Take Your ML Models from Experiment to Production

What is MLOps?

MLOps (Machine Learning Operations) is the set of practices that combines Machine Learning, DevOps, and Data Engineering to deploy, maintain, and scale ML systems reliably and efficiently in production. It brings engineering discipline to the typically experimental and iterative world of data science.

The goal of MLOps is to bridge the gap between data science experimentation and production systems. While data scientists excel at building accurate models in notebooks, productionizing those models requires infrastructure, automation, monitoring, and operational practices that are distinct from traditional software engineering.

MLOps addresses the unique challenges of ML systems: data dependencies, model drift, experiment tracking, feature engineering, model versioning, and the need for continuous retraining. Without MLOps, organizations struggle to get value from their ML investments.

Why MLOps Matters

The statistics are stark: 87% of ML projects never make it to production. Even when they do, many fail to deliver sustained value because they lack the operational foundation to maintain quality over time. MLOps addresses this through:

• Reproducibility: Every experiment, training run, and deployment can be reproduced exactly, enabling debugging and compliance.
• Automation: Manual processes become automated pipelines, reducing time to deployment and human error.
• Monitoring: Production models are continuously monitored for performance, drift, and system health.
• Scalability: Infrastructure scales with demand, from training on GPU clusters to serving millions of predictions.

MLOps Maturity Levels

Organizations progress through maturity levels as their MLOps capabilities evolve. Understanding where you are helps plan the path forward.

Level 0: Manual ML

At this stage, data scientists work primarily in notebooks. Model training is manual and interactive. Deployment, if it happens, involves ad-hoc scripts and manual handoffs to engineering. There's no versioning, monitoring, or automation. Most ML projects start here.

Level 1: ML Pipeline Automation

The first major step is automating the ML pipeline—data preparation, feature engineering, model training, and validation happen in automated, reproducible workflows. Experiments are tracked, models are versioned, and there's a clear path from training to deployment. This level enables faster iteration and more reliable deployments.

Level 2: CI/CD for ML

At this maturity level, ML systems have continuous integration and continuous deployment just like software. Code changes trigger automated tests. Model updates go through validation gates. Deployment is automated with rollback capabilities. The ML system can be updated rapidly and safely.

Level 3: Full MLOps

The highest maturity includes comprehensive monitoring, automated retraining, feature stores, and self-service platforms. Models are continuously monitored for drift. Retraining triggers automatically based on performance degradation. Feature engineering is centralized and reusable. Data scientists can deploy models independently through platform abstractions.

ML Pipelines

ML pipelines are the backbone of MLOps—automated workflows that orchestrate the steps from raw data to deployed model. Well-designed pipelines are reproducible, scalable, and maintainable.

Pipeline Components

A typical ML pipeline includes these stages:

• Data Ingestion: Extract data from sources—databases, APIs, data lakes—and prepare it for processing.
• Data Validation: Check data quality, schema consistency, and detect anomalies before training.
• Feature Engineering: Transform raw data into features that ML models can use effectively.
• Model Training: Train models with tracking of hyperparameters, metrics, and artifacts.
• Model Validation: Evaluate model performance against baselines and business requirements.
• Model Registration: Store validated models in a registry with metadata and lineage.
• Model Deployment: Deploy approved models to serving infrastructure.

Pipeline Orchestration Tools

Common pipeline orchestrators include Kubeflow Pipelines (Kubernetes-native), Apache Airflow (general-purpose), Vertex AI Pipelines (GCP), SageMaker Pipelines (AWS), and modern tools like Prefect and Dagster. The choice depends on your cloud environment, existing tooling, and team preferences.

Feature Stores

Feature stores are centralized repositories for storing, managing, and serving ML features. They solve one of the most common problems in ML: training-serving skew—where features computed during training differ from those computed during inference.

Why Feature Stores Matter

• Consistency: Features are defined once and reused across training and serving, eliminating skew.
• Reusability: Features computed for one model can be reused by others, reducing duplicate engineering.
• Time Travel: Access historical feature values for point-in-time training data creation.
• Low Latency: Online stores provide millisecond-latency feature serving for real-time inference.

Feature Store Architecture

Most feature stores have two components: an offline store for batch feature access during training (typically a data lake or warehouse) and an online store for low-latency serving during inference (typically a key-value store like Redis or DynamoDB). Popular options include Feast (open-source), Tecton, and cloud-native stores from AWS, GCP, and Databricks.

Model Serving

Model serving is the infrastructure that runs ML models in production, handling inference requests at scale. The serving layer must be reliable, low-latency, and cost-efficient.

