CIOs must rethink talent, processes, and technology to lead in the AI era: https://t.co/YFHIpbE1vn
Join our Gartner session to learn about key decisions, pitfalls to avoid, and a roadmap for long-term success.
#GartnerIT#CIO#AI#ITStrategy
🛢️ The Journey of Oil & Gas
🔍 Upstream — Exploration, drilling, and crude extraction
🚛 Midstream — Pipeline transport, tankers, and LNG storage
⛽ Downstream — Refining, marketing, and final distribution
🧪 Key Products — Gasoline, Diesel, Jet Fuel, Plastics
From deep-well extraction to the fuel in your tank, the energy lifecycle is a complex three-stage global operation.
Enterprise architecture is the blueprint for long-term value creation.
When modernizing enterprise IT, tech leaders face a fundamental choice: evolve incrementally or transform at scale. Each path comes with distinct benefits and risks. https://t.co/GDN9fLMS89
If you're serious about SYSTEM DESIGN (in 2026), learn these 12 case studies:
1. How Google Docs Works
↳ https://t.co/W57IkAjXpT
2. How Spotify Works
↳ https://t.co/BxrH3oHIFS
3. How Reddit Works
↳ https://t.co/o6Pw2hhj3T
4. How Bluesky Works
↳ https://t.co/2rLYlRlky0
5. How ChatGPT Works
↳ https://t.co/5lCKxq2g4N
6. How Kafka Works
↳ https://t.co/8rOy9KgCMo
7. How Slack Works
↳ https://t.co/eIo29uOQOJ
8. How Meta Handles 11.5M Serverless Function Calls per Second:
↳ https://t.co/NSt6jovxu5
9. How Uber Finds Nearby Drivers
↳ https://t.co/kJ2t8dtmch
10. How Twitter Timeline Works
↳ https://t.co/pF2RYmPaIG
11. How YouTube Was Able to Support 2.49 Billion Users With MySQL
↳ https://t.co/4VDJ5cs6fL
12 How to Scale an App to 10 Million Users on AWS
↳ https://t.co/RozCGli0r8
(What else should make this list?)
——
👋 PS - Want my System Design Playbook for free?
Join my newsletter with 200K+ software engineers:
→ https://t.co/ByOFTtOihX
———
💾 Save this for later, and RT it to help others master system design.
👤 Follow @systemdesignone + turn on notifications.
📄 𝗙𝗿𝗼𝗺 𝗣𝗮𝗽𝗲𝗿 𝘁𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲:
A new 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗔𝗜 𝗣𝗮𝗽𝗲𝗿 shows end-to-end 𝗔𝗴𝗲𝗻𝘁𝘀 from data to reasoning and I’ll show you how you 𝗯𝘂𝗶𝗹𝗱 𝗶𝘁 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳. 👇
𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱:
In hospitals, data comes as notes, scans, and spreadsheets — fragmented and messy.
You’ll build an AI Agent system that turns raw hospital data into clean, private, interpretable predictions.
Powered by cooperating agents — no manual work.
Fast. Transparent. Scalable.
𝗦𝘁𝗲𝗽𝘀:
【1】 Ingestion Identifier Agent
Checks incoming data: spreadsheet, clinical note, or scan image.
Auto-detects type so the right workflow starts.
→ Think: file scanning + MIME detection + structured-data check.
→ Framework: implement inside an Ingestion Agent with CrewAI or LangGraph.
【2】 Data Anonymizer Agent
Privacy first: removes names, IDs, record numbers.
Delivers fully anonymized data ready for analysis.
→ Think: regex filters + hashing + redaction layer.
→ Framework: a local PrivacyAgent for text & image anonymization.
【3】Feature Extraction Agent
Understands what the data means.
Tables → headers like Age, Gender, Diagnosis.
Images → scan type & condition (e.g., colonoscopy → polyp).
→ Think: header parsing + embedding similarity + lightweight vision labeling.
→ Framework: MetadataAgent with pandas (tables) or simple vision modules (scans).
【4】 Model–Data Feature Matcher Agent
Chooses the best model automatically.
Text → LLM for reading/reasoning.
Tabular → structured model.
Imaging → VLM for medical scans.
→ Think: feature matching + routing logic + model registry.
→ Framework: ModelSelectorAgent that switches between LLMs, VLMs, or structured models dynamically.
【5】 Preprocessing Recommender & Implementor Agents
Recommend and apply cleaning/normalization before inference.
Tables scaled, missing values filled; images resized.
Everything optimized for the selected model.
→ Think: pandas.fillna(), StandardScaler(), torchvision.transforms.
→ Framework: PreprocessorAgent linking cleaning steps to model metadata.
【6】 Model Inference Agent (with Explainability)
Runs the model and explains decisions.
Predicts (e.g., detect a polyp, estimate risk) and shows why (features or image regions).
→ Think: SHAP values + attention heatmaps + local explanations.
→ Framework: ExplainabilityAgent connected to model outputs.
🏆 In my AI Agents Mastery training,
I teach you to build systems like this—step by step.
Paper: https://t.co/KLiuT9GihG
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⫸ꆛ Want to build Real-World AI Agents?
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