Data Science in the Wild

Google's WeatherNext 3 refreshes global AI weather forecasts every hour instead of every six by ingesting live satellite imagery, at up to 5 km resolution. We cover who that gap costs, how the model works, and what the precipitation numbers really mean.

Key takeaways:

• Hourly, not six-hourly. WeatherNext 3 ingests live hourly geostationary satellite mosaics, so each new forecast starts from fresh observations. WeatherNext 2 ran on a 25 km grid in 6-hour steps.
• One generative model. A single Functional Generative Network (FGN) mesh transformer outputs a distribution of possible futures rather than one deterministic forecast.
• Sharper maps. Surface temperature and moisture come at 5 km, roughly five times sharper than the previous model.
• Read the rain stat carefully. Google reports up to 60% better precipitation scores (CRPS) than baselines when graded against NASA's IMERG data, at early lead times. IMERG is the yardstick, not the rival.
• Open questions. The announcement gives no parameter counts, so it's hard to separate architecture gains from scale.

Chapters:

00:00 Your weather app's six-hour-old forecast

01:33 Why a six-hour gap is costly

04:38 Live satellite data, every hour

06:11 One mesh transformer for everything

07:42 Outputs and 5 km resolution

09:14 Precipitation and IMERG

10:45 Who benefits

11:31 How novel is it?

12:17 Open questions

13:02 GraphCast and other weather models

14:33 Closing thought

Sources:

• Introducing WeatherNext 3, our most advanced and accurate global weather AI model (Google DeepMind and Google Research, Sep 2026)

Related episode: E11: NVIDIA — One Checkpoint, Three Jobs for Robots

Enjoyed it? Follow Data Science in the Wild (one real production ML/AI system or paper per episode, hosted by Peter Liu) and rate it on Apple Podcasts or Spotify.

Also from Peter: The GenAI Evolution Atlas, Wednesday AI deep dives and Friday news roundups.

Made with AI-generated voices; curated by Peter Liu from the sources above.

Instacart's generative Shopping Hub: instead of a static, hand-built content library, an LLM pipeline designs each shopper's page top-down, grounds it in the real catalog with RAG, and checks it with LLM judges and a cheap fine-tuned classifier.

Key takeaways:

• Themes first, products second. An agent uses purchase history to design personalized page themes; products are found afterward. Asking an LLM to pick items directly runs into latency and a catalog that changes by the minute.
• Ground it in the catalog. Retrieval keywords are restricted to real catalog terms via embedding search, cutting generation cost 15–20% per generation.
• Cheap judges at scale. LLM judges audit cohesion, diversity and brand fit, while a fine-tuned DeBERTa relevance classifier costs over 99% less than closed-weight LLM inference, so it can filter every placement.
• Keep what works. Generated placements feed Instacart's existing ranking systems rather than replacing them.
• Results are early. Offline evaluations beat the baseline, but the post calls A/B results only “quite promising,” with no lift numbers.

Chapters:

00:00 A store that rebuilds itself for you

01:32 Instacart's generative Shopping Hub

02:17 Why static placements stopped scaling

05:18 Why not let the LLM pick products?

07:34 Phase 1: an agent designs page themes

08:20 Phase 2: catalog-grounded keywords

10:35 Phase 3: LLM judges and a cheap classifier

13:38 Phase 4: existing ranking systems

15:55 What the results do and don't show

16:41 How Netflix and others compare

20:31 Recap and closing question

Sources:

• Our Early Journey to Transform Instacart's Discovery Recommendations with LLMs (Moein Hasani et al., Instacart Tech, Feb 2026)

Related episode: E12: Netflix — One Model, One Pass, Your Whole Homepage

Enjoyed it? Follow Data Science in the Wild (one real production ML/AI system or paper per episode, hosted by Peter Liu) and rate it on Apple Podcasts or Spotify.

Also from Peter: The GenAI Evolution Atlas, Wednesday AI deep dives and Friday news roundups.

Made with AI-generated voices; curated by Peter Liu from the sources above.

