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1000 results for “Sarah_Lea”
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Have you heard "it works on my machine"? Enter: containers. Learn how Docker, Inc ensures consistent ML models, data pipelines, and environments across any system in this article :blobcoffee: https://towardsdatascience.com/why-data-scientists-should-care-about-containers-and-stand-out-with-this-knowledge/
#docker #container #kubernetes #datascientist #dataengineering #dataengineers #datascience #virtualmachines
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From Delivery to Smart Cities: Learn Pygame Simulation Basics :blobcoffee:
Try out a simple project with pygame to simplify more complex situations: https://medium.com/pythoneers/from-delivery-to-smart-cities-learn-pygame-simulation-basics-5b9cffcfe5f7
If you don't have the paid Medium version: https://open.substack.com/pub/sarahleaschrch/p/from-delivery-to-smart-cities-learn?utm_source=share&utm_medium=android&r=3khq41
#python #programming #beginnersguide #pygame #smartcity #parcelservice #energygrid
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🚀 Exciting news!
I'm working on "Terraform for Ops: Automating Infrastructure Tasks" 📘.
This book is your guide to mastering Terraform and streamlining IT operations.
Sign up for updates and be the first to know when it's out! 👉 https://leanpub.com/terraformforops
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Regex vs. LLM for B2B document extraction. This week, I tried out both.
:blobcoffee: The rule-based pipeline with pytesseract + regex worked perfectly for Layout A. For Layout B? Every single field returned None.
:blobcoffee: Because "PO Number" and "Order Reference" are the same thing for a human. Not for a regex pattern.
:blobcoffee: The LLM-based approach (pytesseract + Ollama + LLaMA 3) extracted both layouts correctly, without touching a single rule. It even normalized the date format automatically.
:blobcoffee: But LLMs aren't always the right answer. If your documents are stable, speed matters at scale, or explainability is required, regex might still win.
Full comparison with code and trade-off breakdown on TDS: https://shorturl.at/v4gdl
#Python #DataScience #business #technology #dataengineering #LLM #Automation #OCR
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Regex vs. LLM for B2B document extraction. This week, I tried out both.
:blobcoffee: The rule-based pipeline with pytesseract + regex worked perfectly for Layout A. For Layout B? Every single field returned None.
:blobcoffee: Because "PO Number" and "Order Reference" are the same thing for a human. Not for a regex pattern.
:blobcoffee: The LLM-based approach (pytesseract + Ollama + LLaMA 3) extracted both layouts correctly, without touching a single rule. It even normalized the date format automatically.
:blobcoffee: But LLMs aren't always the right answer. If your documents are stable, speed matters at scale, or explainability is required, regex might still win.
Full comparison with code and trade-off breakdown on TDS: https://shorturl.at/v4gdl
#Python #DataScience #business #technology #dataengineering #LLM #Automation #OCR
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Regex vs. LLM for B2B document extraction. This week, I tried out both.
:blobcoffee: The rule-based pipeline with pytesseract + regex worked perfectly for Layout A. For Layout B? Every single field returned None.
:blobcoffee: Because "PO Number" and "Order Reference" are the same thing for a human. Not for a regex pattern.
:blobcoffee: The LLM-based approach (pytesseract + Ollama + LLaMA 3) extracted both layouts correctly, without touching a single rule. It even normalized the date format automatically.
:blobcoffee: But LLMs aren't always the right answer. If your documents are stable, speed matters at scale, or explainability is required, regex might still win.
Full comparison with code and trade-off breakdown on TDS: https://shorturl.at/v4gdl
#Python #DataScience #business #technology #dataengineering #LLM #Automation #OCR
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Regex vs. LLM for B2B document extraction. This week, I tried out both.
:blobcoffee: The rule-based pipeline with pytesseract + regex worked perfectly for Layout A. For Layout B? Every single field returned None.
:blobcoffee: Because "PO Number" and "Order Reference" are the same thing for a human. Not for a regex pattern.
:blobcoffee: The LLM-based approach (pytesseract + Ollama + LLaMA 3) extracted both layouts correctly, without touching a single rule. It even normalized the date format automatically.
:blobcoffee: But LLMs aren't always the right answer. If your documents are stable, speed matters at scale, or explainability is required, regex might still win.
Full comparison with code and trade-off breakdown on TDS: https://shorturl.at/v4gdl
#Python #DataScience #business #technology #dataengineering #LLM #Automation #OCR
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Regex vs. LLM for B2B document extraction. This week, I tried out both.
:blobcoffee: The rule-based pipeline with pytesseract + regex worked perfectly for Layout A. For Layout B? Every single field returned None.
:blobcoffee: Because "PO Number" and "Order Reference" are the same thing for a human. Not for a regex pattern.
:blobcoffee: The LLM-based approach (pytesseract + Ollama + LLaMA 3) extracted both layouts correctly, without touching a single rule. It even normalized the date format automatically.
