Scaling machine learning is one of the most complex challenges in modern engineering. It’s not just about training smarter models, but about building resilient systems that can handle messy, ever-changing data, high user demand, and the constant pressure to innovate at speed. Few engineers know this landscape as deeply as Sai Vishnu Kiran Bhyravajosyula, Staff Software Engineer at Rippling. With more than a decade of experience, Sai has designed large-scale distributed systems and data platforms that power products used by millions around the world. Holding multiple U.S. patents in network optimization and content access, he has shaped the way organizations think about reliability, portability, and performance in multi-cloud environments.
In this conversation with AI Journal, Sai reflects on the mental shift from model-centric to system-centric thinking, the role of automation in keeping pipelines reliable, and the delicate balance between scaling aggressively and protecting team well-being. He also shares lessons on monitoring without alert fatigue, separating experimentation from production, and why collaboration between data scientists and engineers often matters more than any single piece of technology.
When scaling machine learning systems from a single laptop to a production environment, what do you see as the biggest mental shift engineers need to make?
The biggest mental shift is learning to see beyond the model itself and to think about the entire system it lives in. When you’re working locally, it’s easy to get hyper-focused on tweaking an algorithm to get the best possible score on a clean, static dataset. But in the real world, that model is just one small part of a living, breathing system that has to handle a constant, messy flow of new data, day in and day out. So the mental shift is from “model-centric thinking” to “system-centric thinking.”
In production, data is messy, streaming, and constantly changing, requiring pipelines, validation, and automated retraining. Latency and throughput become critical, so models must be optimized, batched, and served at scale. Monitoring is no longer optional — you need logs, drift detection, and alerts to ensure reliability. Business trade-offs also shift: the fastest or cheapest model often wins over the “fanciest” one. Ultimately, success is defined not by test accuracy but by reliability, scalability, and adaptability in the real world.
Can you share an example where automating a manual step in your ML pipeline had a measurable impact on efficiency or error reduction?
Automated feature drift monitoring is a huge leap forward for today’s ML pipelines. Feature drift happens when the incoming data starts to look different from what the model was trained on, and it can really mess up performance. In the past, catching feature drift was a pain. We either had to check manually, or we only found out about it after our model started performing poorly in the real world, which was often too late to avoid affecting users. Luckily, those days are over thanks to automated drift monitoring systems. These systems run continuously, crunching numbers like population stability index, KL divergence, and missing value rates for important features.
If the numbers show that the data is veering off course beyond a threshold, an alert automatically zips over to the on-call ML engineer. This automation cuts down the time it takes to spot drift from weeks to just a few hours, catching problems before they spiral out of control and hit users. Besides preventing errors, this automation also helps you ensure the retraining process is updated constantly. Basically, automated drift detection has changed model monitoring from a stressful, reactive task into a proactive guardian, making our models much more reliable.
Monitoring can easily lead to alert fatigue. How do you decide which metrics to prioritize, and how do you keep your team focused on what matters most?
Teams often get swamped with so many alerts that they start ignoring the really important ones. This is especially dangerous in machine learning, because models usually break down subtly due to changes in data, not with obvious crashes. To stay focused, you need to layer your monitoring and make sure the most urgent signals get attention first.
Start with the basics: Is the system up and running? The most crucial step is to keep a close eye on incoming data for unexpected shifts or degradation, which we call “data drift.” Catching data drift early is like seeing smoke before a fire; if you wait for your model’s accuracy to drop, you’re already dealing with the damage.
To avoid getting tired of alerts, every single alert needs to mean a clear, actionable task, not just more noise. This means using smart, dynamic thresholds that adjust to how your data naturally behaves, instead of relying on old, rigid rules. Plus, alerts should go to the exact person who can fix the problem, and related warnings should be grouped so one upstream issue doesn’t set off a bunch of alarms. Ultimately, beating alert fatigue is just as much about having clear ownership and disciplined processes as it is about the technology itself.
What practices have you found most effective in keeping experimental work safely separated from production environments?
Think of your production environment as a pristine, quiet system where everything works perfectly for your users. You wouldn’t want someone running a messy science experiment in the middle of it, which is why you build a completely separate workshop for all the experimental stuff. This separation is built on a foundation of tools like Git, versioning every change you make, and makes use of containers, like Docker, to package each experiment into its own clean, self-contained box so it can’t make a mess elsewhere.
This eliminates the classic “but it worked on my machine” problem that can cause chaos in production. The journey from idea to reality involves moving through distinct environments: a personal space for initial tinkering, a full-scale “staging” area that’s an identical twin of production, and finally, the live system itself. Before a new model ever faces a real user, deploy it in “shadow mode” to see how it performs with live data, completely risk-free. This lets you compare the new model directly against the old one without anyone ever knowing it’s there. Once it passes that test, use a “canary release,” slowly rolling it out to a tiny fraction of users to ensure it’s stable and effective. This entire process creates a resilient barrier that protects your users while giving your teams the freedom to innovate safely.
