How Feature Stores will revolutionize Enterprise AI

In this contributed article, Monte Zweben, CEO and co-founder of Splice Machine, discusses Feature Stores which are a new MLOps technology being adopted by cutting-edge companies like Uber, Airbnb, and Netflix, and for good reason. A Feature Store is a system made specifically to automate the input, tracking, and governance of data into machine learning models.

Scylla Summit 2019 Comes to San Francisco in November

Scylla community members are invited November 5-6 to Scylla Summit 2019 in San Francisco. Use discount code InsideBigData50 to save 50% on a 2-day summit pass. “See us unveil our latest features, including Lightweight Transactions and Change Data Capture, learn about product-ready capabilities beyond what’s available in Cassandra, like Workload Prioritization, Materialized Views and Global Secondary Indexes, and get a preview of Project Alternator, our open source DynamoDB-compatible API.”

Why You Need a Modern Infrastructure to Accelerate AI and ML Workloads

Recent years have seen a boom in the generation of data from a variety of sources: connected devices, IoT, analytics, healthcare, smartphones, and much more. This data management problem is particularly acute in the areas of Artificial Intelligence (AI) and Machine Learning (ML) workloads. This guest article from WekaIO highlights why focusing on optimizing infrastructure can spur machine learning workloads and AI success.

How Hadoop Can Help Your Business Manage Big Data

Hadoop. Once largely unknown, hit the scene in part due to the explosion of unstructured data. Download the new white paper, “Making the Most of Your Investment in Hadoop,” through which SQREAM explores an approach to Hadoop that aims to help businesses reduce time-to-insight, increase productivity, empower data teams for better decision making, and increase revenue.

Using Converged HPC Clusters to Combine HPC, AI, and HPDA Workloads

Many organizations follow an old trend to adopt AI and HPDA as distinct entities which leads to underutilization of their clusters. To avoid this, clusters can be converged to save (or potentially eliminate) capital expenditures and reduce OPEX costs. This sponsored post from Intel’s Esther Baldwin, AI Strategist, explores how organizations are using converged HPC to combine HPC, AI, and HPDA workloads.

New Io-Tahoe White Paper Designed to Help Prepare for CCPA

There’s no denying it. Big data, and the resulting applications and technology have not only made consumers’ lives easier, but have also created new revenue streams for enterprises across all sectors. That said, the explosion of data has also created concerns around data privacy and cyber security, and has gotten the attention of regulators. Download the new report, “6 Steps: Getting Ready for CCPA,” courtesy of Io-Tahoe, to learn more about today’s new data privacy regulations to better protect your enterprise.

AI-Driven Data Catalogs: How to Find the Right one for Your Business

The commoditization of data has opened a world of opportunities up for countless enterprises. But as big data explodes, metadata initiatives are failing, and data discovery and retrieval is getting more and more difficult. A new white paper from IO-Tahoe explores data catalogs as a potential answer to this challenge.

CDO Role Evolving as Data Becomes Driving Force for Many Companies

The job of Chief Data Officers is changing and evolving at a rapid pace. The CDO role, in many cases, is one of the most important strategically for many businesses and organizations driven by data. A new white paper from Io-Tahoe explores the best approaches for CDOs to succeed in todays quickly evolving market.

3 Non-Obvious Keys to Being AI-Ready

Data scientists know what they are doing, and most organizations have no cause to worry about the soundness of their machine learning (ML) algorithms. Where AI readiness typically lags is in other parts of the process. In most organizations today, the process of building, deploying and maintaining AI systems bears no resemblance to traditional IT. Alegion explores three key strategies your business can employ to be AI-ready.

Distributed GPU Performance for Deep Learning Training

If there is a time deadline by which training must be completed, or if it simply takes too long to complete training, distributing the workload across many GPUs can be used to reduce training time.  This flexibility allows GPU resources to be maximally utilized and provides high ROI since time to results can be minimized. HPE highlights recent research that explores the performance of GPUs in a scale-out and scale-up scenarios for deep learning training.