On AI certification. Q&A with Rahul Padigela
Q1. You mentioned that fragmented toolchains can extend AI development timelines by 40-60%. What specific pain points did you observe in the field that led Scality to develop this industry-first certification program? Were there particular customer conversations or deployment scenarios that crystallized the need for a systematic approach to AI infrastructure interoperability?
A: In conversations with both enterprise customers and AI startups, we heard a consistent theme: teams were spending months, and sometimes entire quarters, just integrating disparate AI tools before they could even begin meaningful experimentation, much less deployment. Each stage of the AI lifecycle (data ingestion, cleansing, training, fine-tuning, and inference) depends on a mix of open-source and commercial components that weren’t designed to work seamlessly together.
The result was what many described as an ‘integration tax’: extended development timelines by 40–60%, inconsistent data handling, and increased exposure to operational as well as security risks. These challenges were especially pronounced in data-intensive sectors like financial services, healthcare, and large-scale analytics, where governance, reproducibility, and compliance are non-negotiable.
It became clear that AI innovation was being constrained not by model performance, but by infrastructure friction. Scality’s Certification Program was created to address exactly that challenge. It is a trusted interoperability framework that eliminates guesswork, accelerates deployment, and ensures that from day one, every component of the AI stack works together as a cohesive whole.
Q2. While most certification programs focus purely on technical compatibility, Scality emphasizes that yours is built on a ‘cyber-resilient storage architecture’ that protects data integrity throughout the AI lifecycle. Can you elaborate on what cyber-resilience means in the context of AI workflows? What specific security or data integrity challenges emerge when you’re moving data through collection, training, and inference stages, and how does the certification program address these?
A: Cyber-resilience, in the context of AI, means protecting the integrity, availability, and security of data throughout every stage of the model lifecycle. AI workflows are particularly vulnerable because they involve high-volume data movement across multiple systems, each system its own potential attack surface and risk of data corruption or leakage.
For instance, during data preparation, an undetected integrity error or tampered dataset can compromise downstream training. During inference, a lack of traceability can make it difficult to verify model outputs or comply with audit requirements.
Scality’s certification program dramatically mitigates these risks by validating that every certified tool operates on a cyber-resilient storage foundation: one that is immutable, versioned, and protected by end-to-end encryption and integrity checks. By combining interoperability testing with these security assurances, the program ensures that data remains trustworthy and compliant across the entire AI pipeline.
Q3. You describe ‘rigorous real-world testing, including integration trials with other certified tools.’
Could you walk us through what this certification process actually looks like? How do you test for interoperability between, say, Apache Airflow for data collection, Ray for filtering, and PyTorch for training? What failure modes or integration issues have you discovered that organizations might not anticipate when assembling their own AI stacks?
A: Our certification process combines deep technical validation with operational realism. Each tool is deployed within a controlled environment that mirrors enterprise-scale AI pipelines. We test not only whether an individual tool (for instance, Apache Airflow for data orchestration) functions correctly, but whether it integrates seamlessly with adjacent certified components such as Ray for distributed processing or PyTorch for model training.
We examine data movement, metadata consistency, version control, and failure recovery scenarios. For example, we simulate what happens when a network node goes down mid-training, or when a dataset is updated during an active inference process. Common failure modes we’ve uncovered include inconsistent schema propagation, inefficient data serialization formats, and hidden dependencies between framework versions. Issues like that can cause silent data corruption or model drift if left unchecked.
By identifying and resolving these challenges within our certification process, we help organizations to avoid costly surprises and to deliver AI systems that are reliable, reproducible, and production-ready.
Q4. Your program spans both Fortune 500 enterprises and AI startups, yet these organizations have vastly different requirements, constraints, and maturity levels. When you have multiple certified tools in the same category, for example, several options for training or inferencing, how should organizations approach the selection process? What factors beyond certification should guide their architectural decisions?
A: While certification ensures that every tool meets baseline standards for interoperability, security, and resilience, the optimal choice still depends on an organization’s context and objectives.
For large enterprises, priorities often include compliance, long-term vendor viability, and scalability across hybrid or multi-cloud environments. These organizations might favor tools with robust governance capabilities and proven performance under heavy data workloads.
Startups, on the other hand, tend to prioritize agility and rapid iteration. They may select lighter-weight, cloud-native frameworks that enable faster experimentation and lower operational overhead.
Our guidance is to use certification as a foundational assurance. Knowing the tools work well together, and in a next step, layer in strategic considerations: team expertise, deployment environment, data sovereignty, and roadmap alignment. Scality’s certification program is intentionally broad to support this flexibility, enabling both Fortune 500 firms and emerging AI innovators to assemble architectures that fit their unique growth trajectories.
Q5. As Scality expands this certification program, what emerging tools, frameworks, or AI development paradigms are you watching most closely? Are there specific gaps in the current AI tooling landscape (perhaps around model governance, observability, or multi-modal workflows) that you believe need standardized, certified solutions? How do you see the role of storage infrastructure evolving as AI models and datasets continue to grow in scale and complexity?
A: Looking ahead, we will see major opportunities for certification in areas that are still maturing across the AI ecosystem. This is particularly true for model governance, observability, and multi-modal data management. As organizations move beyond single-model workflows into federated, generative, and multi-modal architectures, the need for standardized, trusted interoperability becomes even more critical.
We’re closely watching emerging frameworks that address responsible AI, explainability, and automated lineage tracking, as these will require tight integration with storage systems capable of preserving not just data, but context and metadata at massive scale.
Storage infrastructure is rapidly evolving from a passive repository to an active enabler of AI agility and compliance. As models and datasets continue to grow exponentially, the ability to store, version, and secure data intelligently, while enabling high-performance access across distributed environments, will define the next generation of AI platforms. Scality’s focus is to ensure that this foundation remains both cyber-resilient and future-ready.
…………………………………………….
Director, Partner Applications Integrations. With over 15 years of experience in the software industry, Rahul is a well-rounded engineering and product leader based in San Francisco. His mission is to certify and ensure that partner applications achieve the best performance with Scality’s products, which are scalable distributed services for data storage and management.
Sponsored by Scality