Scaling projects often push teams to their limits. How do you balance aggressive scaling demands with maintaining team well-being and avoiding burnout?
Aggressive scaling initiatives place immense pressure on engineering teams, and if not managed carefully, can lead to burnout, decreased productivity, and the erosion of the very team culture that drives success. The secret to sustainable growth isn’t just about better technology; it’s about deliberately protecting your team’s well-being and cognitive energy.
The common instinct to just throw more engineers at a problem is a trap; it often just slows things down by creating more communication chaos. Instead, the solution is to create smaller, autonomous teams that can work in parallel without constantly stepping on each other’s toes. During this growth, it’s vital to make your experienced core team members partners in the process, leveraging their deep knowledge and ensuring they don’t get lost in the shuffle. You also have to shield your teams from constant reorganizations, as each change resets their productivity back to zero while they learn to work together again. Throughout it all, leaders must be radically transparent, over-communicating the “why” behind the aggressive goals to build trust and reduce anxiety. Ultimately, balancing speed with sanity means treating your team’s structure and well-being with the same discipline you apply to your system architecture.
Ownership and accountability are critical in complex systems. What approaches have worked best for you to clearly define responsibilities across an ML stack?
In complex ML systems, clarity around ownership is everything. One approach that works well is to break the stack into layers—data, features, model training, deployment, and monitoring—and assign a clear owner for each. Using frameworks like RACI:
- Responsible (does the work)
- Accountable (owns the outcome)
- Consulted (provides input), and Informed (is kept up-to-date)
This framework helps make sure everyone knows who’s responsible versus who’s just consulted or informed.
Treat each component like a service with clear metrics or SLAs, so owners aren’t just building it. They’re accountable for performance and reliability. Regular handoffs and cross-team reviews help manage dependencies between data, engineering, and product without confusion. Finally, even experiments have temporary ownership, but once they’re production-ready, they get handed off to a stable owner. This way, everyone knows their domain, dependencies are clear, and the system runs smoothly without finger-pointing.
Many companies adopt internal tools or frameworks like feature stores or workflow platforms to manage growth. What tool or framework has had the biggest positive impact on your workflow, and why?
When teams grow, they often end up creating their own data pipelines independently. This leads to a lot of duplicated effort and inconsistencies between projects. The feature store is the best way to fix this. It acts like a central, shared library where all the useful, ready-to-use features for models are stored. This means everyone in the organization uses the same, accurate information. Its main job is to prevent “training-serving skew,” a sneaky problem where small differences between the data used for training and the data used in real-world production quietly make the model perform worse.
A feature store naturally solves this by making sure the feature logic is exactly the same for both training and real-world use. Plus, it completely changes how teams work together by offering a reliable place to contribute and reuse features. Instead of different teams making their own versions of things like “user activity,” there’s now one official version that everyone can trust. This not only speeds up development but also makes all ML projects more consistent and trustworthy. It’s a great example of how a technical solution can solve deeply human problems like poor communication and wasted effort, letting teams focus on building better models faster, instead of always starting from scratch.
Looking back on your own experience, what’s one lesson about scaling ML infrastructure that’s more about people and process than technology?
Scaling up machine learning (ML) isn’t just about the technology; it’s much more about people working well together. Think of it less like a relay race where you pass a baton, and more like a true team sport where everyone’s playing at the same time. A big problem often pops up because data scientists, who build the cool models, and engineers, who make them work in the real world, aren’t always on the same page.
These two groups often want different things. Data scientists are all about new ideas and making their models super accurate, so they might try out the latest, sometimes less stable, tools. Engineers, on the other hand, just want everything to run smoothly and reliably, so they prefer things to be standard and predictable. This often leads to data scientists just “throwing their models over the fence” to the engineers, which usually ends up being a messy, frustrating process with confused expectations and systems that break easily.
The real fix is to make ML a truly collaborative effort right from the start. Imagine one big, happy team—data scientists, data engineers, ML engineers, and operations folks—all focused on making one product awesome. This kind of teamwork needs a shared way of working, a “playbook,” that clearly shows how an idea goes from concept to being live and secure. Spending time upfront to figure out who does what, standardize how you work, and automate getting things deployed might seem like extra work, but it’s actually a super important investment for being fast and reliable in the long run.
Ultimately, MLOps is like the friendly bridge, the cultural and procedural glue, that connects these different viewpoints. Technology should always help the process, not tell it what to do. Building this collaborative culture and shared way of working is a much better sign of success than any specific algorithm or platform.
