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There is no need to move data. Data latency is minimised. Data can be transformed and analysed within a single platform.
Let me know what you know about Zero-ETL :blobcoffee:
Why ETL-Zero? Understanding the shift in Data Integration“ by Sarah Lea on Medium: https://medium.com/towards-data-science/why-etl-zero-understanding-the-shift-in-data-integration-as-a-beginner-d0cefa244154
#python #datalake #cloudcomputing #etl #zeroetl #salesforce #data #tech #technology #datawarehousing #datalakehouse
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Unusual Ventures’ Sarah Leary and John Vrionis join Extra Crunch Live today at 2 p.m. ET/11 a.m. PT - Today at 2 p.m. ET/11 a.m. PT, Unusual Ventures’ Sarah Leary and John Vrionis are joining us over ... - http://feedproxy.google.com/~r/Techcrunch/~3/yzhGdSrwAuY/ #extracrunchlive #unusualventures #venturecapital #johnvrionis #sarahleary #startups #tc
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J'ai complètement oublié de partager ce superbe dessin fait par Sarah-Léa.
Un grand merci à elle :ablobcatheartsqueeze:
Lien Twitter : https://twitter.com/SarahMbanzulu
#Splatoon #Splatoon3 #SalmonRun #SalmonRunNextWave #SplatArt
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Discuss the unbundling of early-stage VC with Unusual Ventures’ Sarah Leary & John Vrionis - This year has been everything but business as usual for the venture and tech community. And we still... - http://feedproxy.google.com/~r/Techcrunch/~3/Q5mx5nH-rxY/ #lightspeedventurepartners #extracrunchlive #unusualventures #venturecapital #appdynamics #johnvrionis #sarahleary #wearables #startups #tc
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If you are in the UK, you might want to listen to a story on radio #BBCLondon today, around 11.40 AM:
the National Federation of the #Blind taking legal action against the changes to BBC Local radio - visually impaired listener Sarah Leadbetter, who started the procedure, will be on air. Worth tuning in.
And here is what a former colleague, Sophie Little, had to say about the cuts in her last broadcast on Friday (apols for birdsite post):
https://twitter.com/airchecks/status/1700903995240481134?t=_OwdzEtIJyngcCmyR22G7A&s=19 -
I don’t normally copy and paste a full article, but this one deserves it IMO:
Quote:
In response to the US State Department’s decision to deny visas to Palestinian Authority officials ahead of the September UN General Assembly meeting, DAWN issues the following statement:
“The UNGA should hold its September meeting in Geneva to allow Palestine to participate,” said Sarah Leah Whitson, DAWN’s executive director. “Moving the meeting there will send a message to the Trump administration that the international community does not tolerate these breaches of long standing law requiring access to all representatives.”
The UN Headquarters Agreement of 1947 requires the United States to provide unfettered access to UN proceedings for all representatives, regardless of bilateral disputes. Section 11 establishes an “unrestricted right” for officials to enter the US for UN business, while Section 12 states these provisions apply “irrespective of the relations existing between the Governments” and the US.
This is not the first time the US has violated its obligations under the UN Headquarters Agreement. In 1988, the US denied a visa to Palestine Liberation Organisation chairman Yasser Arafat to attend the UN General Assembly. The UN responded by adopting a resolution concluding that Washington had violated its obligations under the 1947 Agreement and, as a rebuke, moved its General Assembly meeting from New York to Geneva to allow the Palestinian leader to speak.
“The international community can no longer allow American obstructionism to silence Palestinians and prevent accountability for Israeli war crimes and genocide in Palestine,” said Raed Jarrar, DAWN’s advocacy director. “Whether the UNGA meets in Geneva or not, it is time for the international community to deploy peacekeeping forces to protect Palestinians from Israel’s genocide.”
DAWN has called on the UNGA to deploy international peacekeeping forces to Gaza under a “Uniting for Peace” resolution. More information is in our petition.
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#Auspol #WorldPol # USPol #Palastine #Gaza #UNGA
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Anyone working with business intelligence, data science, data analysis, or cloud computing will have come across SQL at some point. Take a deep dive into data lakehouses, SQL, data modeling + more in Sarah Lea's latest article.
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Today I came across the central limit theorem once again. And the name already says it: central 😉
:blobcoffee: Take many independent random variables & average them.
→ You’ll get something close to a normal distribution.
→ Whatever the originals looked like.:blobcoffee: To try it out in R:
m <- replicate(10000, mean(runif(100, 0, 100)))
hist(m, main="Distribution of means (CLT)"):blobcoffee: Essential for anyone working with data: In statistics, machine learning or science. And it turns out to be easier to understand than you might think.
#data #datascience #datascientist #math #statistic #machinelearning #ai #ki #artificialintelligence #science #students
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Today I came across the central limit theorem once again. And the name already says it: central 😉
:blobcoffee: Take many independent random variables & average them.
→ You’ll get something close to a normal distribution.
→ Whatever the originals looked like.:blobcoffee: To try it out in R:
m <- replicate(10000, mean(runif(100, 0, 100)))
hist(m, main="Distribution of means (CLT)"):blobcoffee: Essential for anyone working with data: In statistics, machine learning or science. And it turns out to be easier to understand than you might think.
