Empowering the next generation of data professionals 🚀 • AI/Data Engineer • Building @wisabiHQ

Joined September 2019
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Do you want to become a world-class data analyst in just 6 months? I’ve been there, and I know how overwhelming it can be to learn data analysis from scratch. That’s why I’ve created a 6-month roadmap of free high-quality resources that will teach you everything you need to know, from statistics and Excel to Python and cloud computing. In this thread, I’ll share with you my ultimate roadmap and how you can download it for free. Let’s get started! 👇
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Yesterday at PyData Leeds I shared a simple idea: The cheapest AI inference is the one you never make. Instead of sending every document to an LLM, process locally first and escalate only uncertain cases. The result: ✅ Lower cost ✅ Lower latency ✅ Better reliability Thanks to everyone who attended the session on Local-First AI. Great questions and discussions afterwards. 🚀 #PyData #Python #AIEngineering #LLM #MLOps
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Obinna Iheanachor retweeted
Spoke at @JumpingRivers’ AI in Production conference today Production AI. Trust by design. Great room. Insightful questions. Fantastic experience overall!
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Obinna Iheanachor retweeted
Spoke at Leeds Data Science meetup last night - From £8,000 to £40: What I Learned Shipping AI in a UK Engineering Company. 4,700 engineering drawings. 4 weeks of manual work → 45 minutes. The model was the least interesting part. Sharp room, great questions. Thanks @jumping_uk for the invite
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Obinna Iheanachor retweeted
Ahead of the May 7 elections in the UK, @DataSenseiObi continues his analysis and scenario modeling series, focusing on calibrated uncertainty, historical error, and why some models are most useful when they refuse to forecast. towardsdatascience.com/when-…
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Obinna Iheanachor retweeted
Hey #datafam Ahead of the UK local elections tomorrow, I just published a Tableau scenario dashboard for the 2026 cycle. Finding: the strongest modelled shock is only 13% of the median uncertainty band. Scenarios sit inside the noise. public.tableau.com/app/profi…
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Obinna Iheanachor retweeted
For his latest deep dive, @DataSenseiObi presents a data-quality case study on English local elections, covering categorical normalisation, metric validation, and why raw labels should never define analytical groups. towardsdatascience.com/fract…
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Obinna Iheanachor retweeted
I got it wrong. A bug flipped my entire result: fragmentation didn’t rise in 66/67 councils. It rose in just 18. What actually changed? Voter churn doubled. The party system didn’t fragment. @TDataScience towardsdatascience.com/fract… @tableau public.tableau.com/app/profi…
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Obinna Iheanachor retweeted
Follow along @DataSenseiObi's thorough project walkthrough to learn how a hybrid PyMuPDF GPT-4 Vision pipeline replaced £8,000 in manual engineering effort, and why the latest models weren’t the answer. towardsdatascience.com/from-…
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Obinna Iheanachor retweeted
I replaced £8,000 of manual work with ~$15 in API calls. 4,700 PDFs. 45 minutes. 96% accuracy. Published on @TDataScience 👇 towardsdatascience.com/from-… Here’s the architecture (and why GPT-5 wasn’t the answer) 🧵
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Obinna Iheanachor retweeted
This article is very well explained. As someone new to the field, I was able to clearly understand the problem the approach solves. I especially appreciated the discussion around why different approaches were chosen and how they align with stakeholder needs.
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What actually broke the system: → revision tables mistaken for current values → grid letters (A, B, C) read as revisions → PDFs rotated with incorrect metadata None of this showed up in small tests.
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If you’ve built extraction pipelines: What edge case broke yours at scale?
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