1 min read
Edge AI Deployment: Taking Models to the Data
Sometimes the data can't come to the cloud. Today I'm exploring strategies for deploying AI models at the edge.
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Thoughts on data engineering, cloud architecture, and technology. 2205 articles and counting.
Showing 24 of 2205 posts — page 33 of 92
2 min read
GPT-4o Released: OpenAI's Fastest and Most Capable Multimodal Model
Today OpenAI announced GPT-4o (the "o" stands for "omni") - their new flagship model that can reason across audio, vision, and text in real time. This is…
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Phi-3 Family: Microsoft's Small Language Models
Phi-3 uses high-quality training data (textbooks, filtered web content) rather than raw internet scale.
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Local AI Models: Running Intelligence On-Device
Not every AI workload needs to call the cloud. Today I'm exploring when and how to run AI models locally on your device.
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NPU Development for AI PCs: A Deep Dive
Yesterday I introduced Copilot+ PCs. Today let's dive deep into developing applications that leverage the NPU effectively.
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The Rise of AI PCs: NPUs and Local AI Processing
NPU wins on efficiency, crucial for battery life.
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GPT-4o Speed Improvements: Building Low-Latency Applications
GPT-4o is 2x faster than GPT-4 Turbo. Today I'm exploring how to leverage this speed for responsive applications.
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Cost Reduction Strategies with GPT-4o
GPT-4o is already 50% cheaper than GPT-4 Turbo, but there are more ways to optimize costs. Here are practical strategies I use in production.
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Deploying GPT-4o on Azure OpenAI Service
1. Navigate to your Azure OpenAI resource 2. Go to Model deployments Deploy model 3. Select from the model list 4. Configure deployment settings 5. Deploy…
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Multimodal Conversations with GPT-4o
A typical multimodal conversation might look like: \n{code snippet}\n For real time applications: Managing context across modalities: 1. Maintain coherent…
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GPT-4o Real-Time Vision: Processing Images at Scale
GPT-4o's vision capabilities are impressive, but what makes them practical for enterprise is the combination of quality, speed, and cost. Today I'm…
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Building Voice AI Applications with Azure OpenAI
Voice AI is transforming how we interact with applications. Today I'm exploring how to build voice-enabled AI applications using Azure's current capabilities.
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The Future of Multimodal AI: What's Next After GPT-4 Vision
Each modality is handled separately, then combined. This works, but has latency and integration challenges.
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Microsoft Build 2024 Preview: What to Expect for AI
Microsoft has been rapidly expanding Azure OpenAI capabilities. We can expect: New model deployments : Potentially GPT 4 improvements or new multimodal…
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Foundation Model APIs in Databricks: Enterprise LLM Access
Databricks Foundation Model APIs provide enterprise-ready access to state-of-the-art LLMs. This guide covers using these APIs for building AI applications.
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Serverless Model Serving: Pay-Per-Request ML Inference
Serverless model serving eliminates infrastructure management while providing cost-effective, scalable ML inference. This guide covers implementing…
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Databricks Model Serving Updates: April 2024
Databricks Model Serving continues to evolve with new features for deploying and scaling ML models. This guide covers the latest updates and best practices.
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Online Feature Serving in Databricks: Real-Time ML Features
Online feature serving enables real-time ML inference by providing low-latency access to features. This guide covers setting up and using Databricks online…
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Feature Engineering in Databricks: From Raw Data to ML Features
Feature engineering transforms raw data into meaningful inputs for machine learning models. Databricks provides powerful tools for building and managing…
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Unity Catalog for ML: Governed Machine Learning
Unity Catalog extends governance to machine learning assets. Manage models, features, and experiments with the same rigor as your data.
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Databricks Vector Search Deep Dive: Production Patterns
Taking Vector Search to production requires careful consideration of performance, reliability, and maintenance. This guide covers production-ready patterns.
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Vector Search in Databricks: Semantic Search at Scale
Databricks Vector Search enables semantic similarity search over your lakehouse data. Build RAG applications, recommendation systems, and intelligent search…
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The ai_query Function: Custom LLM Calls in Databricks SQL
The aiquery() function enables custom LLM interactions directly in SQL. Unlike specialized functions, it allows you to craft any prompt and get intelligent…
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SQL AI Functions in Databricks: LLMs Meet SQL
Databricks SQL AI functions bring large language model capabilities directly into SQL queries. Process text, generate insights, and enrich data without…