Analytics, Artificial Intelligence (AI) and Machine Learning (ML)

Overview

Enterprises are emerging into a new data cycle powered by the rise of massive data processing that is required to fuel innovation in all types of enterprises. Many organizations are urgently assessing their data architectures to leverage AI to grow revenue streams and improve operational efficiencies. AI architects are looking for solutions that will help them design for flexibility for the future while providing the key building blocks for today.

One of the biggest challenges facing organizations is putting distributed unstructured data sets to work in their AI strategies.

Hammerspace helps organizations create a data mesh that unifies metadata and automates unstructured data orchestration across multiple locations and data silos. It offers the flexibility to load data sets and storage silos needed for today’s AI initiatives and adding new data sets or data locations with the click of a button in the future. No matter where the AI model is located, local to the data or in a remote cloud or SaaS tool, Hammerspace makes the data easy to access, analyze, process, and move when needed.

Hammerspace optimizes data pipelines requiring:

01. High-Performance

Most AI projects will become business critical in both decision making and cost containment. It is critical that the data pipeline is designed to use all available compute power and can make data available to the cloud models such as those found in Databricks and Snowflake.

Hammerspace high-performance data pipelines power some of the fastest compute farms in the world, some exceeding 60,000 GPUs in a single cluster. It has the performance optimizations to stream data at nearly line rate when loading data, it returns fast results when parameters are regularly checkpointed, and read requests are simultaneously delivered at the speed applications require.

02. Multiple Data Sources

AI models limited to using data from a single storage silo will be at a major disadvantage compared with those that can access a wide range of data sets stored across multiple storage platforms and likely multiple geographic locations. The AI data pipeline needs to be generated from data created and stored in edge devices, data centers, and the cloud.

To effectively access all this data, Hammerspace unifies siloed storage types as well as multiple geographic locations. Doing so enables systems to place, present, and preserve data for access by the AI and ML models wherever the data is and wherever the models are run.

03. Automated Data Access and Controls

As AI is increasingly being used in sensitive areas like healthcare, finance, and more, there are growing regulatory concerns.

Hammerspace helps organizations meet regulatory requirements by automating the processes and data services which oversee data access and protection to ensure data privacy, security, and appropriate usage is enforced. From data creation to archival or deletion, Hammerspace automates the entire lifecycle of data to ensure requirements are met on how long data is retained, when it’s archived, and when it’s deleted.

04. Data Governance

Data governance plays a crucial role in AI and becomes even more complex when the data sources are distributed across many different silos and locations.

Hammerspace helps ensure data quality by unifying multiple data sources into a single namespace and metadata control plane. This helps reduce biases and ensures the accuracy, completeness, reliability, and timeliness of data to drive accuracy in model predictions and data being trustworthy for decision-making.

05. Massive Scale

AI models must be trained with large quantities of data to be most accurate. The more data that is available to them, the more accurate the results will be from the outset.

Hammerspace integrates existing data sets, cloud instances, and any new infrastructure into a unified global data environment that will scale as AI workloads evolve.

06. Enterprise Standard

Enterprise standard data interfaces and data services play a critical role in efficacy, scalability, and reliability of AI systems while also aligning with organizational objectives for compliance, governance, and efficiency.

To speed the integration of models with data, Hammerspace presents industry standard NFS as the interface to data makes it easy for applications to connect to data without being rewritten for object. And using NFSv4.2, performance can scale to the limits of any hyperscale environment.

To meet data management, access and protection requirements, Hammerspace offers a full suite of enterprise data services.

The Hammerspace Solution

Hammerspace makes data a live, globally shared resource, that is no longer localized or trapped within specific storage systems or specific cloud data services. With Hammerspace, organizations can readily utilize workflows with applications and infrastructure found anywhere in the world, not limited by proximity to the infrastructure holding their data. The volume of data, and the ways it can be used, will grow exponentially and with ease. Agility, innovation cycles, and the rate that value can be created from data will accelerate. High-performance local read/write access to data that is shared globally in real time in a global data environment will become indispensable and ubiquitous.