chip, symbolizes the compute power at the heart of the system. The slide highlights GPUs and TPUs for compute, PyTorch and JAX as frameworks, and optimization strategies like mixed precision and checkpointing. The parallax scrolling effect reinforces the layered nature of the infrastructure and how these elements interact. The intent is to convey the message that a well-aligned stack leads to faster, cheaper, and more reliable training.
Choosing the Right Machine Learning Model
Create a slide to help choose between Classical ML, Deep Learning, and Foundation/LLM models. The slide should emphasize selecting the right model based on the problem's constraints and goals, not the other way around. It should highlight the strengths of each paradigm and provide clear criteria for selection. Classical ML excels in structured, smaller datasets where interpretability is key. Deep Learning is best suited for complex patterns with large datasets. Foundation/LLMs are ideal for open-ended language tasks. The slide should be visually appealing and easy to understand.
Building Robust Data Foundations for Data-Driven Success
This slide introduces a shared mental model for establishing solid data foundations, crucial for data-driven decision-making and effective analytics. It covers key aspects of data management: Collection (sources, pipelines, retention), Labeling (taxonomy, consistency, rules), Quality checks (coverage, accuracy, drift), Governance (access, privacy, compliance), and Lineage (source to model). The slide visually represents these concepts with a list and includes a sample dataset view with a quality score, demonstrating how these principles translate into practical application. The emphasis is on building a culture of data quality and consistent practices across teams.
Visualizing the End-to-End Lifecycle of a Project
Create a slide visualizing the end-to-end lifecycle of a project, emphasizing continuous flow and measurable gates. The visualization should be clean and modern, using a single moving element to represent progress through the stages. The stages should be clearly labeled: Frame, Data, Model, Train, Evaluate, Deploy/Monitor. Include a concise caption highlighting the iterative nature of the process and the importance of measurement at each stage. Speaker notes should explain each gate and emphasize the continuous flow and feedback loop from Monitor back to Frame. The slide should be designed for a professional audience interested in project management and process optimization.
Drivers of AI Adoption
Create a slide that explains the key drivers behind the current surge in AI adoption. It should highlight four main factors: decreasing compute costs, increased data availability, the rise of open-source frameworks, and growing business demand. Visually, the slide should have a modern, clean design with a dark background and bright accent colors. The content should be concise and easy to understand, using visuals like charts or graphs to illustrate the points if possible. The target audience is business professionals and technology enthusiasts interested in understanding the current AI landscape.
The AI Development Lifecycle: A Comprehensive Overview
Create a slide that visually represents the full lifecycle of AI development, going beyond just model training. It should emphasize the interconnectedness of each stage, from initial problem framing to ongoing monitoring. Use icons to visually represent each stage. The target audience is technical professionals and product managers involved in AI projects.
AI Development: A Forward-Looking Perspective
Create a slide for a presentation about AI development. The slide should have a futuristic, high-energy feel. It should include a title, a subtle background element, and my name and the date. The title should be 'AI Development'. My name is Alex Delaney, and the date is September 2025. Consider using neon gradients, isometric grids, or other visual elements to enhance the design. I want the overall aesthetic to be modern and engaging, suitable for a tech-focused audience.

Alex DelaneyPage 6 of 11

Alex Delaney
Page 6 of 11