26 The hardest part of building AI isn’t designing the model — it’s stitching together the dozens of tools that feed and run it. This challenge, known as toolchain fragmentation, stretches from data ingestion pipelines to model training, fine-tuning, and inference. Organizations often end up piecing together a patchwork of tools that don’t play well together. The result is added complexity and delays of 40–60%, a costly slowdown in a market where speed is everything.If you’re a Scality RING XP customer — or considering becoming one — our certification program helps you cut through that complexity. More than 20 tools have already been validated to work seamlessly with Scality for ingestion, orchestration, data management, model development, and deployment. That means you can spend less time troubleshooting interoperability and more time driving AI results, backed by a data platform built for high-performance AI, cyber-resilience, and multidimensional scale. Here’s a closer look at the Scality-certified ecosystem, stage by stage across the AI pipeline. Stage 1: Data collection AI starts with data, capturing it from diverse systems, streams, and sources. Certified tools automate and scale this critical first step. Apache Airflow: Orchestrates and schedules complex pipelines, making data workflows repeatable and reliable. Apache Flink: Provides real-time stream processing for event-driven data, enabling instant insights from high-volume feeds. Apache NiFi: Automates ingestion and data flow management with strong lineage and monitoring across diverse systems. Delta Lake: Adds transactional reliability (ACID compliance) to data lakes, ensuring consistent collection at scale. DVC (Data Version Control): Integrates with Git to track datasets and models for reproducible AI experiments. Kubeflow: Provides Kubernetes-native orchestration for data and ML pipelines, integrating tightly with modern AI stacks. LakeFS: Git-style versioning for massive datasets allows teams to branch and merge data just like code. Pachyderm: Ensures strong lineage and versioning, making every stage of data collection auditable and compliant. Stage 2: Data filtering Once collected, data must be filtered, structured and enriched to become useful for model development. Certified filtering tools accelerate this critical stage. Apache Arrow: An efficient, in-memory columnar format that accelerates filtering, analytics, and cross-system data interchange. Haystack: Provides filtering and retrieval for unstructured data, powering intelligent search and RAG pipelines. LangChain: Orchestrates data and models for LLM workflows, connecting data sources with AI applications. Ray: A distributed execution engine that powers data filtering, preprocessing, and large-scale ML tasks. Snorkel: Uses weak supervision to programmatically label datasets, dramatically reducing manual labeling overhead. Stage 3: Data cleansing Filtering helps structure data, but cleansing ensures reliability. This stage removes duplicates, fixes inconsistencies, and guarantees quality at scale. With certified cleaning tools, organizations can trust that training data is accurate, complete, and production-ready. Apache Arrow: Provides a standardized memory format for clean data interchange between tools without loss. Apache Flink: Cleans streaming data in-flight with real-time transformations and deduplication. Apache Spark: Powers large-scale ETL and distributed data wrangling for batch and streaming workloads. Delta Lake: Adds schema enforcement and data validation to keep lakehouse datasets consistent. DVC (Data Version Control): Tracks cleaned datasets with reproducible lineage to avoid “dirty data drift.” Pachyderm: Automates cleaning workflows with containerized pipelines, ensuring every transformation is versioned and auditable. Stage 4: Training Training is where models take shape and become the heart of AI. Certified frameworks here support everything from rapid prototyping to production-grade training. Apache Spark – Scales distributed data processing and ML training. bodo.ai – Supercharges Python and ML workloads with compiler-level optimizations for extreme performance. JAX – Accelerates high-performance machine learning with automatic differentiation. Kubeflow – Orchestrates end-to-end training pipelines on Kubernetes. Metaflow – Simplifies model training workflow management by orchestrating data pipelines, experiments, and versioning to ensure reproducible and scalable machine learning processes. MLFlow – Manages model training, tracking, and experiment management, enabling reproducibility and streamlined collaboration across machine learning workflows. PyTorch – Enables dynamic, developer-friendly deep learning. Ray – Enables distributed compute engine powering training workloads across GPUs and clusters. TensorFlow – Delivers production-grade distributed training. Stage 5: Fine-tuning Fine-tuning represents one of the most critical stages in modern AI development, where pre-trained models are adapted to specific domains, tasks, or organizational requirements. Certified tools ensure this process is efficient and reproducible. Apache Spark: Enables distributed fine-tuning of machine learning models by parallelizing data processing and training across large clusters, improving scalability and efficiency for big datasets. Haystack: Supports fine-tuning and adaptation of language models for RAG pipelines, enabling domain-specific AI applications. JAX: Optimizes gradient-based fine-tuning and research experimentation with a high-performance numerical computing library. Kubeflow: Handles orchestration and distributed fine-tuning. LangChain: Streamlines framework for fine-tuning and chaining large language models with custom data sources. MLflow: Provides comprehensive experiment tracking and model lifecycle management to drive systematic fine-tuning workflows. PyTorch: Adapts pre-trained models with a flexible deep learning framework and native fine-tuning capabilities. Ray: Accelerates fine-tuning with distributed parallel processing and hyperparameter optimization. TensorFlow: Applies transfer learning with robust fine-tuning APIs. Stage 6: Inferencing Production success depends on low-latency inference and scalable serving. Certified tools help deploy models reliably at scale. Apache Flink: Streams processing for real-time AI inferencing on live data streams. Apache Spark: Provides big data processing for batch inference and large-scale model predictions. Haystack: Provides a framework for building production NLP applications with document retrieval. Kubeflow: Orchestrates Kubernetes-native inference pipelines and model serving. LangChain: Deploys complex LLM applications with retrieval-augmented generation. Metaflow: Production ML infrastructure manages inference pipelines and model deployment. MLflow: Manages, versions, and serves models with version control and A/B testing. Pachyderm: Performs data versioning and pipeline orchestration for reproducible inference workflows. Ray: Scales inference with automatic load balancing and distributed execution. Weaviate: Powers semantic search, similarity matching, and retrieval-augmented AI recommendations with a vector database. Why certification matters Building an AI pipeline is complex enough without having to verify that every component plays nicely together. By certifying tools across every phase of the AI pipeline, Scality ensures you don’t have to spend valuable cycles testing, tweaking, and troubleshooting interoperability. Scality-certified AI tools and applications across every phase provide: Interoperability: Seamless integration across complex AI stacks saves you from configuration headaches and gives IT teams the confidence that workflows will run as designed. Cyber resilience: Protects your data pipelines and model integrity from corruption or compromise at every stage. Reduced risk: Eliminates costly integration delays with proven tool compatibility, accelerating projects and avoiding the 40–60% slowdowns caused by fragmentation. Future-proofing: Proactive certification keeps pace with an evolving AI landscape so you can adopt new tools without starting over. Your AI blueprint, powered with confidence As new AI frameworks and orchestration engines emerge every year, Scality remains committed to expanding ISV application certifications — validating the tools that matter most so data scientists and enterprises can focus on what drives real value. Armed with a trusted blueprint for building AI pipelines, you can move forward with confidence — accelerating time-to-value, reducing risk, and freeing your teams to focus on what matters most: delivering successful AI outcomes. Related blog articles: Scality partner application certification Enterprise AI in action — 5 real-world use cases powered by object storage The AI storage problem you didn’t see coming — and how Scality RING already solved it Stop building dumb chatbots with Scality RING: Smarter AI workflows
