**A/B Testing**

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Deploying a new machine learning model to production is one of the most critical stages of the ML lifecycle. Even if a model performs well on validation and test datasets, directly replacing the existing production model can be risky. Offline evaluation rarely captures the full complexity of real-world environments—data distributions may shift, user behavior can change, and system constraints in production may differ from those in controlled experiments.

As a result, a model that appears superior during development might still degrade performance or negatively impact user experience once deployed. To mitigate these risks, ML teams adopt controlled rollout strategies that allow them to evaluate new models under real production conditions while minimizing potential disruptions.

In this article, we explore four widely used strategies—A/B testing, Canary testing, Interleaved testing, and Shadow testing—that help organizations safely deploy and validate new machine learning models in production environments.

A/B Testing

A/B testing is one of the most widely used strategies for safely introducing a new machine learning model in production. In this approach, incoming traffic is split between two versions of a system: the existing legacy model (control) and the candidate model (variation). The distribution is typically non-uniform to limit risk—for example, 90% of requests may continue to be served by the legacy model, while only 10% are routed to the candidate model.

By exposing both models to real-world traffic, teams can compare downstream performance metrics such as click-through rate, conversions, engagement, or revenue. This controlled experiment allows organizations to evaluate whether the candidate model genuinely improves outcomes before gradually increasing its traffic share or fully replacing the legacy model.

Canary Testing

Canary testing is a controlled rollout strategy where a new model is first deployed to a small subset of users before being gradually released to the entire user base. The name comes from an old mining practice where miners carried canary birds into coal mines to detect toxic gases—the birds would react first, warning miners of danger. Similarly, in machine learning deployments, the candidate model is initially exposed to a limited group of users while the majority continue to be served by the legacy model.

Unlike A/B testing, which randomly splits traffic across all users, canary testing targets a specific subset and progressively increases exposure if performance metrics indicate success. This gradual rollout helps teams detect issues early and roll back quickly if necessary, reducing the risk of widespread impact.

Interleaved Testing

Interleaved testing evaluates multiple models by mixing their outputs within the same response shown to users. Instead of routing an entire request to either the legacy or candidate model, the system combines predictions from both models in real time. For example, in a recommendation system, some items in the recommendation list may come from the legacy model, while others are generated by the candidate model.

The system then logs downstream engagement signals—such as click-through rate, watch time, or negative feedback—for each recommendation. Because both models are evaluated within the same user interaction, interleaved testing allows teams to compare performance more directly and efficiently while minimizing biases caused by differences in user groups or traffic distribution.

Shadow Testing

Shadow testing, also known as shadow deployment or dark launch, allows teams to evaluate a new machine learning model in a real production environment without affecting the user experience. In this approach, the candidate model runs in parallel with the legacy model and receives the same live requests as the production system. However, only the legacy model’s predictions are returned to users, while the candidate model’s outputs are simply logged for analysis.

This setup helps teams assess how the new model behaves under real-world traffic and infrastructure conditions, which are often difficult to replicate in offline experiments. Shadow testing provides a low-risk way to benchmark the candidate model against the legacy model, although it cannot capture true user engagement metrics—such as clicks, watch time, or conversions—since its predictions are never shown to users.

Simulating ML Model Deployment Strategies

Setting Up

Before simulating any strategy, we need two things: a way to represent incoming requests, and a stand-in for each model.

Each model is simply a function that takes a request and returns a score — a number that loosely represents how good that model’s recommendation is. The legacy model’s score is capped at 0.35, while the candidate model’s is capped at 0.55, making the candidate intentionally better so we can verify that each strategy actually detects the improvement.

make_requests() generates 200 requests spread across 40 users, which gives us enough traffic to see meaningful differences between strategies while keeping the simulation lightweight.

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A/B Testing

ab_route() is the core of this strategy — for every incoming request, it draws a random number and routes to the candidate model only if that number falls below 0.10, otherwise the request goes to legacy. This gives the candidate roughly 10% of traffic.

We then collect the prediction scores from each model separately and compute the average at the end. In a real system, these scores would be replaced by actual engagement metrics like click-through rate or watch time — here the score just stands in for “how good was this recommendation.”

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Canary Testing

The key function here is get_canary_users(), which uses an MD5 hash to deterministically assign users to the canary group. The important word is deterministic — sorting users by their hash means the same users always end up in the canary group across runs, which mirrors how real canary deployments work where a specific user consistently sees the same model.

