Coding & Programming
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A technical breakdown of the Transformer architecture, focusing on its core mechanics and scaling properties.
The user requested a slide that explains the forward pass of a Transformer model, titled "Transformers: Mechanics That Scale". The slide needs to visually walk through the main components, starting from input tokens and proceeding through embedding, multi-head self-attention, the MLP, and residual/norm layers. It should connect these mechanics to system-level choices and optimizations, such as the O(n²) cost of attention, the role of the KV cache in decoding, positional strategies like RoPE, and the impact of scaling laws on model training and efficiency.
Cost & Performance Tuning for Optimal Business Outcomes
Create a slide about cost and performance tuning, emphasizing the importance of finding a balance between the two for optimal business outcomes, not just raw speed. The content should cover strategies for achieving this balance, including profiling, batching, quantization, distillation, caching, autoscaling, and spot instances. It should also visually represent the relationship between cost and latency, highlighting the 'sweet spot' where optimal balance is achieved. Include specific examples like using KV cache or mixed precision to operate near this sweet spot. Finally, emphasize the iterative nature of this process and the need for continuous monitoring and adjustment.
Team Workflow & Shared Standards
This slide visually represents the workflow and collaboration between different teams (Product, Data Engineering, ML Engineering, and Platform/SRE) involved in a product's lifecycle, from ideation to production. It emphasizes the roles and responsibilities of each team and highlights the importance of shared standards (schemas, feature store, model registry) for a streamlined and efficient process. The slide uses distinct colors and icons for each team, facilitating quick comprehension of their respective contributions. The animation further enhances engagement by sequentially introducing each team's role.
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.
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.
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.
React Components: Props, State, and Purity
Create a slide about React components, focusing on props, state, and the concept of pure render. The slide should explain how function components work with props as input and JSX as output. It should also differentiate between props and state, highlighting that props are external and immutable while state is internal and can trigger re-renders when updated. Emphasize the importance of pure render, where the same props and state always produce the same UI, avoiding side effects during render. Include a code example demonstrating a simple component with props, state, and a button that updates the state. Visually, the slide should have a title, a short description of pure render, a list of key concepts (props in, JSX out; state updates trigger re-renders; pure render = same input, same output), and the code example. Use animations to make the content appear dynamically. The speaker notes should reinforce these concepts and provide a detailed explanation of the code example.

Coding & ProgrammingPage 4 of 12

Coding & Programming
Page 4 of 12