Abie Cohen

Keeping Your Data Close to Home: Google Cloud's Commitment to UK Data Residency 🇬🇧 As businesses increasingly rely on AI and Machine Learning, data residency and security are top of mind. The good news? You can innovate responsibly and keep your data within UK borders. My latest blog dives into how we're making this a reality. main take-aways: 🔐 Control over your data: The ability to choose where your data is processed, even during ML training, is crucial. Google Cloud gives you that power. 🇬🇧 Meeting UK regulations: The post highlights how Google Cloud's data residency options help UK businesses stay compliant with evolving data protection laws. 🤖 Unlocking innovation: With data residency concerns addressed, businesses can confidently leverage Google Cloud's cutting-edge ML capabilities. This commitment to data sovereignty is a game-changer, especially for UK businesses operating in highly regulated sectors. Read the full blog post here: https://lnkd.in/eikeBVPu #GoogleCloud #DataResidency #AI #MachineLearning #DataSecurity #UKBusiness #Innovation #Compliance https://lnkd.in/eikeBVPu

18

Currently digging Bryce Codell's MDS-fest talk on the use cases of the activity-schema. He does a really great job up front characterizing how easy it is to fall into the 'service-model trap', why the service model trap is bad, and why event driven modeling can really drive towards a more scalable data-team...which he would know because he's a data team of 1, supporting data-heavy products at Ashby Analytics https://lnkd.in/eshUT8ZY #DataEngineering #AnalyticsEngineering #DataAnalytics

18

2 Comments

✍️ A couple of things I've been working on have been published recently. One is an article for Harvard Data Science Review that I wrote with Elena Simperl and Sir Nigel Shadbolt. It makes the case for drastically increasing transparency around the data used to train foundation AI models. It's admittedly a little out of date, as we submitted back in 2023. But there's still plenty of work to do to ensure we really know what this wave of AI has been trained on. There's more interesting articles in this special edition, 'Future Shock: Grappling with the Generative AI Revolution'. https://lnkd.in/eKapqiBX The other is on intellectual property, with Gefion Thuermer, Ben Snaith, Thomas Carey-Wilson and others at the Open Data Institute. It argues that a fair intellectual property regime is key to creating a data and AI ecosystem that works for everyone. It recommends that an incoming UK Government avoids reaching for a one-size-fits-all solution to the copyright conundrum, supports collective rights management and seeks international alignment. https://lnkd.in/eJBJE8p4 Please share and let me know if you'd like to talk.

52

Regulatory compliance with EIOPA is non-negotiable—but it doesn’t have to be daunting. We’ve built end-to-end data lineage across your entire data stack to ensure your data remains complete, accurate, and fully compliant with regulatory standards. Metaphor provides deep visibility into your data’s journey so that you can meet the stringent requirements of EIOPA Solvency II and DORA without breaking a sweat. Ensure accuracy across platforms and maintain data visibility that satisfies regulators and stakeholders. #EIOPA #DORA #DataLineage #DataCompliance

40

2 Comments

Here’s the 2nd and final part to our series on building data products with snowflake and how to leverage a data developer platform to optimise compute with Animesh Kumar. Do give it a read. And do follow Modern Data 101 for more such articles. #DataProducts

12

1 Comment

There are now three new additions to the Gemma 2 family: 1) Gemma Scope: a model interpretability tool for transparency⁠ 2) Gemma 2 (2B): a new model with an updated 2 billion (2B) parameter size⁠ 3) ShieldGemma: a suite of safety content classifiers for user protection Learn more now ↓

1

I'm thrilled to share a bonus episode of the Data Today #podcast on #AIAppreciationDay. This one is particularly special to me: Data and Artificial Neural Networks with Damian Borth.    Neural networks are the backbone of many #AI and machine learning systems. But how can they be applied to real-world scenarios?    I had the incredible opportunity to sit down with Damian Borth, Professor of Artificial Intelligence and Machine Learning at the University of St.Gallen and director of its Computer Science Institute. Damian's ground-breaking work on neural networks spans several crucial areas, including climate change, financial fraud detection, and text-to-speech. It's no wonder he’s a recipient of the prestigious Google Research Scholar Award!    In this episode, we dive into:  What #neuralnetworks are and what they do How their application can help tackle global challenges Insights from Damian's vast experience and groundbreaking research   I found our conversation incredibly enlightening and inspiring, and I can't wait for you all to hear it. Tune in to discover how #ArtificialIntelligence and neural networks could help save the world! #TechForGood https://lnkd.in/ePTYn3AC #MachineLearning #DataScience #Innovation #Research #Data #Technology

