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23 May

Breaking Down Silos, Building Up Insights: Implementing a Data Fabric

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(amiak/Shutterstock) Data is the lifeblood of modern business, but for commercial-sized companies, managing and leveraging data can feel like navigating a maze. But what if there was a way to simplify the journey and unlock the full potential of a company’s data? Read on to learn how a data fabric can add value by maximizing the value of a company’s data infrastructure. Large, global enterprises have massive data teams set up to transfer and manage their data, using approaches like a data mesh. But commercial-sized companies are also dealing with more and more complex data landscapes, and finding that a…

23 May

Data Machina #251

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Six Nerdy AI Activities for the Long W/E. I’ve just read that lots of AI engineers in the US are running the rate race, feeling burnout. Here in the European AI scene things are innately a bit more relaxed.Aah… A long bank holiday in London; so much stuff to do in this amazing city! But if you are feeling the AI FOMO kick and can’t survive a long weekend IRL, here are six AI activities for you:Generate comics with AI. I gave it a go, generated a few short comics, and having fun so far. The AI team at Bytedance…

23 May

The Importance of Data Analytics in Servitization

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23 May

Optimizing Databricks LLM Pipelines with DSPy

stp2y0 CommentsViral Newsdspy, GenAI, generative ai, LLM

If you’ve been following the world of industry-grade LLM technology for the last year, you’ve likely observed a plethora of frameworks and tools in production. Startups are building everything from Retrieval-Augmented Generation (RAG) automation to custom fine-tuning services. Langchain is perhaps the most famous of all these new frameworks, enabling easy prototypes for chained language model components since Spring 2023. However, a recent, significant development has come not from a startup, but from the world of academia. In October 2023, researchers working in Databricks co-founder Matei Zaharia’s Stanford research lab released DSPy, a library for compiling declarative language model calls into…

23 May

Hugging Face Autotrain – Getting Started

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Autotrain is a no-code platform from Hugging Face to train, evaluate, and deploy machine learning and deep learning models. In this article, we will use Hugging Face Autotrain to train a Small Language Model (SLM). The Hugging Face Autotrain platform offers several functionalities for training: Computer Vision models Machine Learning models And LMs & LLMs However, in this article we will focus on training a language model for instruction following using the Autotrain platform. Although we can directly access Autotrain from their platform, we will use local installation. So, in way, we will use some code rather than the no-code…

23 May

Self-Driving Cars vs. Coding Copilots

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Back in the mid-2010s, the world of autonomous vehicles was making great progress, and it seemed that we would soon be ushered around in cars that drove themselves, leaving us free to spend our time how we wanted. That obviously hasn’t happened, but instead, we’ve been treated to a form of AI we weren’t expecting: generative AI-powered copilots. Following the launch of ChatGPT in late 2022, the world of generative AI has been on a tear. Every company seems to be investing in large language models (LLMs) to build one of the two most visible forms of GenAI: chatbots and…

23 May

Data Machina #252

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Diffusion, FM & Pre-Trained AI models for Time-Series. DeepNN-based models are starting to match or even outperform statistical time-series analysis & forecasting methods in some scenarios. Yet, DeepNN-based models for time-series suffer from 4 key issues: 1) complex architecture 2) enormous amount of time required for training 3) high inference costs, and 4) poor context sensitivity.Latest innovative approaches. To address those issues, a new breed of foundation or pre-trained AI models for time-series is emerging. Some of these new AI models use hybrid approaches borrowing from NLP, vision/ image, or physics modelling, like: transformers, diffusion models, KANs and state space…

23 May

The Role of Synthetic Data in Cybersecurity

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Data's value is something of a double-edged sword. On one hand, digital data lays the groundwork for powerful AI applications, many of which could change the world for the better. Conversely, storing so many details on people creates huge privacy risks. Synthetic data provides a possible solution. What Is Synthetic Data? Synthetic data is a subset of anonymized data – data that doesn't reveal any real-world details. More specifically, it refers to information that looks and acts like real-world data but has no ties to actual people, places or events. In short, it's fake data that can produce real results. In…

22 May

Announcing General Availability of Liquid Clustering

stp2y0 CommentsViral Newsdata management, delta lake, liquid clustering

We’re excited to announce the General Availability of Delta Lake Liquid Clustering in the Databricks Data Intelligence Platform. Liquid Clustering is an innovative data management technique that replaces table partitioning and ZORDER so you no longer have to fine-tune your data layout to achieve optimal query performance. Liquid clustering significantly simplifies data layout-related decisions and provides the flexibility to redefine clustering keys without data rewrites. It allows data layout to evolve alongside analytic needs over time – something you could never do with partitioning on Delta. Since the Public Preview of Liquid Clustering at the Data and AI Summit last year, we’ve…

22 May

Top Data Validation Tools for Machine Learning in 2024

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Image generated with MidjourneyIt was challenging to stop myself from starting this article with some variation of the popular phrase "garbage in, garbage out." Well, I did it anyway. But jokes aside, we can easily imagine a situation in which we have built and deployed a machine learning model (possibly a black box) that accepts some input and returns some predictions. So far, so good.However, with tons of complexity happening before the model (data preprocessing and manipulation), the model itself, and any post-processing of the outputs, many things can go wrong. And in some mission-critical fields (finance, healthcare, or security),…