Posts tagged with mlp
Why it matters: This approach enables faster, more cost-effective evaluation of search ranking models in A/B tests. Engineers can detect smaller, more nuanced effects, accelerating product iteration and improving user experience by deploying features with higher confidence.
- •Pinterest uses fine-tuned open-source LLMs to automate search relevance assessment, overcoming the limitations of costly and slow human annotations.
- •The LLMs are trained on a 5-level relevance guideline using a cross-encoder architecture and comprehensive Pin textual features, supporting multilingual search.
- •This approach significantly reduces labeling costs and time, enabling much larger and more sophisticated stratified query sampling designs.
- •Stratified sampling, based on query interest and popularity, ensures sample representativeness and drastically reduces measurement variance.
- •The implementation led to a significant reduction in Minimum Detectable Effects (MDEs) from 1.3-1.5% to <= 0.25%, accelerating A/B experiment velocity and feature deployment.
- •Paired sampling and sDCG@K are used to measure the relevance impact of A/B experiments on search ranking.
Why it matters: This article details significant AI platform advancements from Microsoft Ignite, offering developers more model choices and improved semantic understanding for building robust, secure, and flexible AI applications and agents.
- •Microsoft Ignite 2025 showcased significant advancements in agentic AI and cloud solutions, emphasizing rapid developer adoption.
- •Microsoft Foundry now integrates Claude models (Sonnet, Opus) alongside OpenAI's GPT, providing developers with diverse model choices for AI application and agent development.
- •This model diversity in Azure Foundry offers flexibility, enterprise-grade security, compliance, and governance for building AI solutions.
- •New Microsoft IQ offerings aim to enhance semantic understanding, connecting productivity apps, analytics platforms, and AI development environments.
Why it matters: This move provides a stable, open-source foundation for AI agent development, standardizing how LLMs securely interact with external systems. It resolves critical integration challenges, accelerating the creation of robust, production-ready AI tools across industries.
- •The Model Context Protocol (MCP), an open-source standard for connecting LLMs to external tools, has been donated by Anthropic to the Agentic AI Foundation under the Linux Foundation.
- •MCP addresses the "n x m integration problem" by providing a vendor-neutral protocol, standardizing how AI models communicate with diverse services like databases and CI pipelines.
- •Before MCP, developers faced fragmented APIs and brittle, platform-specific integrations, hindering secure and consistent AI agent development.
- •This transition ensures long-term stewardship and a stable foundation for developers building production AI agents and enterprise systems.
- •MCP's rapid adoption highlights its critical role in enabling secure, auditable, and cross-platform communication for AI in various industries.
Why it matters: Engineers can leverage AI for rapid development while maintaining high code quality. This article introduces tools and strategies, like GitHub Code Quality and effective prompting, to prevent "AI slop" and ensure reliable, maintainable code in an accelerated workflow.
- •AI significantly accelerates development but risks generating "AI slop" and technical debt without proper quality control.
- •GitHub Code Quality, leveraging AI and CodeQL, ensures high standards by automatically detecting and suggesting fixes for maintainability and reliability issues in pull requests.
- •Key features include one-click enablement, automated fixes for common errors, enforcing quality bars with rulesets, and surfacing legacy technical debt.
- •Engineers must "drive" AI by providing clear, constrained prompts, focusing on goals, context, and desired output formats to maximize quality.
- •This approach allows teams to achieve both speed and control, preventing trade-offs between velocity and code reliability in the AI era.
Why it matters: This expansion provides engineers with more Azure regions and Availability Zones, enabling highly resilient, performant, and geographically diverse cloud architectures for critical applications and AI workloads.
- •Microsoft is significantly expanding its cloud infrastructure in the US, including a new East US 3 region in Atlanta by early 2027.
- •The East US 3 region will incorporate Availability Zones for enhanced resiliency and support advanced Azure workloads, including AI.
- •Five existing US Azure regions (North Central US, West Central US, US Gov Arizona, East US 2, South Central US) will also gain Availability Zones by 2026-2027.
- •These expansions aim to meet growing customer demand for cloud and AI services, offering greater capacity, resiliency, and agility.
- •The new infrastructure emphasizes sustainability, with the East US 3 region designed for LEED Gold Certification and water conservation.
