This paper argues that a techno-philosophical reading of the EU AI Act provides insight into the long-term dynamics of data in AI systems, specifically, how the lifecycle from ingestion to deployment generates recursive value chains that challenge existing frameworks for Responsible AI. We introduce a conceptual tool to frame the AI pipeline, spanning data, training regimes, architectures, feature stores, and transfer learning. Using cross-disciplinary methods, we develop a technically grounded and philosophically coherent analysis of regulatory blind spots. Our central claim is that what remains absent from policymaking is an account of the dynamic of becoming that underpins both the technical operation and economic logic of AI. To address this, we advance a formal reading of AI inspired by Simondonian philosophy of technology, reworking his concept of individuation to model the AI lifecycle, including the pre-individual milieu, individuation, and individuated AI. To translate these ideas, we introduce futurity: the self-reinforcing lifecycle of AI, where more data enhances performance, deepens personalisation, and expands application domains. Futurity highlights the recursively generative, non-rivalrous nature of data, underpinned by infrastructures like feature stores that enable feedback, adaptation, and temporal recursion. Our intervention foregrounds escalating power asymmetries, particularly the tech oligarchy whose infrastructures of capture, training, and deployment concentrate value and decision-making. We argue that effective regulation must address these infrastructural and temporal dynamics, and propose measures including lifecycle audits, temporal traceability, feedback accountability, recursion transparency, and a right to contest recursive reuse.

Companies are using machine learning to solve real-world problems and are developing hundreds to thousands of features in the process. They are building feature engineering pipelines as part of MLOps life cycle to transform data from various data sources and materialize the same for future consumption. Without feature stores, different teams across various business groups would maintain the above process independently, which can lead to conflicting and duplicated features in the system. Data scientists find it hard to search for and reuse existing features and it is painful to maintain version control. Furthermore, feature correctness violations related to online (inferencing) - offline (training) skews and data leakage are common. Although the machine learning community has extensively discussed the need for feature stores and their purpose [10, 11], this paper aims to capture the core architectural components that make up a managed feature store and to share the design learning in building such a system.

Feast (Feature Store) is an open source feature store for machine learning. Feast is the fastest path to manage existing infrastructure to productionize analytic data for model training and online inference. Please see our documentation for more information about the project, or sign up for an email newsletter. The above architecture is the minimal Feast deployment. Want to run the full Feast on Snowflake/GCP/AWS?

Moving across the typical machine learning lifecycle can be a nightmare. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. Supporting the operations of data scientists and ML engineers requires you to reduce--or eliminate--the engineering overhead of building, deploying, and maintaining high-performance models. To do that, you'd need to take a systematic approach to MLOps--enter platforms! Machine learning platforms are increasingly looking to be the "fix" to successfully consolidate all the components of MLOps from development to production. Not only does the platform give your team the tools and infrastructure they need to build and operate models at scale, but it also applies standard engineering and MLOps principles to all use cases. But here's the catch: understanding what makes a platform successful and building it is no easy feat.

"Feature store" has been around for a few years. There are both open-source solutions (such as Feast and Hopsworks), and commercial offerings (such as Tecton, Hopsworks, Databricks Feature Store) for "feature store". There have been a lot of articles and blogs published around what "feature store" is, and why "feature store" is valuable. Some organizations have also already adopted "feature store" to be part of their ML applications. However, it is worthwhile to point out that "feature store" is another component added to your overall ML infrastructure, which requires extra investment and effort to both build and operate. Therefore it is necessary to truly understand and discuss "is Feature Store really necessary for every organization?".

This book is all about patterns for doing ML. It's broken up into several key parts, building and serving. Both of these are intertwined so it makes sense to read through the whole thing, there are very many good pieces of advice from seasoned professionals. The parts you can safely ignore relate to anything where they specifically use GCP. The other issue with the book it it's very heavily focused on deep learning cases.

DataOps increases the productivity of AI practitioners by automating data analytics pipelines and speeding up the process of moving from ideas to innovations. DataOps best practices make raw data polished and useful for building AI models.

Since launching Dojo in 2019 to address business owners' pain points across the UK, we've seen incredible growth as a scaling fintech, welcoming over 60,000 new customers and growing the Dojo team to 900 people across five locations. In that time, we've also introduced Dojo Virtual Queues & Bookings and the Dojo restaurant finder app, formerly known as WalkUp and loved by 600 restaurants and 600,000 diners across the UK. Find out more about the journey we've been on here . Today, we work towards our mission to empower businesses to thrive in the Experience Economy by creating the tools and technology that turn transactions into meaningful relationships. For our business customers, that means fast funding, the fastest payments, smart integrations, and efficient virtual queues & bookings - all with instant insight across the board.

At a rapid pace, many machine learning techniques have moved from proof of concepts to powering crucial pieces of technology that people rely on daily. In attempts to capture this newly unlocked value, many teams have found themselves caught up in the fervor of productionizing machine learning in their product without the right tools to do so successfully. The truth is, we are in the early innings of defining what the right tooling suite will look like for building, deploying, and iterating on machine learning models. In this piece we will talk about the only 3 ML tools you need to make your team successful in applying machine learning in your product. Before we jump into our ML stack recommendations, let's turn our attention quickly to how the tooling that the software engineering industry has settled on.

The industrialization of artificial intelligence – one of the 7 hot data topics for 2022 requires the implementation of MLOps. This approach includes some necessary steps, including a common platform and a feature store. To learn more about this approach, we offer you a how-to-guide for an iterative, but unavoidable transformation. After years, which were certainly fruitful in gaining experience, working on the development of PoC, organizations now aim to move into a new phase of maturity. And this phase aims in particular to design in an industrial way Data products with embedded artificial intelligence.