The machinery behind machine learning is growing more complex, but the bottleneck often shows up in small, unglamorous work: backfilling data, retiring stale features, and keeping experiments moving without turning the platform into a maze. The MLOps market alone is projected to grow from $2.33 billion in 2024 to $19.55 billion by 2032, a signal that more companies are paying for operational consistency, not just better models. Surya Bhaskar Reddy Karri, an Engineering Manager at Pinterest, brings a practitioner’s view to that reality, shaped in part by his IJETCSIT membership, where the habit is to treat operational rigor as something you can explain, not just something you hope holds. To understand how teams are keeping ML development moving without letting backfills and feature sprawl take over, we turned to Karri.
Backfills Are Where Momentum Quietly Dies
“Backfills are rarely hard because the math is hard,” Karri says. “They hurt because they interrupt the day, and the interruption is unpredictable. Once a team learns to fear the process, they delay the work, and the platform starts to feel slow.” That fear is not abstract. A backfill request can land right before a review, when nobody wants to touch a brittle workflow that lives in scripts and tribal knowledge. In one common scenario, an engineer thinks they are signing up for a quick recomputation and ends up spending the afternoon chasing a missing config detail across repos and old notes. It is the kind of time sink that does not show up in a glossy platform diagram.
This pattern aligns closely with how Karri frames system decay more broadly in his book titled, “Architecting the Modern Web: Performance, Observability, and AI Integration”, he argues that platforms do not lose velocity because they lack features, but because everyday operations accumulate invisible friction. Performance degrades not only at runtime, but in the moments where engineers hesitate to act because the cost of intervention feels uncertain. Backfills sit squarely in that category: operationally necessary, cognitively expensive, and easy to postpone until the platform quietly drifts out of sync.
That friction is widespread because dataset upkeep is real work, not a rounding error. In a 2024 survey of analytics and data practitioners, 55% cited “maintaining or organizing data sets” as a top challenge, which is exactly the category that backfills fall into when they are manual and inconsistent. Karri’s answer inside MLHub was to take the Feature Backfill process out of multi-step Git-based workflows and put it behind a guided UI that engineers could run with confidence. The result was a 70% reduction in backfill kickoff time, from roughly two hours to under thirty minutes, because the platform carried the sequence and validation instead of the person carrying it in their head. One sentence mattered internally: if a backfill is routine, it should feel routine.
Metadata Stops Being a Nice-to-Have Once You Need to Retire Things
Platform teams can tolerate messy metadata right up until they cannot, usually the moment storage bills grow or feature sets balloon past what anyone can reason about. At that point, “What is this feature used for?” becomes a production question, not a documentation question. The pressure is also visible in the market around tooling that treats metadata as an operational asset: the global market for metadata management tools is expected to reach $33.4 Billion by 2030, reflecting how many organizations are trying to make lineage and usage legible enough to act on.
In MLHub, Karri’s core move was not a new dashboard, it was a dataset-to-model mapping framework that could be reconstructed automatically by correlating metadata that already existed in low-level layers. Feature access logs, training job configurations, and internal lineage artifacts each told part of the story, but they were never joined into a single view that could support decisions. “We were not missing data,” he explains. “We were missing correlation. Once you can map datasets to models consistently, lifecycle work stops being a debate and starts being a workflow.” That mapping became the backbone for scalable feature deprecation and cost governance, and he led the effort with a cross-functional team of six engineers spanning backend, data, and ML platform domains. The point was practical: you cannot safely remove what you cannot trace.
Adoption Is a Product Problem, Even When the Users Are Engineers
Once the platform can run a backfill without drama and can explain what a feature is attached to, the next question is whether people actually show up and use it. MLHub’s footprint at Pinterest grew to more than 800 monthly active users because it consolidated fragmented ML workflows into a single place, from dataset creation and feature engineering through training, evaluation, deployment, and ongoing governance. That adoption did not happen because teams were forced to migrate; it happened because the platform reduced the number of sharp edges in daily work.
A quiet but consequential design choice was backward compatibility. Karri pushed for a path where teams could adopt MLHub without rewriting existing pipelines, which meant the platform had to absorb complexity rather than export it. “If adoption requires heroics, it is not adoption,” he says. “It is a one-time migration story, and those don’t scale.” His work on foundational UI components used across MLHub modules reinforced the same theme: reduce cognitive load, keep the steps legible, and make the default path safe. That is also why his work has translated into public technical writing, including his DZone publication on integrating Node.js applications with MCP servers, where the emphasis is on integration patterns that hold up under real engineering constraints.
Cost Governance Gets Real When You Can Delete Confidently
Feature lifecycle management is easy to praise and hard to execute because the risk is asymmetric. Removing a feature that is truly unused is a win; removing a feature that is quietly critical is an incident. MLHub’s Feature Deprecation and Lifecycle Management module was designed to close that gap by programmatically detecting unused features, mapping datasets to models across heterogeneous teams, computing feature importance, notifying owners, and deprecating redundant, high-cost features safely. In practice, the best version of cost governance is not a quarterly cleanup, it is a system that can keep making the correct call.
The financial impact inside Pinterest was direct: MLHub drove $4.4M annual savings in S3 storage in 2025 through automated feature deprecation. That number matters because it is not an accounting trick, it is a signal that governance can be operationalized. “Storage costs are not just about bytes,” Karri notes. “They are about uncertainty. When you can prove what is used and what is not, you can act without fear.” The module’s value is that it makes deprecation a controlled process with traceable inputs, not a guessing game carried out by whoever happens to be brave that week.
The Next Phase Is Platforms That Keep Their Own Promises
As ML work scales, the pressure shifts from building capability to maintaining behavior. The market is moving in that direction too: IMARC estimates the MLOps market will reach $49.2 Billion by 2033, with an expected growth rate of 34.77% during 2025 to 2033. That trajectory implies more teams will be judged on repeatability: whether a backfill kicks off cleanly, whether a deprecation decision is explainable, and whether platform defaults keep people out of trouble.
MLHub’s operational scale offers a preview of what “repeatable” looks like when it is not a slogan. The backfill module alone reached 550+ monthly active users and supported 500+ automated backfills covering 6,000+ features, which is where process quality stops being optional. Karri’s view is that the next phase of ML platforms will be measured less by what they enable on paper and more by what they prevent in practice. “A good platform lets you move fast,” he says. “A great one lets you move fast twice, the same way, and sleep afterward.” His HackerNoon work on building an LLM-powered CLI tool in Python reflects the same instinct in a different form: make powerful workflows approachable, and keep the path from intent to execution honest.