We then simulate three phases by simply expanding the fraction of canary users — 5%, 20%, and 50%. For each request, routing is decided by whether the user belongs to the canary group, not by a random coin flip like in A/B testing. This is the fundamental difference between the two strategies: A/B testing splits by request, canary testing splits by user.

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Interleaved Testing

Both models run on every request, and interleave() merges their outputs by alternating items — one from legacy, one from candidate, one from legacy, and so on. Each item is tagged with its source model, so when a user clicks something, we know exactly which model to credit.

The small random.uniform(-0.05, 0.05) noise added to each item’s score simulates the natural variation you’d see in real recommendations — two items from the same model won’t have identical quality.

At the end, we compute CTR separately for each model’s items. Because both models competed on the same requests against the same users at the same time, there is no confounding factor — any difference in CTR is purely down to model quality. This is what makes interleaved testing the most statistically clean comparison of the four strategies.

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Shadow Testing

Both models run on every request, but the loop makes a clear distinction — live_pred is what the user gets, shadow_pred goes straight into the log and nothing more. The candidate’s output is never returned, never shown, never acted on. The log list is the entire point of shadow testing. In a real system this would be written to a database or a data warehouse, and engineers would later query it to compare latency distributions, output patterns, or score distributions against the legacy model — all without a single user being affected.

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Arham Islam

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I am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.

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Arham Islam

How to Create a Custom Model Context Protocol (MCP) Client Using Gemini

Arham Islam

Implementing Persistent Memory Using a Local Knowledge Graph in Claude Desktop

Arham Islam

Step by Step Guide on How to Convert a FastAPI App into an MCP Server

Arham Islam

Integrating Figma with Cursor IDE Using an MCP Server to Build a Web Login Page

Arham Islam

Code Implementation to Building a Model Context Protocol (MCP) Server and Connecting It with Claude Desktop

Arham Islam

40+ Cool AI Tools You Should Check Out (Oct 2024)

Arham Islam

Pinterest Researchers Present an Effective Scalable Algorithm to Improve Diffusion Models Using Reinforcement Learning (RL)

Arham Islam

Meta AI Researchers Open-Source Pearl: A Production-Ready Reinforcement Learning AI Agent Library

Arham Islam

Researchers from the University of Texas Showcase Predicting Implant-Based Reconstruction Complications Using Machine Learning

Arham Islam

UC Berkeley Researchers Propose an Artificial Intelligence Algorithm that Achieves Zero-Shot Acquisition of Goal-Directed Dialogue Agents

Arham Islam

Can Language Models Reason Beyond Words? Exploring Implicit Reasoning in Multi-Layer Hidden States for Complex Tasks

Arham Islam

Meta Researchers Introduced VR-NeRF: An Advanced End-to-End AI System for High-Fidelity Capture and Rendering of Walkable Spaces in Virtual Reality

Arham Islam

Are You Doing Retrieval-Augmented Generation (RAG) for Biomedicine? Meet MedCPT: A Contrastive Pre-trained Transformer Model for Zero-Shot Biomedical Information Retrieval

Arham Islam

Intel Researchers Propose a New Artificial Intelligence Approach to Deploy LLMs on CPUs More Efficiently

Arham Islam

This AI Paper Unveils DiffEnc: Advancing Diffusion Models for Enhanced Generative Performance

Arham Islam

A New AI Research from China Introduces GLM-130B: A Bilingual (English and Chinese) Pre-Trained Language Model with 130B Parameters

Arham Islam

Unlocking the Secrets of CLIP’s Data Success: Introducing MetaCLIP for Optimized Language-Image Pre-training

Arham Islam

Researchers from the University of Washington and Princeton Present a Pre-Training Data Detection Dataset WIKIMIA and a New Machine Learning Approach MIN-K% PROB

Arham Islam

50+ New Cutting-Edge Artificial Intelligence AI Tools (November 2023)

Arham Islam

List of Artificial Intelligence AI Advancements by Non-Profit Researchers

Arham Islam

Revolutionizing Language Model Fine-Tuning: Achieving Unprecedented Gains with NEFTune’s Noisy Embeddings

Arham Islam

A New AI Research from China Proposes 4K4D: A 4D Point Cloud Representation that Supports Hardware Rasterization and Enables Unprecedented Rendering Speed

Arham Islam

This AI Paper Proposes ‘MotionDirector’: An Artificial Intelligence Approach to Customize Video Motion and Appearance

Arham Islam

From 2D to 3D: Enhancing Text-to-3D Generation Consistency with Aligned Geometric Priors

Arham Islam

Google AI Introduces SANPO: A Multi-Attribute Video Dataset for Outdoor Human Egocentric Scene Understanding

Arham Islam

This AI Research Proposes Kosmos-G: An Artificial Intelligence Model that Performs High-Fidelity Zero-Shot Image Generation from Generalized Vision-Language Input Leveraging the property of Multimodel LLMs

Arham Islam

Latest Advancements in the Field of Multimodal AI: (ChatGPT + DALLE 3) + (Google BARD + Extensions) and many more….

