Data

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The Year in AI So Far: Massive Models and How to Use Them

Future Editorial

A recap of artificial intelligence and machine learning coverage in Future so far in 2022, as well as the biggest advances in AI/ML research.

The Two Things We’ll Need for the Next AlphaFold

Daphne Koller and Nicole Neuman

Daphne Koller explains why some fail the academia-to-biotech transition and identifies what we'll need for AlphaFold-level successes across biology and biotech.

7 Techniques for Building Reliable AI Models

Beena Ammanath

As enterprises grow their AI footprints, they must pay attention to data quality and real-world conditions to ensure what works in the lab works in production.

Data50: The World’s Top Data Startups

Jennifer Li, Sarah Wang, and Jamie Sullivan

The decade of data is here, as shown by bellwether startups across the most exciting categories, like AI/ML, ELT and orchestration, and data observability.

How ‘Hyperscalers’ are Innovating — and Competing — in the Data Center

Nick McKeown, Martin Casado, and Zoran Basich

As traffic explodes, programmable chips are creating ways for the biggest companies to differentiate, with major implications for the industry.

The Great Data Debate

Bob Muglia, Michelle Ufford, Martin Casado, Tristan Handy, and George Fraser

As the technical capabilities of data lakes and data warehouses converge, are the separate tools and teams that run AI/ML and analytics converging as well?

Data Alone Is Not Enough: The Evolution of Data Architectures

Ali Ghodsi and Martin Casado

Ali Ghodsi and Martin Casado explore the evolution of data architectures and where they’re going, and consider a surprising use case for streaming data.

Emerging Architectures for Modern Data Infrastructure

Matt Bornstein, Jennifer Li, and Martin Casado

Software systems are increasingly based on data, rather than code. A new class of tools and technologies have emerged to process data for both analytics and ML.

Emerging Architectures for Modern Data Infrastructure: 2020

Matt Bornstein, Martin Casado, and Jennifer Li

Software systems are increasingly based on data, rather than code. And a new class of tools and technologies have emerged to process data for both analytics and operational AI/ ML.

Reining in Complexity — Data Science & Future of AI/ML Businesses

Peter Wang and Martin Casado

Customers are looking for artificial intelligence and machine learning help. So what does this all mean for the software value chain?