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MLA 013 Tech Stack for Customer-Facing Machine Learning Products

Machine Learning Guide

Release Date: 01/03/2021

MLG 035 Large Language Models 2 show art MLG 035 Large Language Models 2

Machine Learning Guide

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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Machine Learning Guide

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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Machine Learning Guide

Tool use in code AI agents allows for both in-editor code completion and agent-driven file and command actions, while the Model Context Protocol (MCP) standardizes how these agents communicate with external and internal tools. MCP integration broadens the automation capabilities for developers and machine learning engineers by enabling access to a wide variety of local and cloud-based tools directly within their coding environments. Links Notes and resources at  stay healthy & sharp while you learn & code Tool Use in Code AI Agents Code AI agents offer two primary modes of...

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Machine Learning Guide

Gemini 2.5 Pro currently leads in both accuracy and cost-effectiveness among code-focused large language models, with Claude 3.7 and a DeepSeek R1/Claude 3.5 combination also performing well in specific modes. Using local open source models via tools like Ollama offers enhanced privacy but trades off model performance, and advanced workflows like custom modes and fine-tuning can further optimize development processes. Links Notes and resources at  stay healthy & sharp while you learn & code Model Current Leaders According to the  (as of April 12, 2025), leading...

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Machine Learning Guide

Vibe coding is using large language models within IDEs or plugins to generate, edit, and review code, and has recently become a prominent and evolving technique in software and machine learning engineering. The episode outlines a comparison of current code AI tools - such as Cursor, Copilot, Windsurf, Cline, Roo Code, and Aider - explaining their architectures, capabilities, agentic features, pricing, and practical recommendations for integrating them into development workflows. Links Notes and resources at   stay healthy & sharp while you learn & code Definition and...

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Machine Learning Guide

Links: Notes and resources at 3Blue1Brown videos:   stay healthy & sharp while you learn & code  audio/video editing with AI power-tools Background & Motivation RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware. Breakthrough: “Attention Is All You Need” replaced recurrence with self-attention, unlocking massive parallelism and scalability. Core Architecture Layer Stack: Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped...

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Machine Learning Guide

Databricks is a cloud-based platform for data analytics and machine learning operations, integrating features such as a hosted Spark cluster, Python notebook execution, Delta Lake for data management, and seamless IDE connectivity. Raybeam utilizes Databricks and other ML Ops tools according to client infrastructure, scaling needs, and project goals, favoring Databricks for its balanced feature set, ease of use, and support for both startups and enterprises. Links Notes and resources at   stay healthy & sharp while you learn & code Raybeam and Databricks Raybeam is a...

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Machine Learning Guide

Machine learning pipeline orchestration tools, such as SageMaker and Kubeflow, streamline the end-to-end process of data ingestion, model training, deployment, and monitoring, with Kubeflow providing an open-source, cross-cloud platform built atop Kubernetes. Organizations typically choose between cloud-native managed services and open-source solutions based on required flexibility, scalability, integration with existing cloud environments, and vendor lock-in considerations. Links Notes and resources at   stay healthy & sharp while you learn & code  - Data Scientist...

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Machine Learning Guide

The deployment of machine learning models for real-world use involves a sequence of cloud services and architectural choices, where machine learning expertise must be complemented by DevOps and architecture skills, often requiring collaboration with professionals. Key concepts discussed include infrastructure as code, cloud container orchestration, and the distinction between DevOps and architecture, as well as practical advice for machine learning engineers wanting to deploy products securely and efficiently. Links Notes and resources at   stay healthy & sharp while you learn...

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Machine Learning Guide

AWS development environments for local and cloud deployment can differ significantly, leading to extra complexity and setup during cloud migration. By developing directly within AWS environments, using tools such as Lambda, Cloud9, SageMaker Studio, client VPN connections, or LocalStack, developers can streamline transitions to production and leverage AWS-managed services from the start. This episode outlines three primary strategies for treating AWS as your development environment, details the benefits and tradeoffs of each, and explains the role of infrastructure-as-code tools such as...

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Primary 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

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

Server

Database, Job-Queues, Sessions