Arham Islam

What is Model Merging?

Arham Islam

LLMs & Knowledge Graphs

Arham Islam

LLMs and Data Analysis: How AI is Making Sense of Big Data for Business Insights

Arham Islam

Role of Data Contracts in Data Pipeline

Arham Islam

40+ AI Tools For Video Creation and Editing in 2023

Arham Islam

Artificial Intelligence (AI) and Web3: How are they Connected?

Arham Islam

LLMs Outperform Reinforcement Learning- Meet SPRING: An Innovative Prompting Framework for LLMs Designed to Enable in-Context Chain-of-Thought Planning and Reasoning

Arham Islam

52 AI Tools For Sales Professionals (2023)

Arham Islam

Use of Analog Computers in Artificial Intelligence (AI)

Arham Islam

A New AI Research Presents A Prompt-Centric Approach For Analyzing Large Language Models LLMs Capabilities

Arham Islam

Multimodal Language Models: The Future of Artificial Intelligence (AI)

Arham Islam

Top 50+ AI Coding Assistant Tools in 2023

Arham Islam

List of Groundbreaking and Open-Source Conversational AI Models in the Language Domain

Arham Islam

Application of Large Language Models in Biotechnology and Pharmaceutical Research

Arham Islam

Top 50+ AI Tools for Marketers 2023

Arham Islam

The Groundbreaking Influence of Generative AI in the Automotive Industry

Arham Islam

What is Field Programmable Gate Array (FPGA): FPGA vs. GPU for Artificial Intelligence (AI)

Arham Islam

10 Use Cases of ChatGPT in Marketing for 2023

Arham Islam

Meet AIAgent: A Web-based AutomateGPT that Needs No API Keys and is Powered by GPT4

Arham Islam

Exploring the Benefits and Drawbacks of Integrating ChatGPT into Healthcare

Arham Islam

How To Use Third-Party Plugins In ChatGPT? 80+ Plugins Just Added by ChatGPT For Public

Arham Islam

Google Just Announced “Help Me Write” Feature in Gmail: AI Creates An Email With Just One Line Prompt

Arham Islam

7 AI Tools that Transform Anything into Interactive Chatbots

Arham Islam

Meet Window AI: A New Way To Use Your Own AI Models On The Web – Including Local Ones

Arham Islam

12 Creative Ways Developers Can Use Chat GPT-4

Arham Islam

A History of Generative AI: From GAN to GPT-4

Arham Islam

Roadmap of Becoming a Prompt Engineer (2023)

Arham Islam

What is ChatGPT? Technology Behind ChatGPT

Arham Islam

Top Large Language Models (LLMs) in 2023 from OpenAI, Google AI, Deepmind, Anthropic, Baidu, Huawei, Meta AI, AI21 Labs, LG AI Research and NVIDIA

Arham Islam

A New Prompt Engineering Research Proposes PEZ (Prompts Made Easy): A Gradient Optimizer For Text That Utilizes Continuous Embeddings To Reliably Optimize Hard Prompts

Arham Islam

5 GANs Concepts You Should Know About in 2023

Arham Islam

What are Transformers? Concept and Applications Explained

Arham Islam

Best Practices For Machine Learning Model Monitoring

Arham Islam

Artificial Intelligence (AI) Research Innovations in 2022 from Google, NVIDIA, Salesforce, Meta, Apple, Amazon, and AI2

Arham Islam

Bad Data Engineering Practices And How To Avoid Them

Arham Islam

What is Multimodal Learning? Some Applications

Arham Islam

High-Performance Computing (HPC) And Artificial Intelligence (AI)

Arham Islam

What is Dataops (Data Operations)? Difference between DataOps and DevOps

Arham Islam

How Do DALL·E 2, Stable Diffusion, and Midjourney Work?

Arham Islam

What is AIOps (Artificial Intelligence for IT Operations)?AIOps Use Cases

Arham Islam

What is MLOps (Machine Learning Operations)? Why Do You Need MLOps for Machine Learning and Deep Learning Projects?

