# From siloed DataOps, MLOps, and LLMOps to a unified data-intelligence platform

DevFeed: [From siloed DataOps, MLOps, and LLMOps to a unified data-intelligence platform](<https://devfeed.tech/articles/from-siloed-dataops-mlops-and-llmops-to-a-unified-data-intelligence-platform-26354.md>)

Original publisher: [Read original article](<https://medium.com/udemy-engineering/from-siloed-dataops-mlops-and-llmops-to-a-unified-data-intelligence-platform-4400be283641?source=rss----19c6d3367ed4---4>)

Author: Rajit Saha

Published: 2025-08-04T18:03:19Z

Content type: opinion

Language: en

Sources: [Udemy Engineering](<https://devfeed.tech/sources/udemy-engineering.md>)

Topics: [DataOps](<https://devfeed.tech/topics/dataops.md>), [MLOps](<https://devfeed.tech/topics/mlops.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [Apache Spark](<https://devfeed.tech/topics/spark.md>), [Amazon Bedrock](<https://devfeed.tech/topics/amazon-bedrock.md>), [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>), [Amazon Redshift](<https://devfeed.tech/topics/amazon-redshift.md>), [Amazon SageMaker](<https://devfeed.tech/topics/amazon-sagemaker.md>), [apache-flink](<https://devfeed.tech/topics/apache-flink.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [aiops](<https://devfeed.tech/tags/aiops.md>), [amazon-s3](<https://devfeed.tech/tags/amazon-s3.md>), [analytics](<https://devfeed.tech/tags/analytics.md>), [apache-flink](<https://devfeed.tech/tags/apache-flink.md>), [apache-spark](<https://devfeed.tech/tags/apache-spark.md>), [bedrock](<https://devfeed.tech/tags/bedrock.md>), [dataops](<https://devfeed.tech/tags/dataops.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [llmops](<https://devfeed.tech/tags/llmops.md>), [mlops](<https://devfeed.tech/tags/mlops.md>)

## AI overview

The article describes how DataOps, MLOps, and AI/LLM Ops commonly rely on separate systems and teams for data processing, model deployment, inference, evaluation, orchestration, governance, and monitoring. It then introduces Databricks' Data Intelligence Platform as a unified environment intended to bring these domains together.

## Source excerpt

Introduction In modern data-driven businesses, the pace of innovation in analytics and artificial intelligence has outstripped the capacity of many teams. Three distinct disciplines emerged to handle this expansion: Data platform (DataOps) teams built data lakes on cloud storage such as Amazon S3, processed them with Apache Spark and Hive on EMR, ingested streaming data with Spark Structured Streaming or Apache Flink, and loaded tabular copies into MPP warehouses like Redshift for interactive SQL and BI. Cataloguing and governance were offloaded to external tools such as DataHub, and fine-grained access controls required third-party services like Privacera. This architecture worked, but it required separate workflows for batch and streaming, extra systems for lineage and governance, and a mosaic of operational teams. MLOps teams provided an additional layer. Data scientists used notebook environments (for example, Amazon SageMaker) to preprocess data, train, and evaluate models. Deploying models meant writing integration code to move features into a serving layer, to register models in disparate registries and to build custom APIs for inference. Feature stores and model registries were bought from additional vendors. Updates and monitoring were often manual processes. AI/LLM Ops teams are a new addition because generative AI requires specialized components: LLM gateways (e.g., Amazon Bedrock) to proxy access to foundation models; evaluation tooling to compare large language models; orchestration frameworks for agents; vector databases for retrieval augmented generation; and of course another layer of security, access management and cost control. These tools seldom integrate seamlessly with existing data and ML pipelines. This fragmented state makes it difficult to react quickly when product requirements change. Each new capability requires another system, another integration, and another team. Meanwhile, budgets tighten and go-to-market timelines shrink. The questio