:blobcoffee: But LLMs aren't always the right answer. If your documents are stable, speed matters at scale, or explainability is required, regex might still win.
Full comparison with code and trade-off breakdown on TDS: https://shorturl.at/v4gdl
#Python #DataScience #business #technology #dataengineering #LLM #Automation #OCR
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Formatting in Word stole hours of my thesis work. So I built a different process for long documents.
:blobcoffee: OneNote as the thinking hub.
:blobcoffee: OneLatex as the translator as it turns the notebook into a clean, formatted PDF automatically.My new article: a 7-step workflow + a free OneNote template.
:blobcoffee: 👉 http://bit.ly/4e4y7n4
#business #it #writing #productivity #thesis #onenote #word #latex #technology #student
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Most ML issues are not model problems. They are data problems.
I retrained the same churn model twice.
Same code. Same path to the data.
Different result.Why? Because of mutable data references.
:blobcoffee: I wrote a small Data Lake vs Data Lakehouse demo showing why versioned data makes ML debugging reproducible: https://tinyurl.com/lake-vs-lakehouse-medium
:blobcoffee: Friend-Link: https://medium.com/towards-artificial-intelligence/from-data-lake-to-data-lakehouse-why-ai-changes-the-rules-for-data-platforms-c78feab48e1c?sk=405811cbc10baa4622bcfcad90736ed4
#ai #machinelearning #data #lakehouse #warehouse #python #datalake #technology #regression
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Most ML issues are not model problems. They are data problems.
I retrained the same churn model twice.
Same code. Same path to the data.
Different result.Why? Because of mutable data references.
:blobcoffee: I wrote a small Data Lake vs Data Lakehouse demo showing why versioned data makes ML debugging reproducible: https://tinyurl.com/lake-vs-lakehouse-medium
:blobcoffee: Friend-Link: https://medium.com/towards-artificial-intelligence/from-data-lake-to-data-lakehouse-why-ai-changes-the-rules-for-data-platforms-c78feab48e1c?sk=405811cbc10baa4622bcfcad90736ed4
#ai #machinelearning #data #lakehouse #warehouse #python #datalake #technology #regression
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Most ML issues are not model problems. They are data problems.
I retrained the same churn model twice.
Same code. Same path to the data.
Different result.Why? Because of mutable data references.
:blobcoffee: I wrote a small Data Lake vs Data Lakehouse demo showing why versioned data makes ML debugging reproducible: https://tinyurl.com/lake-vs-lakehouse-medium
:blobcoffee: Friend-Link: https://medium.com/towards-artificial-intelligence/from-data-lake-to-data-lakehouse-why-ai-changes-the-rules-for-data-platforms-c78feab48e1c?sk=405811cbc10baa4622bcfcad90736ed4
#ai #machinelearning #data #lakehouse #warehouse #python #datalake #technology #regression
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Most ML issues are not model problems. They are data problems.
I retrained the same churn model twice.
Same code. Same path to the data.
Different result.Why? Because of mutable data references.
:blobcoffee: I wrote a small Data Lake vs Data Lakehouse demo showing why versioned data makes ML debugging reproducible: https://tinyurl.com/lake-vs-lakehouse-medium
:blobcoffee: Friend-Link: https://medium.com/towards-artificial-intelligence/from-data-lake-to-data-lakehouse-why-ai-changes-the-rules-for-data-platforms-c78feab48e1c?sk=405811cbc10baa4622bcfcad90736ed4
#ai #machinelearning #data #lakehouse #warehouse #python #datalake #technology #regression
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Most ML issues are not model problems. They are data problems.
I retrained the same churn model twice.
Same code. Same path to the data.
Different result.Why? Because of mutable data references.
:blobcoffee: I wrote a small Data Lake vs Data Lakehouse demo showing why versioned data makes ML debugging reproducible: https://tinyurl.com/lake-vs-lakehouse-medium
:blobcoffee: Friend-Link: https://medium.com/towards-artificial-intelligence/from-data-lake-to-data-lakehouse-why-ai-changes-the-rules-for-data-platforms-c78feab48e1c?sk=405811cbc10baa4622bcfcad90736ed4
#ai #machinelearning #data #lakehouse #warehouse #python #datalake #technology #regression
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Chunk size in RAG systems defines the size of the text segments into which documents are split before embedding.
I wanted to understand the impact of three different chunk sizes, so I built a small RAG system to test it: https://towardsdatascience.com/chunk-size-as-an-experimental-variable-in-rag-systems/
:blobcoffee: Wishing you all a successful start to 2026
#ai #datascience #datascientist #ki #artificialintelligence #python #rag #towardsdatascience #programming #Technology
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What’s a CSV Plot Agent? I wanted to create an agent that automatically analyzes and visualizes data from a CSV. I built it using LangChain and Streamlit (two Python frameworks).