Arham Islam

AI Hardware Accelerators For Machine Learning And Deep Learning | How To Choose One

Arham Islam

Understanding the Role of Artificial Intelligence (AI) in Building Smart Cities and Top Startups Working on it

Arham Islam

Understanding The Artificial Intelligence (AI) Bill of Rights From The White House

Arham Islam

Top Real World Applications of Reinforcement Learning in 2022

[How to Build a QwenPaw Agent Workspace with Custom Skills, Model Providers, Console Access, and Streaming API Testing](/content/2026/06/13/how-to-build-a-qwenpaw-agent-workspace-with-custom-skills-model-providers-console-access-and-streaming-api-testing/ "How to Build a QwenPaw Agent Workspace with Custom Skills, Model Providers, Console Access, and Streaming API Testing"/index.html)

[Anthropic Disables Claude Fable 5 and Mythos 5 After US Government Order](/content/2026/06/13/anthropic-disables-claude-fable-5-and-mythos-5-after-us-government-order/ "Anthropic Disables Claude Fable 5 and Mythos 5 After US Government Order"/index.html)

[Moonshot AI Releases Kimi K2.7-Code: a Coding Model Reporting +21.8% on Kimi Code Bench v2 Over K2.6](/content/2026/06/12/moonshot-ai-releases-kimi-k2-7-code-a-coding-model-reporting-21-8-on-kimi-code-bench-v2-over-k2-6/ "Moonshot AI Releases Kimi K2.7-Code: a Coding Model Reporting +21.8% on Kimi Code Bench v2 Over K2.6"/index.html)

[A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric](/content/2026/06/12/a-coding-implementation-on-spatial-graph-neural-networks-for-urban-function-inference-using-city2graph-osmnx-and-pytorch-geometric/ "A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric"/index.html)

[Google Releases Gemini-SQL2: Gemini 3.1 Pro Text-to-SQL Scores 80.04% on BIRD Single-Model Leaderboard](/content/2026/06/12/google-releases-gemini-sql2-gemini-3-1-pro-text-to-sql-scores-80-04-on-bird-single-model-leaderboard/ "Google Releases Gemini-SQL2: Gemini 3.1 Pro Text-to-SQL Scores 80.04% on BIRD Single-Model Leaderboard"/index.html)

[Moonshot AI Launches Kimi Work, a Local Desktop Agent Reportedly Running on Kimi K2.6 With a 300-Sub-Agent Agent Swarm](/content/2026/06/12/moonshot-ai-launches-kimi-work-a-local-desktop-agent-reportedly-running-on-kimi-k2-6-with-a-300-sub-agent-agent-swarm/ "Moonshot AI Launches Kimi Work, a Local Desktop Agent Reportedly Running on Kimi K2.6 With a 300-Sub-Agent Agent Swarm"/index.html)

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[How to Build a QwenPaw Agent Workspace with Custom Skills, Model Providers, Console Access,...](/content/2026/06/13/how-to-build-a-qwenpaw-agent-workspace-with-custom-skills-model-providers-console-access-and-streaming-api-testing/ "How to Build a QwenPaw Agent Workspace with Custom Skills, Model Providers, Console Access, and Streaming API Testing"/index.html)

Sana Hassan-June 13, 20260

In this tutorial, we implement a QwenPaw workflow that provides a practical environment for building and testing an agent-powered assistant. We install and initialize...

Asif Razzaq-June 13, 20260

shutdown followed a US government export control directive citing national security authorities. All other Anthropic models, including Opus 4.8, remain available.

[Moonshot AI Releases Kimi K2.7-Code: a Coding Model Reporting +21.8% on Kimi Code Bench...](/content/2026/06/12/moonshot-ai-releases-kimi-k2-7-code-a-coding-model-reporting-21-8-on-kimi-code-bench-v2-over-k2-6/ "Moonshot AI Releases Kimi K2.7-Code: a Coding Model Reporting +21.8% on Kimi Code Bench v2 Over K2.6"/index.html)

Asif Razzaq-June 12, 20260

Moonshot AI has open-sourced Kimi K2.7-Code under a Modified MIT license. It is a coding-focused, agentic model built on Kimi K2.6, with a 256K context window and roughly 30% lower reasoning-token usage. Moonshot reports gains over K2.6 on six benchmarks, including +21.8% on Kimi Code Bench v2. The model is available via the Kimi API and Kimi Code.