Shopify's Sidekick continual learning loop: a calibrated LLM judge, validated against A/B tests, becomes the reward for fine-tuning a smaller model. For a GraphQL agent at 2,000 requests/min, Shopify estimates serving cost falls from ~$27M to ~$1M a year.

Key takeaways:

• Calibrate humans first. Two expert annotators blind-score samples against a rubric; their agreement (Cohen's kappa) sets the ceiling for any automated judge.
• Then calibrate the judge. An LLM judge is tuned with DSPy and reflection-based optimizers (GEPA, Agentic Context Engineering) and trusted only once it tracks real A/B outcomes.
• Turn failures into weights. Production failures feed SFT plus GRPO fine-tuning, with the calibrated judge as the reward.
• The payoff is an estimate. Shopify estimates frontier-model serving at ~$27M a year versus closer to $1M for the fine-tuned model, a 96% cut. Gisting also compresses the long system prompt.
• The lesson. A judge used as a reward gets gamed unless it's anchored to real user outcomes first.

Chapters:

00:00 A $27M bill and a $1M fix

01:01 Shopify's Sidekick continual learning loop

02:03 The frontier-model trap

04:04 Stage 1: a human-calibrated rubric

06:07 Stage 2: an LLM judge tuned with DSPy

09:14 Validating the judge against A/B tests

10:15 Stage 3: automated prompt and tool research

11:16 Stage 4: SFT and GRPO fine-tuning

13:17 The cost math

14:19 What's new: judge before reward

17:22 Gisting and task routing

20:24 Recap and closing question

Sources:

• Sidekick's continual learning loop (Andrew McNamara and Cody Mazza-Anthony, Shopify Engineering, Aug 2026)

Related episode: E6: Shopify — An AI Agent That Verifies Its Own Work

Enjoyed it? Follow Data Science in the Wild (one real production ML/AI system or paper per episode, hosted by Peter Liu) and rate it on Apple Podcasts or Spotify.

Also from Peter: The GenAI Evolution Atlas, Wednesday AI deep dives and Friday news roundups.

Made with AI-generated voices; curated by Peter Liu from the sources above.

LinkedIn feed ranking with a transformer: Feed SR reads up to 1,000 of a member's past impressions in order and replaced the DCNv2 ranker for 1.2 billion members, lifting time spent 2.10% and likes, comments and reshares 3.52%.

Key takeaways:

• Sequence beats snapshot. Feed SR is a decoder-only transformer over a member's chronological impression history (up to 1,000 items), something the feature-cross DCNv2 ranker couldn't model.
• Fewer features, not more. Feed SR uses about 80% fewer features than the old ranker, and the removed ones added no AUC.
• Serving was the hard part. A CPU/GPU split, history parsing cut from 450 ms to 2 ms (225×), and shared-context batching that scores many candidates against one encoded history made it fit latency budgets.
• Bigger model, less energy. Per item, Feed SR uses about 0.2× the training energy and 0.7× the inference energy of the old system.
• Honest negatives. LinkedIn also tried LLM-based rankers and HSTU layers; HSTU lost 0.23% at matched compute.

Chapters:

00:00 Why order matters: guessing a favorite movie

01:01 Feed SR and LinkedIn's 1.2B-member feed

02:01 The old DCNv2 ranker and its blind spot

04:02 Why replace a ranker that works?

06:08 RoPE and 1,000-item histories

08:08 Cutting about 80% of features

09:08 CPU/GPU serving and custom kernels

12:09 Bigger model, less energy

13:10 Shared-context batching

15:13 What didn't work: LLM rankers and HSTU

18:15 Where the gains landed

20:15 Recap and closing question

Sources:

• An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking (Hertel et al., LinkedIn; arXiv, Feb 2026)

Related episode: E7: Airbnb — A Transformer That Reads Your Whole Travel History

Enjoyed it? Follow Data Science in the Wild (one real production ML/AI system or paper per episode, hosted by Peter Liu) and rate it on Apple Podcasts or Spotify.

Also from Peter: The GenAI Evolution Atlas, Wednesday AI deep dives and Friday news roundups.

Made with AI-generated voices; curated by Peter Liu from the sources above.