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MLA 017 AWS Local Development Environment

Machine Learning Guide

Release Date: 11/06/2021

MLA 030 AI Job Displacement & ML Careers show art MLA 030 AI Job Displacement & ML Careers

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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MLA 029 OpenClaw show art MLA 029 OpenClaw

Machine Learning Guide

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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MLA 028 AI Agents show art MLA 028 AI Agents

Machine Learning Guide

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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MLA 027 AI Video End-to-End Workflow show art MLA 027 AI Video End-to-End Workflow

Machine Learning Guide

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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MLA 026 AI Video Generation: Veo 3 vs Sora, Kling, Runway, Stable Video Diffusion show art MLA 026 AI Video Generation: Veo 3 vs Sora, Kling, Runway, Stable Video Diffusion

Machine Learning Guide

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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MLA 025 AI Image Generation: Midjourney vs Stable Diffusion, GPT-4o, Imagen & Firefly show art MLA 025 AI Image Generation: Midjourney vs Stable Diffusion, GPT-4o, Imagen & Firefly

Machine Learning Guide

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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MLG 036 Autoencoders show art MLG 036 Autoencoders

Machine Learning Guide

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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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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MLG 034 Large Language Models 1 show art MLG 034 Large Language Models 1

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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MLA 024 Agentic Software Engineering show art MLA 024 Agentic Software Engineering

Machine Learning Guide

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...

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More Episodes

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 Terraform and CDK in maintaining replicable, trackable cloud infrastructure.

Links

Docker Fundamentals for Development

  • Docker containers encapsulate operating systems, packages, and code, which simplifies dependency management and deployment.
  • Files are added to containers using either the COPY command for one-time inclusion during a build or the volume directive for live synchronization during development.
  • Docker Compose orchestrates multiple containers on a local environment, while Kubernetes is used at larger scale for container orchestration in the cloud.

Docker and AWS Integration

  • Docker is frequently used in AWS, including for packaging and deploying Lambda functions, SageMaker jobs, and ECS/Fargate containers.
  • Deploying complex applications like web servers and databases on AWS involves using services such as ECR for image storage, ECS/Fargate for container management, RDS for databases, and requires configuration of networking components such as VPCs, subnets, and security groups.

Challenges in Migrating from Localhost to AWS

  • Local Docker Compose setups differ considerably from AWS managed services architecture.
  • Migrating to AWS involves extra steps such as pushing images to ECR, establishing networking with VPCs, configuring load balancers or API Gateway, setting up domain names with Route 53, and integrating SSL certificates via ACM.
  • Configuring internal communication between services and securing databases adds complexity compared to local development.

Strategy 1: Developing Entirely in the AWS Cloud

  • Developers can use AWS Lambda’s built-in code editor, Cloud9 IDE, and SageMaker Studio to edit, run, and deploy code directly in the AWS console.
  • Cloud-based development is not tied to a single machine and eliminates local environment setup.
  • While convenient, in-browser IDEs like Cloud9 and SageMaker Studio are less powerful than established local tools like PyCharm or DataGrip.

Strategy 2: Local Development Connected to AWS via Client VPN

  • The AWS Client VPN enables local machines to securely access AWS VPC resources, such as RDS databases or Lambda endpoints, as if they were on the same network.
  • This approach allows developers to continue using their preferred local IDEs while testing code against actual cloud services.
  • Storing sensitive credentials is handled by AWS Secrets Manager instead of local files or environment variables.
  • Example tutorials and instructions:

Strategy 3: Local Emulation of AWS Using LocalStack

  • LocalStack provides local, Docker-based emulation of AWS services, allowing development and testing without incurring cloud costs or latency.
  • The project offers a free tier supporting core serverless services and a paid tier covering more advanced features like RDS, ACM, and Route 53.
  • LocalStack supports mounting local source files into Lambda functions, enabling direct development on the local machine with changes immediately reflected in the emulated AWS environment.
  • This approach brings rapid iteration and cost savings, but coverage of AWS features may vary, especially for advanced or new AWS services.

Infrastructure as Code: Managing AWS Environments

  • Managing AWS resources through the web console is not sustainable for tracking or reproducing environments.
  • Infrastructure as code (IaC) tools such as TerraformAWS CDK, and Serverless enable declarative, version-controlled description and deployment of AWS services.
  • Terraform offers broad multi-cloud compatibility and support for both managed and cloud-native services, whereas CDK is AWS-specific and typically more streamlined but supports fewer services.
  • Changes made via IaC tools are automatically propagated to dependent resources, reducing manual error and ensuring consistency across environments.

Benefits of AWS-First Development

  • Developing directly in AWS or with local emulation ensures alignment between development, staging, and production environments, reducing last-minute deployment issues.
  • Early use of AWS services can reveal managed solutions—such as Cognito for authentication or Data Wrangler for feature transformation—that are more scalable and secure than homegrown implementations.
  • Infrastructure as code provides reproducibility, easier team onboarding, and disaster recovery.

Alternatives and Kubernetes

  • Kubernetes represents a different model of orchestrating containers and services, generally leveraging open source components inside Docker containers, independent of managed AWS services.
  • While Kubernetes can manage deployments to AWS (via EKS), GCP, or Azure, its architecture and operational concerns differ from AWS-native development patterns.

Additional AWS IDEs and Services

Conclusion

  • Choosing between developing in the AWS cloud, connecting local environments via VPN, or using tools like LocalStack depends on team needs, budget, and workflow preferences.
  • Emphasizing infrastructure as code ensures environments remain consistent, maintainable, and easily reproducible.