#data #datascience #datascientist #math #statistic #machinelearning #ai #ki #artificialintelligence #science #students
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Today I came across the central limit theorem once again. And the name already says it: central 😉
:blobcoffee: Take many independent random variables & average them.
→ You’ll get something close to a normal distribution.
→ Whatever the originals looked like.:blobcoffee: To try it out in R:
m <- replicate(10000, mean(runif(100, 0, 100)))
hist(m, main="Distribution of means (CLT)"):blobcoffee: Essential for anyone working with data: In statistics, machine learning or science. And it turns out to be easier to understand than you might think.
#data #datascience #datascientist #math #statistic #machinelearning #ai #ki #artificialintelligence #science #students
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Today I came across the central limit theorem once again. And the name already says it: central 😉
:blobcoffee: Take many independent random variables & average them.
→ You’ll get something close to a normal distribution.
→ Whatever the originals looked like.:blobcoffee: To try it out in R:
m <- replicate(10000, mean(runif(100, 0, 100)))
hist(m, main="Distribution of means (CLT)"):blobcoffee: Essential for anyone working with data: In statistics, machine learning or science. And it turns out to be easier to understand than you might think.
#data #datascience #datascientist #math #statistic #machinelearning #ai #ki #artificialintelligence #science #students
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Today I came across the central limit theorem once again. And the name already says it: central 😉
:blobcoffee: Take many independent random variables & average them.
→ You’ll get something close to a normal distribution.
→ Whatever the originals looked like.:blobcoffee: To try it out in R:
m <- replicate(10000, mean(runif(100, 0, 100)))
hist(m, main="Distribution of means (CLT)"):blobcoffee: Essential for anyone working with data: In statistics, machine learning or science. And it turns out to be easier to understand than you might think.
#data #datascience #datascientist #math #statistic #machinelearning #ai #ki #artificialintelligence #science #students
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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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Do you always go to the same café? Or do you try something new?
That’s the exploration vs. exploitation dilemma: Decision under uncertainty.
Multi-armed bandits model exactly that.
And this dilemma shows up everywhere: Recommender systems, A/B tests, online ads, even in human psychology.
Nobel Prize winner Daniel Kahneman called this one of the most fundamental cognitive patterns.
🎰 I explain what it is, why it matters, and how AI systems handle it.
:blobcoffee: Full article here: https://towardsdatascience.com/simple-guide-to-multi-armed-bandits-a-key-concept-before-reinforcement-learning/
#ReinforcementLearning #AI #CognitiveScience #Kahneman #Psychology #Behavior #DecisionMaking #Bandits #machinelearning #KI #Datascience #datascientist
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Reinforcement Learning starts with a simple but powerful idea:
Trial & Error. Learning what works.The Multi-Armed Bandit problem is a first step into this world.
It's not just about slot machines. Iit's about how AI (and humans) learn to choose.#ReinforcementLearning #AI #CognitiveScience #Psychology #Behavior #DecisionMaking #Bandits #machinelearning #KI #Datascience #datascientist
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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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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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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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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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Can you remember learning to walk as a baby? You didn’t read a manual. Neither does an AI agent.
Reinforcement Learning (RL) isn’t about knowing the correct answer.
It’s about learning through trial and error, by interacting with an environment & receiving feedback.That’s how AlphaGo defeated a world champion:
It first learned from expert games. Then it played against itself, millions of times, using RL to get better with each game. That’s how it mastered Go.#machinelearning #ai #ki #google #reinforcementlearning #alphago #datascience #datascientist
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What do a baby learning to walk and AlphaGo’s legendary Move 37 have in common?
They both learn by doing — not by being told.
That’s the essence of Reinforcement Learning.It's great to see that my article on Q-learning & Python agents was helpful to many readers and was featured in this week's Top 5 by Towards Data Science. Thanks! :blobcoffee: And make sure to check out the other four great reads too.
-> https://www.linkedin.com/pulse/whats-our-reading-list-week-towards-data-science-dcihe
#Reinforcementlearning #AI #Python #DataScience #KI #alphago #google #googleai #ArtificialIntelligence
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What does a baby learning to walk have in common with AlphaGo’s Move 37?
Both learn by doing — not by being told.
That’s the essence of Reinforcement Learning.
In my latest article, I explain Q-learning with a bit Python and the world’s simplest game: Tic Tac Toe.
-> No neural nets.
-> Just some simple states, actions, rewards.The result? A learning agent in under 100 lines of code.
Perfect if you are curious about how RL really works, before diving into more complex projects.
Concepts covered:
:blobcoffee: ε-greedy policy
:blobcoffee: Reward shaping
:blobcoffee: Value estimation
:blobcoffee: Exploration vs. exploitationRead the full article on Towards Data Science → https://towardsdatascience.com/reinforcement-learning-made-simple-build-a-q-learning-agent-in-python/
#Python #ReinforcementLearning #ML #KI #Technology #AI #AlphaGo #Google #GoogleAI #DataScience #MachineLearning #Coding #Datascientist #programming #data
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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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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
