This is the final blog in a four-part series on DataOps and AIOps. Read the first blog here, the second blog here, and the third blog here.
How DataOps Started as Pure Data – But Things Have Changed!
Let’s start with a bit of history. DataOps used to be all about data. When it started, everything was centered around managing SQL pipelines, transforming data, and ensuring smooth operations. It was straightforward—just pure data-focused work.
But as technology evolved, platforms like Snowflake introduced new capabilities. One of the game-changers was Snowpark, which suddenly let developers work with Java and Python inside Snowflake, not just SQL anymore. That was a big shift. Now, you weren’t just managing data—you were developing code. And with code comes all the complexities of software development. Enter DevOps—version control, automated testing, continuous integration, and deployment pipelines became part of the game.
And then came containerization. Developers could start packaging their applications in containers, meaning you needed CloudOps skills to handle infrastructure management, scaling, and deployment in cloud environments. What started as pure DataOps quickly evolved into a mix of DataOps, DevOps, and CloudOps.
Now, to be really effective in the Snowflake ecosystem, data engineers and teams need to have expertise in all three: DataOps, DevOps, and CloudOps. That’s a tough skill set to acquire, especially when so many teams are still struggling just to hire solid data engineers. But that’s where DataOps.live has helped so many companies in the past 4 years - it abstracts away the complexities of DevOps and CloudOps, so data engineers can be super productive without needing to become experts in everything. Essentially, a great data engineer with DataOps.live can operate as efficiently as one supported by a whole team of DevOps and CloudOps pros.
AIOps – Is It Just Another Extension of DataOps
So, what’s AIOps? At its core, it’s all about operationalizing AI models—taking care of everything from training to deployment to ongoing monitoring. Think of it as managing the life of an AI model and ensuring it performs well continuously. But here’s the catch: AI models rely heavily on solid data pipelines and infrastructure to work properly, which brings us back to DataOps.
DataOps handles the flow of data in an organization—making sure it’s ingested, cleaned, validated, and used effectively. And when it comes to AI, those same pipelines are what feed the models with data. DataOps provides the foundation for AIOps, ensuring the AI can function at its best.
LLMOps: The New Kid on the Block (But It’s Part of AIOps Too!)
Then we have LLMOps, which is just a specialized subset of AIOps that focuses on managing Large Language Models (LLMs). You know, the models powering everything from chatbots to text generation. LLMOps handles fine-tuning these models on specific data, keeping costs in check, and making sure they stay relevant.
But here’s the thing: LLMOps doesn’t exist on its own either—it’s tied to DataOps, just like AIOps. Fine-tuning a language model is kind of like transforming data in a pipeline. The tools and processes are similar, it’s just that instead of producing a data output, the result is a new, better (hopefully – but that’s where automated testing and scoring come in as part of a DataOps pipeline) model.
Enter Snowflake and Cortex: Powerhouses for AI
This is where Snowflake and its powerful Cortex feature come into play. Snowflake isn’t just a data warehouse anymore—it’s a full-blown platform that can handle AI and machine learning. Snowpark lets you build and run sophisticated AI models, and Cortex takes things a step further by allowing you to fine-tune LLMs and other models with ease.
With Cortex, businesses can automate the entire AI lifecycle—from fine-tuning models to validating them and pushing them into production. The beauty of it is that you can continuously fine-tune models on fresh data, ensuring your AI stays sharp over time. And Snowflake makes the whole process scalable and easy to manage, especially when you’ve got solid DataOps practices in place.
Cortex also lets you quickly test different versions of models and compare their performance, which makes it easy for companies to stay on top of the latest AI advancements without a ton of manual effort. That’s the magic of combining DataOps and AI in one smooth process.
The Next Generation of LLMs: Multi-Agent Systems
As AI evolves, we’re moving from simple LLMs to more complex multi-agent systems. These systems involve multiple AI models working together—some may handle reasoning, others handle retrieval, and some are even action-based. It’s like a team of AI agents each playing a different role.
But with all this complexity comes the need for traditional software development practices. Now we’re not just tweaking one model, we’re managing a whole suite of models and microservices. This is where DevOps comes in.
Suddenly, we’re not just focusing on fine-tuning models—we need to manage the whole software stack that these models run on. These multi-agent systems rely on containerized environments, and that means CloudOps is needed to handle scaling, infrastructure, and uptime.
If this sounds familiar, it’s because it’s exactly what happened with data engineering. We started with pure data management, but as technology evolved, we had to combine data, software, and containers to get the job done. Now the same thing is happening in AI. To build these next-gen AI systems, we need a combination of DataOps, DevOps, and CloudOps.
The Power of Automation: DataOps.live Makes it Possible
Managing all of this—data pipelines, software development, and cloud infrastructure—sounds like a huge task. And it is! But the good news is platforms like DataOps.live make it manageable. By automating the tricky parts of DevOps and CloudOps, DataOps.live lets data engineers focus on what they do best: handling data.
Instead of needing an entire team of specialists, DataOps.live gives data engineers the tools to handle complex systems and automation without requiring them to become DevOps or CloudOps experts. It’s a game-changer for companies that want to be nimble and scalable without needing to hire a massive team.
Wrapping Up: AIOps, LLMOps, and the Future
AIOps and LLMOps might sound like new trends, but they’re really just the natural evolution of DataOps. As we dive deeper into the world of AI, especially with multi-agent systems, organizations need to think beyond traditional data management. They need a unified approach that combines DataOps, DevOps, and CloudOps.
Platforms like Snowflake and DataOps.live make this future possible by simplifying the complex processes of managing data and AI models, helping businesses focus on creating value without getting lost in the technical weeds.
The future of AI isn’t just about building smarter models—it’s about operationalizing them effectively and ensuring they deliver continuous value. And that’s where the magic of combining DataOps, DevOps, and CloudOps really comes to life.
