AI adoption has been accelerating at an astounding pace. However, organizations continue to battle the day-to-day AIOps challenges of building the right data management practices, establishing a model training workflow that can be repurposed, and resolving infrastructure and deployment inefficiencies.
According to Gartner (via VentureBeat), 85% of all AI models/projects fail because of poor data quality or little to no relevant data. The key reason for this inadequacy is the lack of a well-defined and implemented data access framework. This lack leads to fragmented data silos and operational bottlenecks that make AI implementation slow and resource-intensive, which in turn holds back organizations from realizing the true value of their AI investments.
By integrating enterprise-grade data management solutions for AIOps with state-of-the-art large language model (LLM) workflows, DataNeuron, powered by Google Cloud NetApp Volumes, is redefining how organizations approach AI.
In this blog post, we discuss how DataNeuron’s use of NetApp Volumes is helping customers optimize their data storage platforms for AIOps. With DataNeuron and NetApp Volumes, organizations can enhance platform scalability, streamline AI lifecycle management, and expedite AI adoption while keeping their infrastructure costs in check.
What is DataNeuron’s mission, and how is it being realized?
Bharath Rao, founder of DataNeuron, says that the company’s mission is to:
Simplify AI adoption by providing an automated platform for managing the end-to-end AI lifecycle, ranging from data preparation to deployment and continuous optimization.
DataNeuron provides an automation framework that streamlines the AI training pipeline with automated curation, validation, and preprocessing of task-specific training data, making the entire process more time-efficient and accurate.
To take things up a notch further, customers can rapidly fine-tune, customize, deploy, and benchmark AI models, enabling organizations to respond swiftly to changing business needs and market dynamics. DataNeuron customers also get a readily available retrieval-augmented generation (RAG) architecture to significantly enhance the quality and relevance of model responses by seamlessly integrating real-time, contextually relevant data into the response process.
All these features are built within a set of guardrails that provide continuous monitoring capabilities so that deployed models maintain optimal performance over time and can be proactively adjusted as needed.
The role of Google Cloud NetApp Volumes
AI-driven organizations require efficient data management to maintain seamless access to data and to confirm its safe retrieval. They also need efficient management to be able to version-control their data footprint as it evolves and to deliver the performance that they need for model training, fine-tuning, and inference.
Google Cloud NetApp Volumes delivers a fully managed, high-performance data storage service that is built on NetApp® ONTAP® technology. It enables organizations to easily migrate, run, and protect their workloads in Google Cloud with enterprise-grade functionality. NetApp Volumes plays a critical role in this integration with DataNeuron by delivering high-performance data access, on-demand data replication, and workflow optimization to support scalable and efficient AI workflows.
The DataNeuron AIOps platform benefits from a host of capabilities delivered by NetApp Volumes, and the following are some of the features that offer significant enhancements.
- NetApp Snapshot™ copies play a key role in capturing point-in-time representations of data near-instantaneously for efficient version control. AIOps teams can quickly and easily roll back and compare AI model iterations without data bloat.
- The NetApp SnapMirror® feature in ONTAP provides highly efficient data replication across environments. It helps to build disaster recovery for datasets and to maintain high availability while preserving data consistency, which is critical when dealing with large AI datasets.
- Clones provide a quick workflow to create multiple data copies while maintaining complete integrity of the data and enabling the writing of new data to the clone copies. Through this capability, parallel experimentation, comparison of multiple models, and machine learning operations (MLOps) workflow versioning are highly simplified.
- Data Tiering helps in automatically moving unused datasets from high-performance storage tiers to lower-cost platforms, and it frees up the performance cycles for other projects. These capabilities lead to a highly cost-optimized AIOps solution.
The power of integration: 1+1 = 11
The AIOps capabilities of DataNeuron combined with the data management features of Google Cloud NetApp Volumes results in a value-proposition multiplier for end customers. This integration provides a combined value of “bringing AI to data” instead of “sending data to AI”!
Through this combined value, customers can steer clear of:
- Unnecessary data moves that are a waste of time and resources
- Security risks that are involved in data moves
- Performance bottlenecks
- Unforeseen infrastructure expenses
Data bloat is one of the key factors in AI projects that increase storage costs, lengthen processing times, and decrease model efficiency. To prevent a bloat in the project and in the derived artifacts, it’s critical to build data lineage and to enable versioning by using the Snapshot copies and the cloning functionality of NetApp Volumes as described in the above figure. The integrated solution helps an organization maintain its AI projects with minimal resources, and it helps the organization repurpose the data instantaneously—on demand—saving time, resources, and money.
All AI solutions are built and customized on large volumes of labeled training data. Acquiring such relevant data is costly and time-consuming for most organizations. Divisive Sampling and Ensemble Active Learning (DSEAL), is one of the proprietary solutions of DataNeuron, which helps organizations in automated task specific data curation that reduces the time to annotate and validate datasets by 95% when compared to Human in the Loop (HITL).
In addition, through the DataNeuron and NetApp Volumes integration, customers benefit from capabilities such as -
- Data Classification to address regulatory/compliance requirements by identifying Personally Identifiable Information (PII) /Protected Health Information (PHI) across a multitude of datasets with support for Optical Character Recognition (OCR)
- Data Redaction to obfuscate personal information either by masking or modifying it to remove personal identity.
- Data encryption at rest and in-flight
These capabilities help organizations identify and protect sensitive data, confirm that models adhere to strict security policies, and implement a secure AI practice.
When solution meets validation - customer experience
Interactly.ai is a venture capital-backed early-stage seed startup specializing in the development of healthcare administration agents and teammates aimed at automating administrative processes, enhancing patient engagement, and to improve healthcare outcomes. These solutions aim to automate over 80% of manual processes, thereby enabling substantial efficiency gains for insurance companies and healthcare providers.
Shiva Chaitanya, chief technology officer at Interactly, calls out the key reasons for selecting this solution:
With a big mission to accomplish, we knew that our infrastructure selection would play a key role in our success. By partnering with DataNeuron, we gained access to not just cutting-edge AIOps features but also a rich data management stack powered by NetApp technology.
An infrastructure with industry-grade compliances of SOC2 and HIPAA, combined with powerful and efficient snapshot and cloning capabilities, has allowed us to rapidly simulate and iterate on our agentic AI features.
The ability to build a hybrid cloud combined with containerized management solutions ensures agile data provisioning, replication, and protection across both on-premises and cloud environments. This is essential for fulfilling our startup's time-to-market objectives while ensuring adherence to compliance standards and minimizing errors.
All these factors have been pivotal in ensuring that our AI agents remain resilient and secure.
The road ahead
AI adoption is no longer a futuristic goal, it is a present-day necessity. Organizations across various sectors, from healthcare to finance to manufacturing, are increasingly investing in AI to enhance decision-making, to automate processes, and to gain a competitive advantage. However, successful AI deployment at scale requires robust infrastructure, seamless data management, and powerful AI development tools.
With Google Cloud NetApp Volumes, DataNeuron is committed to making AI more accessible to customers through intuitive tools and infrastructure solutions that center around the core principles of operational efficiency, security by design, continuous optimization and automation. All these put together will considerably lower the barrier for AI adoption and drive up the success rate of AI projects across organizations.