Many companies are investing heavily in GenAI and machine learning right now. The results, however, often fall short of expectations.
The common thread behind these numbers: it's rarely the models or tools that are missing. What's missing is a solid technical foundation to run ML and GenAI applications reliably, transparently, and maintainably in production. That's exactly the domain of MLOps.
MLOps Is the Logical Evolution of DevOps
DevOps became standard practice in traditional software development because it delivers three things: reproducible artifacts through versioned code, automated tests and deployments, and continuous monitoring in production.
MLOps applies this same principle to machine learning and GenAI systems — with one key extension: instead of a single artifact (code), there are now three that need to be versioned together: code, model, and the underlying data snapshot. Testing and monitoring expand accordingly: model behavior has to be validated against known data points, and production monitoring must also watch for data drift, concept drift, and model degradation.
The result of a consistently implemented MLOps approach: shorter time-to-market, reproducible behavior across environments, and far fewer nasty surprises in live operation.
Why an Off-the-Shelf Solution Rarely Suffices
The obvious question is why not simply solve MLOps with one of the many off-the-shelf frameworks available. In practice — including at a large home appliance manufacturer — three recurring reasons stand out:
Heterogeneous Use Cases
Slow- and fast-moving data, image and NLP applications, sensor data from IoT/IIoT environments — all of this calls for a flexible, modular pipeline rather than a rigid one-size-fits-all solution.
Accumulated Complexity
Many code versions scattered across Jupyter notebooks, hundreds of business users with thousands of roles, high turnover in the data science team — this can only be managed with clear structure and governance.
Limits of Standard Platforms
Off-the-shelf solutions are quick to deploy but quickly hit their limits and are often hard to extend — as a "black box." What works better is deliberately combining best-of-breed components (e.g. MLflow with a specialized monitoring solution) instead of committing to a single platform.
A Pipeline With Five Layers
A robust MLOps pipeline can be thought of as a layered model spanning data ingestion, preprocessing, training, and deployment through to live operation:
Every layer builds on the previous one. Starting GenAI applications without first solving governance and data quality means building on an unstable foundation — which explains a good part of the failure rates cited at the start.
Practical Recommendations
A few clear guardrails emerge from multiple projects:
- Start by building a solid understanding of current and foreseeable future requirements — and use that to find the right balance between standard and custom solutions.
- Design the pipeline so individual components remain swappable. Tools and vendors evolve fast.
- Start early and small (proof of concept), then expand continuously — fail fast rather than big bang.
- For recurring requirements, look for generic solutions rather than one-off fixes.
- Allow enough time for data governance — it's routinely underestimated in practice, especially when rushing into LLMs and GenAI.
MLOps isn't another tool — it's an operating discipline: the consistent application of proven DevOps principles to the added complexity of models and data.
Conclusion
Companies that get this foundation right move noticeably faster and more reliably from idea to production — and avoid most of the costs that would otherwise only surface after launch.
Let's talk about your data and AI platform.
Tell us what you're building or modernizing — we'll tell you honestly whether we're the right fit.
Eric Joachim Liese
Datics is a boutique consultancy for industrial companies that need scalable, production-ready data and AI platforms — not another slide deck. Contact: info@datics-consulting.com