44

What does 'scale' mean in analytics now? Traditionally it's been the number of concurrent queries you can process, the data volumes of those queries, and the number of concurrent users generating them. That is still important, but I'd argue it's not longer the limiting factor on scale. With cloud platforms, distributed compute and new advances in single machine compute performance, there are so many ways to solve these problems it's really not a challenge except at the absolute upper bounds. Scale in analytics is now about the ability to build, operationalize and manage a huge variety of data sources, pipelines, metrics, assets and user experiences efficiently with high velocity and user trust, and doing it all at a favorable cost profile with a reasonable headcount. In short, scale in analytics isn't about having 10k concurrent users for one dashboard, it's about having 10k personalized front end experiences built and maintained by a small, effective team. If this sounds like the stuff of software engineering instead of BI, well it is. And it's coming to a BI team near you soon! #analytics #businessintelligence #data #scale #dataengineering #datamesh

24

2 Comments

What should the North Star of a data team be? It’s not data quality, literacy or insights. It’s clarity. For me, clarity is the single binding concept which ties together all that a data team does. Improving data quality? That’s because it provides better clarity to the business because the data they are looking at is accurate. Building a report? If it doesn’t clarify what the operating state of the company is then it’s failed. Building a visualisation? We should work to make sure it tells as clear a story as possible. Training the wider business how to analyse data? This helps people gain clarity by themselves. Greater business clarity is the value driver of the data team. We can’t always push the decision button but we can work damn hard to make sure people are 100% clear which one to push and why.

49

14 Comments

Few thoughts from me and our AI prodigy Adam Horka on how document classification (categorization/sorting) changed over years and how we often implement it today at ableneo.

10

Looking into Data Contracts I find something difficult to understand. Why would we want to add the retention of data as part of the contract, and not be explicit and attach this as a quality of the output port itself? In my ideal scenario, retention is set and computationally executed by the Data Product Owner (owning team, not the figure). So adding this in the contract, and not in the definition of the Output Port itself is counterproductive. Of course the DPO will validate the right retention based on legal and consumption needs, but still there is nothing to check or validate from the contract perspective. So, question to the world... what would you expect to do with that information? is it merely FYI? or are there any triggers that need to validate this? #datamesh #datacontracts #thisisnotgenai

5

1 Comment

I have a great time spinning up our own delivery data in Omni, to the point that it deserved a write up on how easy is to use and the instant insights that we get from it! Check it out in our blog!

24

1 Comment

Former US President Donald Trump’s recent appearance on Joe Rogan's podcast pulled in an astounding 26 million views on YouTube in just 24 hours – and this debate might just top it! Tune in as Michael Amadi and Giles Middleton face off in the ultimate Databricks vs. Microsoft Fabric showdown. Who do you think came out on top? Link in the comments below ⬇️ #databricksvsfabric #podcast #youtube #spotify

14

3 Comments

If you truly want to make a difference for your business with data, spend as much time understanding how your business works as you do honing your data skills. Deep knowledge of your business context is crucial; one without the other won’t yield significant results. Simply put, if you want to personalise customer experiences using data, first personalise your approach to data for your business. #Data #BusinessIntelligence #DataDriven #BusinessInsight

28

Our yearly report on the State of Spatial Data Science is out! Co-authored with our partners at Databricks, it will give you endless conversation topics for your summer barbecues on how the adoption of Spatial Data Science is growing across different industries. Check it out here: https://lnkd.in/dU4bFVdi

45

2 Comments

Great way to get started as you plan your data governance and discoverability evolution with #Azure Databricks Unity Catalog. The first part of any journey, is knowing where you're starting from. Love this initiative to help customer get started Advancing Analytics 🙌

5

The next generation of personalised digital services will use data ecosystems and AI to build fairer and more profitable services. This short article on Medium outlines a few thoughts about what they will look like.

8