MLOps, short for Machine Learning Operations, refers to the work of enabling teams to train and run models at scale. It covers the systems and practices that turn experiments into production inference: how models are deployed, how data and features stay consistent over time, how performance is monitored, and how failures are caught before users notice.
While these challenges show up differently depending on role, they tend to surface across the entire ML lifecycle: from experimentation to deployment to long-term maintenance. Data scientists feel them when experiments don’t translate cleanly to production, data and ML engineers feel them when infrastructure becomes brittle or slow, and MLOps teams feel them when reliability, governance, and observability break down at scale.
Why Is MLOps So Challenging?
This work is difficult not because teams lack expertise, but because machine learning behaves fundamentally differently from traditional software. In standard applications, behavior changes only when engineers modify the code. In machine learning systems, behavior can change even when the code stays the same, because the data feeding the model is constantly evolving. User behavior shifts, upstream systems change, new edge cases appear, and distributions drift. As a result, production ML is not a one-time deployment, but a continuous process of adaptation.
While software engineering has had decades to converge on shared deployment patterns and tooling, MLOps is still a relatively young discipline. Many organizations are building their ML systems by stitching together tools that were never designed to work as a cohesive whole. The result is a recurring set of challenges that slow teams down and undermine model reliability.
Common MLOps Challenges
1. Fragmented Data + Loss of Traceability
Most production models rely on data from many different systems at once. Historical information typically lives in a data warehouse such as Snowflake or BigQuery, while real-time state comes from operational databases like Postgres or MySQL. Fast lookups may be served from key-value stores such as Redis or DynamoDB, and additional context is often pulled from third-party APIs for things like risk scoring, identity verification, or pricing.
Each of these systems updates on a different schedule and is owned by a different team. When a model’s predictions start to drift or degrade, it can be extremely difficult to determine which input changed or where the issue originated. Without clear data lineage and traceability, teams often spend days debugging symptoms rather than identifying root causes of model drift.
This lack of end-to-end traceability is why many teams look for a single system of record for features: one that can track where features come from, how they’re computed, and which models depend on them across both historical and real-time data. Without that shared foundation, observability and auditability are perpetually bolted on after the fact.
2. Feature Inconsistency
A feature can be an input or an output to an ML model, often derived by transforming raw data into a more useful signal through feature engineering. In many organizations, features are computed one way during model training and another way when the model runs in production.
For example, a user attribute might be backfilled in a warehouse for training purposes but be missing or delayed in a real-time operational database at serving time. Batch pipelines may handle timestamps or missing values differently than real-time systems. In other cases, engineers rewrite feature logic entirely when moving from notebooks to production APIs.
These differences rarely cause explicit failures. Instead, they introduce subtle inconsistencies that quietly degrade model performance over time: a phenomenon often referred to as train-serve skew. This is frequently where responsibility blurs between data scientists defining features in notebooks and data engineers rebuilding them for production systems, each with good intentions but no shared execution layer.
Eliminating this class of problems requires defining features once and reusing them everywhere, rather than relying on parallel pipelines and conventions to keep systems in sync.
3. Disconnected Training + Serving Environments
Training and serving ML models usually happen in different environments, with different assumptions about data. During training, models typically learn from historical data stored in data warehouses. Once deployed, those same models are expected to run inside production systems, executing on live, real-time data pulled from operational databases, APIs, caches, or streaming platforms like Kafka, often under much stricter latency and reliability constraints.
For ML engineers, this gap between experimentation and serving is where velocity is lost. Models ship, but confidence erodes once they hit production traffic. Each layer of this stack has its own dependencies, configurations, and failure modes. Small mismatches—such as differences in library versions or transformation logic—can lead to incorrect predictions without triggering errors. Because the system technically “works,” these issues can persist unnoticed for long periods.
Bridging this gap requires a runtime that can execute the same feature logic end to end, resolving data directly from source systems at inference time rather than relying on precomputed pipelines that are difficult to keep aligned.
4. Monitoring Systems Without Understanding Model Behavior
Most production monitoring focuses on infrastructure metrics like uptime and latency. While these signals are necessary, they are not sufficient for machine learning systems. A model can be serving predictions quickly and reliably while still producing worse outcomes.
This often happens when a model’s inputs change in subtle ways. A third-party API may alter a field format, a streaming pipeline might drop events, or a feature sourced from a cache could start returning null values. Without visibility into feature values, distributions, freshness, and provenance, teams often end up debugging the model itself instead of the data feeding it.
Closing this gap requires observability at the feature level, not just the service level, so teams can understand what changed, when it changed, and why it mattered. Some teams pair this with isolated experimentation environments that allow them to validate changes safely before rolling them out broadly.
5. Scaling Real-Time Inference Reliably
As models mature, they tend to depend on more features from more systems. What begins as a simple model using a handful of batch-computed features can evolve into a real-time service that requires multiple low-latency lookups across APIs, databases, and caches.
While batch pipelines can scale relatively easily for training, real-time inference introduces strict latency and reliability constraints. Each additional dependency increases cost, complexity, and the risk of failure. Systems that worked well for experimentation often become fragile when exposed to production traffic.
At this point, real-time inference stops being a modeling problem and becomes a systems problem: one that demands predictable latency, efficient execution, and careful control over what data is fetched and when.
6. Organizational Fragmentation Across Teams
MLOps challenges are rarely purely technical. They are often amplified by organizational boundaries. Data science teams typically define features against warehouse tables, engineers reimplement those features against production systems, and MLOps teams manage infrastructure separately. When a schema changes in a warehouse or a key changes in a cache, a downstream model owned by another team may break without anyone realizing it immediately.
Without a shared source of truth connecting data, features, and models, coordination becomes reactive rather than proactive. Changes are discovered through performance regressions rather than code review, and teams spend more time responding to incidents than improving systems.
7. Security, Access Control, and Compliance Constraints
Production ML systems routinely touch sensitive data, including transaction records, identity information, and behavioral events. Teams must control access across environments, audit how predictions were made, and demonstrate compliance with internal and external requirements.
When access controls, auditing, and governance are bolted on after the fact, they often slow teams down or block production deployments entirely. Security and compliance become obstacles rather than built-in properties of the system, making it harder to ship models confidently in regulated or high-stakes environments.
How Chalk Can Help
At their core, most MLOps challenges stem from fragmentation: different systems for training and serving, duplicated feature logic, and limited visibility into how models behave once they are live. Teams running high-stakes systems (such as real-time marketplaces, fraud detection, and credit decisioning) use Chalk to eliminate this fragmentation in practice.
Chalk provides a single place to define, compute, and serve features using the same code across experimentation and production, batch and real time. Feature consistency is enforced by design rather than left to convention. Built-in lineage, versioning, and feature-level observability make it possible to trace predictions back to their inputs and understand what changed when model behavior shifts.
Because Chalk runs directly in a team’s own cloud and integrates with existing data sources (including warehouses, operational databases, streaming systems, and APIs) teams do not need to move or duplicate their data. They get low-latency inference, predictable performance, and the ability to scale without maintaining parallel systems.
The result is simpler infrastructure and more reliable machine learning. Instead of stitching together pipelines and hoping they stay in sync, teams can focus on building production ML systems that are easier to reason about, easier to maintain, and faster to ship.
[See how teams use Chalk to run real-time ML] → Customer Stories
[Read the docs] → Docs
