MLA 014 Machine Learning Hosting and Serverless Deployment
Release Date: 01/18/2021
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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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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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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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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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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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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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 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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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...
info_outlineMachine learning model deployment on the cloud is typically handled with solutions like AWS SageMaker for end-to-end training and inference as a REST endpoint, AWS Batch for cost-effective on-demand batch jobs using Docker containers, and AWS Lambda for low-usage, serverless inference without GPU support. Storage and infrastructure options such as AWS EFS are essential for managing large model artifacts, while new tools like Cortex offer open source alternatives with features like cost savings and scale-to-zero for resource management.
Links
- Notes and resources at ocdevel.com/mlg/mla-14
- Try a walking desk stay healthy & sharp while you learn & code
Cloud Providers for Machine Learning Hosting
- The major cloud service providers for machine learning hosting are Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.
- AWS is widely adopted due to rapid innovation, a large ecosystem, extensive documentation, and ease of integration with other AWS services, despite some features of GCP, such as TPUs, being attractive for specific use cases.
Core Machine Learning Hosting Services
1. AWS SageMaker
- SageMaker is an end-to-end service for training, monitoring, and deploying machine learning models, including REST endpoint deployment for inference.
- It features auto-scaling, built-in monitoring, and support for Jupyter notebooks, but it incurs at least a 40% cost premium over direct EC2 usage and is always-on, which can be costly for low-traffic applications.
- AWS SageMaker provides REST endpoint deployment and training analytics.
- Google Cloud offers GCP Cloud ML with similar functionality.
2. AWS Batch
- AWS Batch allows one-off batch jobs, typically for resource-intensive ML training or infrequent inference, using Docker containers.
- Batch supports spot instances for significant cost savings and automatically shuts down resources when jobs complete, reducing always-on costs.
- Batch jobs can be triggered via CLI, console, or programmatically, and the service does not provide automatic deployment or monitoring functionality like SageMaker.
- AWS Batch enables Docker-based batch jobs and leverages ECR for container hosting.
3. AWS Lambda
- AWS Lambda provides serverless deployment for machine learning inference, auto-scaling to meet demand, and incurs costs only during actual usage, but it does not support GPU or Elastic Inference.
- Lambda functions can utilize attached AWS EFS for storing and loading large model artifacts, which helps manage deployment size and cold start performance.
- Only models that can perform inference efficiently on CPU within Lambda’s memory and compute limits are suitable for this approach.
4. Elastic Inference and Persistent Storage
- AWS Elastic Inference enables the attachment of fractional GPU resources to EC2 or SageMaker for inference workloads, driving down costs by avoiding full GPU allocation.
- AWS EFS (Elastic File System) is used to provide persistent, shared storage for model artifacts, allowing services like Batch and Lambda to efficiently access large files without repeated downloads.
- AWS EFS allows mounting persistent file systems across services.
Model Optimization and Compatibility
- Model optimizers such as ONNX (Open Neural Network Exchange) and Intel’s OpenVINO can compress and optimize machine learning models for efficient inference, enabling CPU-only deployment with minimal loss of accuracy.
- ONNX helps convert models to a format that is interoperable across different frameworks and architectures, which supports serverless environments like Lambda.
Emerging and Alternative Providers
1. Cortex
- Cortex is an open source system that orchestrates model training, deployment, and scaling on AWS, including support for spot instances and potential for scale-to-zero, reducing costs during idle periods.
- Cortex aims to provide SageMaker-like capabilities without the additional premium and with greater flexibility over infrastructure management.
2. Other Providers
- PaperSpace Gradient and FloydHub are additional providers offering ML model training and deployment services with cost-competitive offerings versus AWS.
- PaperSpace is highlighted as significantly less expensive than SageMaker and Batch, though AWS integration and ecosystem breadth may still steer users toward AWS-native solutions.
Batch and Endpoint Model Deployment Scenarios
- If model usage is rare (e.g., 1–50 times per day), batch approaches such as AWS Batch are cost-effective, running containerized jobs as needed and then shutting down.
- For customer-facing applications requiring consistently available models, endpoint-based services like SageMaker, GCP Cloud ML, or Cortex are more appropriate.
Orchestration and Advanced Architectures
- Kubernetes and related tools can be used to orchestrate ML models and complex pipelines at scale, enabling integration of components such as API gateways, serverless functions, and scalable training and inference systems.
- Tools like KubeFlow leverage Kubernetes for deploying machine learning workloads, but require higher expertise and greater management effort.