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
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 Code AI MCP Servers, ML Engineering
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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MLA 023 Code AI Models & Modes
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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MLA 022 Code AI: Cursor, Cline, Roo, Aider, Copilot, Windsurf
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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MLG 033 Transformers
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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MLA 021 Databricks: Cloud Analytics and MLOps
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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MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes
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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MLA 019 Cloud, DevOps & Architecture
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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MLA 017 AWS Local Development Environment
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...
info_outlineThe 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 healthy & sharp while you learn & code
;## Translating Machine Learning Models to Production
- After developing and training a machine learning model locally or using cloud tools like AWS SageMaker, it must be deployed to reach end users.
- A typical deployment stack involves the trained model exposed via a SageMaker endpoint, a backend server (e.g., Python FastAPI on AWS ECS with Fargate), a managed database (such as AWS RDS Postgres), an application load balancer (ALB), and a public-facing frontend (e.g., React app hosted on S3 with CloudFront and Route 53).
Infrastructure as Code and Automation Tools
- Infrastructure as code (IaC) manages deployment and maintenance of cloud resources using tools like Terraform, allowing environments to be version-controlled and reproducible.
- Terraform is favored for its structured approach and cross-cloud compatibility, while other tools like Cloud Formation (AWS-specific) and Pulumi offer alternative paradigms.
- Configuration management tools such as Ansible, Chef, and Puppet automate setup and software installation on compute instances but are increasingly replaced by containerization and Dockerfiles.
- Continuous Integration and Continuous Deployment (CI/CD) pipelines (with tools like AWS CodePipeline or CircleCI) automate builds, testing, and code deployment to infrastructure.
Containers, Orchestration, and Cloud Choices
- Containers, enabled by Docker, allow developers to encapsulate applications and dependencies, facilitating consistency across environments from local development to production.
- Deployment options include AWS ECS/Fargate for managed orchestration, Kubernetes for large-scale or multi-cloud scenarios, and simpler services like AWS App Runner and Elastic Beanstalk for small-scale applications.
- Kubernetes provides robust flexibility and cross-provider support but brings high complexity, making it best suited for organizations with substantial infrastructure needs and experienced staff.
- Use of cloud services versus open-source alternatives on Kubernetes (e.g., RDS vs. Postgres containers) affects manageability, vendor lock-in, and required expertise.
DevOps and Architecture: Roles and Collaboration
- DevOps unites development and operations through common processes and tooling to accelerate safe production deployments and improve coordination.
- Architecture focuses on the holistic design of systems, establishing how different technical components fit together and serve overall business or product goals.
- There is significant overlap, but architecture plans and outlines systems, while DevOps engineers implement, automate, and monitor deployment and operations.
- Cross-functional collaboration is essential, as machine learning engineers, DevOps, and architects must communicate requirements, constraints, and changes, especially regarding production-readiness and security.
Security, Scale, and When to Seek Help
- Security is a primary concern when moving to production, especially if handling sensitive data or personally identifiable information (PII); professional DevOps involvement is strongly advised in such cases.
- Common cloud security pitfalls include publicly accessible networks, insecure S3 buckets, and improper handling of secrets and credentials.
- For experimentation or small-scale safe projects, machine learning engineers can use tools like Terraform, Docker, and AWS managed services, but should employ cloud cost monitoring to avoid unexpected bills.
Cloud Providers and Service Considerations
- AWS dominates the cloud market, followed by Azure (strong in enterprise/Microsoft-integrated environments) and Google Cloud Platform (GCP), which offers a strong user interface but has a record of sunsetting products.
- Managed cloud machine learning services, such as AWS SageMaker and GCP Vertex AI, streamline model training, deployment, and monitoring.
- Vendor-specific tools simplify management but limit portability, while Kubernetes and its ML pipelines (e.g., Kubeflow, Apache Airflow) provide open-source, cross-cloud options with greater complexity.
Recommended Learning Paths and Community Resources
- Learning and prototyping with Terraform, Docker, and basic cloud services is encouraged to understand deployment pipelines, but professional security review is critical before handling production-sensitive data.
- For those entering DevOps, structured learning with platforms like aCloudGuru or AWS’s own curricula can provide certification-ready paths.
- Continual learning is necessary, as tooling and best practices evolve rapidly.
Reference Links
Expert coworkers at Dept
- Matt Merrill - Principal Software Developer
- Jirawat Uttayaya - DevOps Lead
- The Ship It Podcast (frequent discussions on DevOps and architecture)
DevOps Tools
Visual Guides and Comparisons
- Which AWS container service should I use?
- A visual guide on troubleshooting Kubernetes deployments
- Public Cloud Services Comparison
- Killed by Google
Learning Resources