[A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph,...](/content/2026/06/12/a-coding-implementation-on-spatial-graph-neural-networks-for-urban-function-inference-using-city2graph-osmnx-and-pytorch-geometric/ "A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric"/index.html)

Sana Hassan-June 12, 20260

We build an end-to-end spatial graph learning pipeline using city2graph. We collect urban POI and street network data from OpenStreetMap, with a synthetic fallback for reliability. We engineer spatial features, construct several proximity graph families, and compare how each represents the same urban environment. We then build heterogeneous and homogeneous graphs, convert them to PyTorch Geometric, and train a GraphSAGE model to predict POI categories from spatial structure.

Asif Razzaq-June 12, 20260

We look at Gemini-SQL2, the text-to-SQL capability Google Research announced on June 12, 2026. Powered by Gemini 3.1 Pro, it posted 80.04% execution accuracy on the BIRD single-model leaderboard. We explain what the score measures, how the leaderboard stacks up, and what Google has not yet disclosed. We also cover use cases and a schema-grounded implementation pattern.

[Moonshot AI Launches Kimi Work, a Local Desktop Agent Reportedly Running on Kimi K2.6...](/content/2026/06/12/moonshot-ai-launches-kimi-work-a-local-desktop-agent-reportedly-running-on-kimi-k2-6-with-a-300-sub-agent-agent-swarm/ "Moonshot AI Launches Kimi Work, a Local Desktop Agent Reportedly Running on Kimi K2.6 With a 300-Sub-Agent Agent Swarm"/index.html)

Asif Razzaq-June 12, 20260

Moonshot AI's Kimi Work is a local desktop agent for macOS and Windows. It runs a 300-sub-agent swarm, drives your logged-in browser via WebBridge, and schedules background jobs.

[Zyphra Release Zamba2-VL: Hybrid Mamba2–Transformer Vision-Language Models That Cut Time-to-First-Token by About an Order...](/content/2026/06/12/zyphra-release-zamba2-vl-hybrid-mamba2-transformer-vision-language-models-that-cut-time-to-first-token-by-about-an-order-of-magnitude/ "Zyphra Release Zamba2-VL: Hybrid Mamba2–Transformer Vision-Language Models That Cut Time-to-First-Token by About an Order of Magnitude"/index.html)

Asif Razzaq-June 12, 20260

Zyphra has released Zamba2-VL, a family of open vision-language models at 1.2B, 2.7B, and 7B parameters. The models use a hybrid Mamba2 state-space and Transformer backbone, shipping under Apache 2.0. They stay competitive with comparable Transformer VLMs while cutting time-to-first-token by about an order of magnitude.

[A Coding Implementation on MONAI for End-to-End 3D Spleen Segmentation Using UNet on Medical...](/content/2026/06/12/a-coding-implementation-on-monai-for-end-to-end-3d-spleen-segmentation-using-unet-on-medical-ct-volumes/ "A Coding Implementation on MONAI for End-to-End 3D Spleen Segmentation Using UNet on Medical CT Volumes"/index.html)

Sana Hassan-June 12, 20260

In this tutorial, we build an end-to-end 3D medical image segmentation pipeline using MONAI to segment the spleen on the Medical Segmentation Decathlon Task09...

[Perplexity Moves Deep Research Into Computer, Routing Research Subtasks Across 20+ Frontier Models For...](/content/2026/06/11/perplexity-moves-deep-research-into-computer-routing-research-subtasks-across-20-frontier-models-for-reports-decks-and-dashboards/ "Perplexity Moves Deep Research Into Computer, Routing Research Subtasks Across 20+ Frontier Models For Reports, Decks, And Dashboards"/index.html)

Michal Sutter-June 11, 20260

Deep Research now lives inside Perplexity Computer, breaking hard questions into subtasks and routing across 20+ frontier models.

[xAI Ships Grok Build Plugin Marketplace With MongoDB, Vercel, Sentry, Chrome DevTools, Cloudflare, and...](/content/2026/06/11/xai-ships-grok-build-plugin-marketplace-with-mongodb-vercel-sentry-chrome-devtools-cloudflare-and-superpowers-plugins-at-launch/ "xAI Ships Grok Build Plugin Marketplace With MongoDB, Vercel, Sentry, Chrome DevTools, Cloudflare, and Superpowers Plugins at Launch"/index.html)

Michal Sutter-June 11, 20260

Grok Build's in-terminal marketplace bundles skills, agents, hooks, and MCP servers, with commit-SHA verification on every remote plugin.

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A/B Testing

Canary Testing

Interleaved Testing

Shadow Testing

Simulating ML Model Deployment Strategies

Setting Up

A/B Testing

Canary Testing

Interleaved Testing

Shadow Testing

Arham Islam

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