MLA 017 AWS Local Development Environment

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1. 

   S1 · E55 · 9 Feb 2025 · 28 min

   MLA 022 Vibe Coding: Codex vs Claude Code vs Antigravity vs Grok

   What a coding agent actually is, and how Claude Code, OpenAI Codex, Google Antigravity and xAI's Grok Build differ in philosophy rather than features. Plus open harnesses pi and OpenCode, the open-weight coders, and a decision rule by constraint. Links Try a walking desk - stay healthy & sharp while you learn & code More OCDevel shows - this one has siblings, each on its own subject and produced the same way First of three episodes on vibe coding: the field of coding agents (Claude Code, OpenAI Codex, Google Antigravity, Grok Build, pi, OpenCode) as of late 2026 and how to choose one. The…

2. 

   S1 · E54 · 9 Feb 2025 · 43 min

   MLG 033 Transformers

   Links : Notes and resources at ocdevel.com/mlg/33 3Blue1Brown videos: https://3blue1brown.com/ Try a walking desk stay healthy & sharp while you learn & code Try Descript 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)…

3. 

   S1 · E53 · 22 Jun 2022 · 26 min

   MLA 021 Databricks: Cloud Analytics and MLOps

   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 ocdevel.com/mlg/mla-21 Try a walking desk stay healthy & sharp while you learn & code Raybeam and…

4. 

   S1 · E52 · 29 Jan 2022 · 1 hr 9 min

   MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes

   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 ocdevel.com/mlg/mla-20 Try a walking desk stay healthy & sharp while you learn & code…

5. 

   S1 · E51 · 13 Jan 2022 · 1 hr 15 min

   MLA 019 Cloud, DevOps & Architecture

   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 ocdevel.com/mlg/mla-19 Try a walking desk stay…

6. 

   S1 · E48 · 5 Nov 2021 · 1 hr

   MLA 016 AWS SageMaker MLOps 2

   SageMaker streamlines machine learning workflows by enabling integrated model training, tuning, deployment, monitoring, and pipeline automation within the AWS ecosystem, offering scalable compute options and flexible development environments. Cloud-native AWS machine learning services such as Comprehend and Poly provide off-the-shelf solutions for NLP, time series, recommendations, and more, reducing the need for custom model implementation and deployment. Links Notes and resources at ocdevel.com/mlg/mla-16 Try a walking desk stay healthy & sharp while you learn & code Model Training and…

7. 

   S1 · E47 · 4 Nov 2021 · 48 min

   MLA 015 AWS SageMaker MLOps 1

   SageMaker is an end-to-end machine learning platform on AWS that covers every stage of the ML lifecycle, including data ingestion, preparation, training, deployment, monitoring, and bias detection. The platform offers integrated tools such as Data Wrangler, Feature Store, Ground Truth, Clarify, Autopilot, and distributed training to enable scalable, automated, and accessible machine learning operations for both tabular and large data sets. Links Notes and resources at ocdevel.com/mlg/mla-15 Try a walking desk stay healthy & sharp while you learn & code Amazon SageMaker: The Machine Learning…

8. 

   S1 · E46 · 18 Jan 2021 · 36 min

   MLA 014 Hosting and Deploying ML: Managed APIs, Serverless GPUs, Self-Hosting

   Where the model behind your product should run in 2026: managed APIs vs open weights, AWS native vs Modal, RunPod and Cloud Run GPU, vLLM and SGLang, quantized CPU inference on Spot, batch pricing, prod evals, and infra as code. Links Try a walking desk - stay healthy & sharp while you learn & code More OCDevel shows - this one has siblings, each on its own subject and produced the same way The infrastructure episode, standalone: where the model behind your product runs in 2026, and what each choice costs. Managed APIs versus self-hosted open weights, AWS native (Bedrock, SageMaker) versus…

9. 

   S1 · E45 · 3 Jan 2021 · 48 min

   MLA 013 Tech Stack for Customer-Facing Machine Learning Products

   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…

10. 

    S1 · E44 · 9 Nov 2020 · 32 min

    MLA 012 Docker for Machine Learning Workflows

    Docker enables efficient, consistent machine learning environment setup across local development and cloud deployment, avoiding many pitfalls of virtual machines and manual dependency management. It streamlines system reproduction, resource allocation, and GPU access, supporting portability and simplified collaboration for ML projects. Machine learning engineers benefit from using pre-built Docker images tailored for ML, allowing seamless project switching, host OS flexibility, and straightforward deployment to cloud platforms like AWS ECS and Batch, resulting in reproducible and…