:blobcoffee: Check out the step-by-step guide here: https://medium.com/towards-artificial-intelligence/csv-plot-agent-with-langchain-streamlit-your-introduction-to-data-agents-aa282ae970ff?sk=f9be8a191ca89eca28b4aacc45efa52f
:blobcoffee: Here’s the code in the GitHub repo: https://github.com/Sari95/CSV-Plot-Agent-with-LangChain-and-Streamlit
#python #langchain #programming #agenticai #ai #ki #data #datascience #datascientist #streamlit #agent
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I asked ChatGPT to create a study plan and add the sessions directly to my calendar. It worked.
2025 has been called the year of AI agents and these two new ChatGPT modes show why:
:blobcoffee: Agents that research, act, and run tools on their own
:blobcoffee: Tutors that guide your thinking instead of just answeringWhat do you think about the two modes?
👉 https://medium.com/p/77e5477efe59
#chatgpt #openai #agent #agentai #agenticai #samaltman #technology
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LangWHAT?
You've seen names like LangChain, LangGraph, LangFlow or LangSmith – but what’s really behind them?:blobcoffee: LangChain helps us build LLM apps via modular code.
:blobcoffee: LangGraph adds branching logic and multi-agent workflows.
:blobcoffee: LangFlow lets us create flows with drag & drop.
:blobcoffee: LangSmith monitors and evaluates our LLM stack.
LangChain, LangGraph and LangSmith come from the same ecosystem. LangFlow is a visual builder developed independently by DataStax.
Tried both LangChain and Langflow to build the same chatbot — Medium article coming shortly.
#LangChain #LangFlow #LLM #AI #KI #python #OpenSource #LangGraph #LangSmith #technology #chatbot #ollama
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One of the most highlighted parts: "There is no need to move data. Data latency is minimised. Data can be transformed and analysed within a single platform.“
This is one of the reasons for 'Why ETL-Zero' :blobcoffee:
#data #datascience #dataanalysis #dataanalytics #DataEngineering #sql #salesforce #etl #datawarehouse #datalake #datalakehouse #programming
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In a data warehouse you store structured & organized data. In a data lake you can additionally store unstructured data. And was is now a data lakehouse?
Think of a combination of the strengths of both previous data platforms. :blobcoffee:
#data #DataEngineering #datalakehouse #datacenters #datawarehouse #datalake #datascience #sql
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Planning some new content for the YouTube channel.
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New home lab kit has arrived! 👌
#Intel #IntelNUC #HybridCloud #Azure #AzureArc #WindowsServer #WindowsServer2022
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In today's blog post as part of the #FestiveTechCalendar, I am talking about Azure Stack HCI!
https://www.techielass.com/azure-stack-hci-the-best-of-the-cloud-and-on-premises/
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Sarah Pidgeon Learned to Speak Her Mind by Playing Style Icon Carolyn Bessette Kennedy in ‘Love Story’
#TV #TVFeatures #FX #JFKJr #LoveStory #NextBigThing #RyanMurphy #SarahPidgeon #TheKennedys -
Sarah Pidgeon Learned to Speak Her Mind by Playing Style Icon Carolyn Bessette Kennedy in ‘Love Story’
#TV #TVFeatures #FX #JFKJr #LoveStory #NextBigThing #RyanMurphy #SarahPidgeon #TheKennedys -
Sarah Ferguson Leaves TV Talk While Asked About Old Problem
https://newsletter.tf/sarah-ferguson-interview-walk-out-scandal/
Sarah Ferguson walked out of a TV talk years ago when asked about money for access. A video of this is now being shared.
#SarahFerguson, #RoyalFamily, #Scandal, #TVInterview, #PrinceAndrew
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Sarah Ferguson Leaves TV Talk While Asked About Old Problem
A video shows Sarah Ferguson leaving a TV talk a long time ago. She was asked about an old problem where she was accused of taking money to help people meet Prince Andrew. She got upset and walked away.
https://newsletter.tf/sarah-ferguson-interview-walk-out-scandal/
#SarahFerguson, #RoyalFamily, #Scandal, #TVInterview, #PrinceAndrew
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Sarah McBride leads bipartisan coalition to secure ‘freedom and dignity’ for LGBTQ+ people globally
https://web.brid.gy/r/https://www.advocate.com/politics/sarah-mcbride-global-respect-act
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Sarah McBride leads bipartisan coalition to secure ‘freedom and dignity’ for LGBTQ+ people globally
https://fed.brid.gy/r/https://www.advocate.com/politics/sarah-mcbride-global-respect-act
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Sarah McBride leads bipartisan coalition to secure ‘freedom and dignity’ for LGBTQ+ people globally
https://web.brid.gy/r/https://www.advocate.com/politics/sarah-mcbride-global-respect-act