MLA 013 Tech Stack for Customer-Facing Machine Learning Products
Release Date: 01/03/2021
Machine Learning Guide
ML engineering demand remains high with a 3.2 to 1 job-to-candidate ratio, but entry-level hiring is collapsing as AI automates routine programming and data tasks. Career longevity requires shifting from model training to production operations, deep domain expertise, and mastering AI-augmented workflows before standard implementation becomes a commodity. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want Market Data and Displacement ML engineering demand rose 89% in early 2025....
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OpenClaw is a self-hosted AI agent daemon that executes autonomous tasks through messaging apps like WhatsApp and Telegram using persistent memory. It integrates with Claude Code to enable software development and administrative automation directly from mobile devices. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want OpenClaw is a self-hosted AI agent daemon (Node.js, port 18789) that executes autonomous tasks via messaging apps like WhatsApp or Telegram. Developed by Peter...
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AI agents differ from chatbots by pursuing autonomous goals through the ReACT loop rather than responding to turn-based prompts. While coding agents are currently the most reliable due to verifiable feedback loops, the market is expanding into desktop and browser automation via tools like Claude co-work and open claw. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want Fundamental Definitions Agent vs. Chatbot: Chatbots are turn-based and human-driven. Agents receive...
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How to maintain character consistency, style consistency, etc in an AI video. Prosumers can use Google Veo 3’s "High-Quality Chaining" for fast social media content. Indie filmmakers can achieve narrative consistency by combining Midjourney V7 for style, Kling for lip-synced dialogue, and Runway Gen-4 for camera control, while professional studios gain full control with a layered ComfyUI pipeline to output multi-layer EXR files for standard VFX compositing. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI...
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Google Veo leads the generative video market with superior 4K photorealism and integrated audio, an advantage derived from its YouTube training data. OpenAI Sora is the top tool for narrative storytelling, while Kuaishou Kling excels at animating static images with realistic, high-speed motion. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want S-Tier: Google Veo The market leader due to superior visual quality, physics simulation, 4K resolution, and , which removes...
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The AI image market has split: Midjourney creates the highest quality artistic images but fails at text and precision. For business use, OpenAI's GPT-4o offers the best conversational control, while Adobe Firefly provides the strongest commercial safety from its exclusively licensed training data. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want The 2025 generative AI image market is defined by a split between two types of tools. "Artists" like Midjourney excel at creating...
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Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links Notes and resources at - stay healthy & sharp while you learn & code Build the future of multi-agent software with . Thanks to from for recording...
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At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as...
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Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex...
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Agentic engineering shifts the developer role from manual coding to orchestrating AI agents that automate the full software lifecycle from ticket to deployment. Using Claude Code with MCP servers and git worktrees allows a single person to manage the output and quality of an entire engineering organization. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want The Shift: Agentic Engineering Andrej Karpathy transitioned from "vibe coding" in February 2025 to "agentic engineering" in...
info_outlinePrimary technology recommendations for building a customer-facing machine learning product include React and React Native for the front end, serverless platforms like AWS Amplify or GCP Firebase for authentication and basic server/database needs, and Postgres as the relational database of choice. Serverless approaches are encouraged for scalability and security, with traditional server frameworks and containerization recommended only for advanced custom backend requirements. When serverless options are inadequate, use Node.js with Express or FastAPI in Docker containers, and consider adding Redis for in-memory sessions and RabbitMQ or SQS for job queues, though many of these functions can be handled by Postgres. The machine learning server itself, including deployment strategies, will be discussed separately.
Links
- Notes and resources at ocdevel.com/mlg/mla-13
- Try a walking desk stay healthy & sharp while you learn & code
Client Applications
- React is recommended as the primary web front-end framework due to its compositional structure, best practice enforcement, and strong community support.
- React Native is used for mobile applications, enabling code reuse and a unified JavaScript codebase for web, iOS, and Android clients.
- Using React and React Native simplifies development by allowing most UI logic to be written in a single language.
Server (Backend) Options
- The episode encourages starting with serverless frameworks, such as AWS Amplify or GCP Firebase, for rapid scaling, built-in authentication, and security.
- Amplify allows seamless integration with React and handles authentication, user management, and database access directly from the client.
- When direct client-to-database access is insufficient, custom business logic can be implemented using AWS Lambda or Google Cloud Functions without managing entire servers.
- Only when serverless frameworks are insufficient should developers consider managing their own server code.
- Recommended traditional backend options include Node.js with Express for JavaScript environments or FastAPI for Python-centric projects, both offering strong concurrency support.
- Using Docker to containerize server code and deploying via managed orchestration (e.g., AWS ECS/Fargate) provides flexibility and migration capability beyond serverless.
- Python's FastAPI is advised for developers heavily invested in the Python ecosystem, especially if machine learning code is also in Python.
Database and Supporting Infrastructure
- Postgres is recommended as the primary relational database, owing to its advanced features, community momentum, and versatility.
- Postgres can serve multiple infrastructure functions beyond storage, including job queue management and pub/sub (publish-subscribe) messaging via specific database features.
- NoSQL options such as MongoDB are only recommended when hierarchical, non-tabular data models or specific performance optimizations are necessary.
- For situations requiring in-memory session management or real-time messaging, Redis is suggested, but Postgres may suffice for many use cases.
- Job queuing can be accomplished with external tools like RabbitMQ or AWS SQS, but Postgres also supports job queuing via transactional locks.
Cloud Hosting and Server Management
- Serverless deployment abstracts away infrastructure operations, improving scalability and reducing ongoing server management and security burdens.
- Serverless functions scale automatically and only incur charges during execution.
- Amplify and Firebase offer out-of-the-box user authentication, database, and cloud function support, while custom authentication can be handled with tools like AWS Cognito.
- Managed database hosting (e.g., AWS RDS for Postgres) simplifies backups, scaling, and failover but is distinct from full serverless paradigms.
Evolution of Web Architectures
- The episode contrasts older monolithic frameworks (Django, Ruby on Rails) with current microservice and serverless architectures.
- Developers are encouraged to leverage modern tools where possible, adopting serverless and cloud-managed components until advanced customization requires traditional servers.
Links
Client
- React for web client
- create-react-app: quick-start React setup
- React Bootstrap: CSS framework (alternatives: Tailwind, Chakra, MaterialUI)
- react-router and easy-peasy as useful plugins
- React Native for mobile apps
Server
- AWS Amplify for serverless web and mobile backends
- GCP Firebase
- AWS Serverless (underlying building blocks)
- AWS Lambda for serverless functions
- ECR, Fargate, Route53, ELB for containerized deployment