- •Leveraging Availability Zones and multi-region architectures is highlighted for improving application performance, latency, and overall resilience.
Why it matters: As AI agents become integrated into development, ensuring their output is safe and predictable is critical. This system provides a blueprint for building trust in automated code generation through rigorous feedback loops and validation.
- •Spotify's system focuses on making AI coding agents predictable and trustworthy through structured feedback loops.
- •The architecture ensures that agent-generated code is validated against existing engineering standards and tests.
- •Background agents operate asynchronously to improve code quality without disrupting the primary developer workflow.
- •The framework addresses the challenge of moving from experimental AI generation to production-ready software engineering.
- •Automated verification steps are integrated to prevent the introduction of bugs or technical debt by autonomous agents.
Why it matters: Achieving sub-second latency in voice AI requires rethinking performance metrics and optimizing every microservice. This article shows how semantic end-pointing and synthetic testing are critical for building responsive, human-like voice agents at scale.
- •Developed the Flash Reasoning Engine to achieve sub-second Time to First Audio (TTFA) for natural, human-fast voice interactions.
- •Optimized the real-time voice pipeline by shaving hundreds of milliseconds from microservices, synchronous calls, and serialization paths.
- •Implemented semantic end-pointing algorithms that use confidence thresholds to distinguish between meaningful pauses and true utterance completion.
- •Created AI-driven synthetic customer testing frameworks to generate repeatable data sets and eliminate noise in performance metrics.
- •Resolved measurement inaccuracies where initial tests incorrectly reported 70-second latencies by focusing on TTFA instead of total output duration.
Why it matters: This system provides real-time, statistically robust insights into content safety, enabling platforms to proactively identify and mitigate harms. It's crucial for maintaining user trust and scaling content moderation efficiently with AI.
- •Pinterest developed an AI-assisted system to measure "prevalence" of policy-violating content, focusing on the percentage of total views.
- •This system addresses the shortcomings of report-only metrics, which often miss under-reported harms and lack statistical power.
- •It utilizes ML-assisted sampling from daily user impressions, leveraging production risk scores for efficiency while ensuring unbiased prevalence estimates.
- •A multimodal LLM (vision + text) enables bulk labeling of sampled content, significantly reducing latency and cost compared to human review.
- •Inverse-probability weighting ensures unbiased, design-consistent prevalence metrics, decoupling measurement from enforcement model thresholds.
- •Continuous calibration, human validation, and periodic checks against SME-labeled gold sets maintain LLM accuracy and detect model drift.
- •The system provides daily, statistically powered insights for faster interventions and effective content safety tracking.
Why it matters: This article demonstrates a practical approach to de-biasing recommendation systems by integrating direct user feedback via surveys into ML model training. Engineers can learn how to move beyond pure engagement metrics to build more user-centric and high-quality content platforms.
- •Pinterest implemented in-app Pinner surveys to gather direct user feedback on content visual quality, moving beyond traditional engagement metrics.
- •The survey design collected at least 10 ratings per image for 5k Pins across diverse interest verticals, averaging scores to ensure data reliability and reduce subjectivity.
- •A machine learning model was trained using this aggregated survey data, mapping image embedding features to a single score (0-1) indicating perceived visual quality.
- •This ML model is integrated into Pinterest's core recommendation systems, including Homefeed, Related Pins, and Search, to promote higher quality content.
- •The approach aims to de-bias recommendation systems, prevent the promotion of low-quality "clickbait," and align content delivery with user well-being and satisfaction.
Why it matters: GitHub Copilot Spaces significantly reduces the time engineers spend hunting for context during debugging by providing AI with project-specific knowledge. This leads to faster, more accurate solutions and streamlined development workflows.
- •GitHub Copilot Spaces enhances AI debugging by providing project-specific context like files, pull requests, and issues, leading to more accurate suggestions.
- •Spaces act as dynamic knowledge bundles, automatically syncing with linked content to ensure Copilot always has up-to-date information.
- •Users create a space, add relevant project assets (e.g., security docs, architecture overviews, specific issues), and define custom instructions for Copilot's behavior.
- •Copilot leverages this curated context to generate detailed debugging plans and propose code changes, citing its sources for transparency and auditability.
- •The integrated coding agent can then create pull requests with before/after versions, explanations, and references to the guiding instructions and files.