Serving Patterns

• Real-Time Serving: Synchronous inference for applications requiring immediate predictions—recommendations, fraud detection, chatbots.
• Batch Inference: Scheduled processing of large datasets for offline predictions—churn scoring, demand forecasting, lead scoring.
• Streaming Inference: Processing continuous data streams for near-real-time use cases.
• Edge Inference: Running models on edge devices for latency-critical or offline scenarios.

Deployment Strategies

Production deployments need safe rollout strategies. A/B testing compares new models against baselines with live traffic. Canary deployments gradually shift traffic to new models while monitoring for issues. Shadow deployments run new models alongside production without affecting users, validating behavior before switching.

ML Monitoring

ML models degrade over time as the world changes. Unlike traditional software that fails obviously, ML models fail silently—predictions become less accurate without any errors or crashes. Monitoring is essential for catching degradation early.

What to Monitor

• Model Performance: Accuracy, precision, recall, and business metrics tracked over time against baselines.
• Data Drift: Changes in input data distribution that may indicate the model is seeing different data than it was trained on.
• Concept Drift: Changes in the relationship between inputs and outputs—the world has changed in ways the model doesn't capture.
• Prediction Drift: Changes in model output distribution that may indicate problems even without ground truth labels.
• System Health: Latency, throughput, errors, and infrastructure metrics for the serving system.

Automated Response

Monitoring should trigger action. Alerts notify teams when metrics breach thresholds. Automated retraining pipelines can kick off when drift is detected. Rollback mechanisms revert to previous model versions when performance degrades significantly.

ML Governance

As ML systems make increasingly important decisions, governance becomes critical. ML governance ensures models are fair, explainable, compliant, and properly managed throughout their lifecycle.

Key Governance Areas

• Model Documentation: Every model should have clear documentation of its purpose, training data, limitations, and intended use.
• Lineage Tracking: Complete traceability from data sources through feature engineering, training, and deployment.
• Access Control: Role-based permissions for model development, approval, and deployment.
• Fairness & Bias: Testing for bias across protected groups and monitoring fairness metrics in production.
• Explainability: Understanding why models make specific predictions, especially for high-stakes decisions.

Regulatory Compliance

Industries like healthcare, finance, and insurance have specific requirements for ML systems. Model risk management, audit trails, explainability requirements, and data privacy regulations all shape governance needs. A proper governance framework enables compliance without slowing down ML development.

Building an MLOps Team

Successful MLOps requires the right skills and organizational structure. The gap between data science and production engineering needs dedicated roles to bridge it effectively.

Key Roles

• ML Engineer: Bridges data science and software engineering, building production ML systems and pipelines.
• Data Scientist: Develops and trains ML models, focusing on algorithm selection and performance optimization.
• MLOps Engineer: Focuses on infrastructure, CI/CD, monitoring, and operational aspects of ML systems.
• Data Engineer: Builds data pipelines and infrastructure that feed ML systems.
• Platform Engineer: Develops internal ML platforms that enable self-service for data scientists.

Team Structures

Some organizations embed ML engineers within product teams. Others have centralized platform teams that provide MLOps capabilities as a service. The right structure depends on ML maturity, team size, and how broadly ML is used across the organization.

Why Salt for MLOps?

Salt brings deep expertise in MLOps engineering and the practical experience to make your ML initiatives successful. Here's what sets us apart:

Production ML Experience: Our engineers have built and operated ML systems at scale across industries. We bring hands-on experience with SageMaker, Vertex AI, Kubeflow, MLflow, and the tools that power successful ML organizations.

End-to-End Capability: From pipeline development to model serving to monitoring, we handle the complete MLOps lifecycle. Our Data & AI Pods provide dedicated teams that own your MLOps delivery.

Business Value Focus: We don't build infrastructure for infrastructure's sake. Every engagement starts with understanding your ML use cases and designing the MLOps platform to accelerate business outcomes.

Pragmatic Approach: We recommend the right tools for your context, not the newest or most complex options. Start with quick wins, iterate based on feedback, and expand capabilities as value is demonstrated.

SPARK™ Framework: Our SPARK™ framework brings structure to MLOps initiatives. Clear phases, quality gates, and transparent communication ensure predictable delivery and stakeholder alignment.

Knowledge Transfer: We build your team's capability alongside the platform. Documentation, training, and embedded collaboration ensure your organization can own and evolve the MLOps platform independently.

Ready to operationalize your machine learning? Schedule an MLOps assessment with our team to discuss your ML goals and how Salt can help you build the MLOps platform that turns experiments into production value.