The promise of machine learning (ML) to drive business value relies on organizations‘ ability to reliably build, deploy and manage ML models at scale. Enter MLOps – an emerging discipline focused on industrializing ML workflows to enable robust, continuous delivery of ML systems.
What exactly is MLOps? MLOps refers to the best practices that enable businesses to deploy and maintain machine learning models in production reliably and efficiently.
Much like DevOps revolutionized software development, MLOps introduces engineering rigor into machine learning to move models past proof-of-concepts into sustainable, impactful systems.
The MLOps market is predicted to grow exponentially – from $350 million in 2019 to $4 billion by 2025 according to Deloitte. However, over 60% of companies struggle to successfully deploy ML in real business applications.
Adopting MLOps is key to overcoming many bottlenecks in operationalizing AI – from integrating with existing data pipelines, tracking experiments systematically, monitoring model drift, maintaining rigorous model governance standards and enabling rapid iterations on features.
In this comprehensive guide, we explore the top 8 MLOps best practices that can set your organization up for ML success:
Table of Contents
Defining Business Problems
The first step to MLOps success starts even before you develop models – clearly defining the business problem you want to solve with ML. Setting precise, measurable goals aligned to business KPIs is crucial.
For instance, rather than a vague goal like "predict customer churn", aim for "reduce customer churn rate from 15% to 12% in 1 year by developing an ML model for early churn prediction". This forces clarity on:
- Key objective – reducing customer churn
- Metric to track – monthly/annualized churn rate
- How ML aids this – early prediction to drive retention campaigns
Well-defined problems enable you to measure progress and ROI of the ML system accurately. It also helps streamline collaboration across business, data and ML teams to solve the right challenges.
💡 Best practice: Document business motivations, metrics, ground truths, applicable constraints and success criteria for every ML feature before kicking off development. Revisit and refine them iteratively.
Promoting Cross-Functional Teams
Since ML projects touch multiple organizational areas – from data infrastructure to subject matter expertise – collaboration across functions is key to operationalizing models successfully.
Teams that represent different competencies bring complementary skills:
- Business – frames problem, defines metrics, success criteria
- Data engineering – builds and maintains data pipelines
- Data science – transforms data, engineers features, develops ML models
- IT/DevOps – productionalizes models, monitors performance
Enable seamless coordination between the above units through protocols for communication and division of responsibilities.
💡 Best practice: Foster a culture of transparency, knowledge sharing and collective ownership between functions involved in the model lifecycle.
However, cross-functional collaboration also poses challenges:
- Domain complexity makes roles blurry
- Lack of shared vocabulary
- Mismatched incentives and timelines
Creating frameworks around decision rights, educational programs, team rituals and collaboration tools can go a long way in smoothing teamwork.
Cost-Benefit Analysis
ML projects require significant upfront investment – in technology, infrastructure, skills. Conducting a clear-eyed cost-benefit analysis is critical before undertaking any MLOps initiative:
Gather details on full costs:
- Cloud compute and storage for development, testing and production
- MLOps platforms and tools
- Data engineering, ML engineering and DevOps headcount
- Training and skill building
Then analyze expected business value (in $):
- Revenue upside or cost optimization from ML
- How quickly can breakeven be achieved
- Will benefits sustainably outweigh ongoing costs
This allows you to size investments appropriately and prioritize high-impact use cases. It also fosters realistic expectations on ROI timeline for leadership buy-in.
💡 Best practice: Clearly project cost and timeline to achieve ROI for each ML initiative before funding and resourcing. Prioritize opportunities accordingly.
Managing Data
MLOps introduces rigor into critical data management processes:
Sourcing Data
Identify trusted, reliable sources of input data for both development (training, validation) and production (scoring). Document any special legal/regulatory requirements for accessing the data.
Data Pipelines
Automate ETL (extract, transform, load) pipelines with workflow orchestration platforms to move data from sources into distributed storage like data lakes. Enforce orderly data flow.
Sanitizing Data
Cleanse, preprocess data to create high-quality, labelled datasets for model development.
Validating Data
Continuously monitor datasets for drift – changes in input/output distribution over time that make models lose accuracy. Production data behave differently than static development data. So checking for drift allows taking preventive steps like retraining models or adjusting data pipelines.
💡 Best practice: Institute automated, industrial-grade data pipelines from sourcing to labeling, processing and analyzing data at scale. Monitor for drift.
High-quality data is the lifeblood of ML systems. So investing in MLOps data infrastructure provides long-term dividends.
Tracking Experiments
Iteratively building, testing and improving ML models involves running many experiments. Tracking them effectively is key to model versioning, reproducibility and auditability.
For each experiment, log:
- Model parameters and metrics
- Training data and preprocessing
- Results and evaluation metrics
- Generated artifacts like models, figures
Standardize experiment structures, naming conventions and metadata to enable organization-wide searchability, comparisons andreuse.
Platforms like 
Comet ML provide dedicated experiment tracking capabilities – from logging to visualization to model registry.
💡 Best practice: Enforce consistent logging for all experiments across projects to build organizational knowledge. Enable reuse of promising models.
Treating experiments as first-class artifacts advances iterative development and collaboration on ML initiatives.
Monitoring Models
The job doesn‘t end once models are deployed. You need to continuously monitor predictive performance and data drift between development, test/staging vs production environments.
Key aspects to monitor
Data Drift
- Compare distributions for all input features
- Detect shifts indicating drop in model accuracy
Model Performance
- Key metrics like AUC, F1 scores, recall
- Business KPIs model aims to improve
Errors and Anomalies
- Prediction errors and failures
- Code exceptions and infra bottlenecks
When monitoring reveals deteriorating performance or unacceptable predictive bias, quick preventive or corrective action becomes possible – like retraining models on updated data or tuning decision thresholds.
💡 Best practice: Set up continuous, automated monitoring on business metrics, model metrics, data drift. Enable observability into all aspects of production ML.
This tight feedback loop between ops telemetry and model tuning is a hallmark of MLOps transformation.
Automating Pipelines
The need to retrain models on newer data requires automating the end-to-end ML pipeline – from data acquisition to model deployment. MLOps introduces DevOps-style automation, enabling reliable continuous development and delivery for ML systems.
- CI/CD pipelines – automate model rebuilds, testing, versioning and releases
- Infrastructure orchestration – programmatically scale compute for experiments, training
- Containerization – package models, environments into containers
Standardizing deployment templates and integrating ML pipelines with existing systems accelerates change delivery. Testing locally and only promoting vetted models to staging/prod reduces risk.
💡 Best practice: Adopt MLOps CI/CD tools like Kubeflow Pipelines to enable fast, repeatable model development from source code to deployment.
End-to-end automation and integration unlocks rapid iteration so models stay relevant to changing business environments.
Model Governance
For many regulated industries or applications involving personal data, rigorously governing ML systems becomes crucial. MLOps provides guardrails regarding ethics, compliance and responsible AI:
- Auditability – facilitate model inspection by regulators
- Explainability – interpret model behavior and predictions
- Fairness – ensure models work well across customer segments
- Robustness – improve security, prevent adversarial attacks
- Compliance – enforce regulatory principles into model development
💡 Best practice: Embed cross-functional checks and balances at every stage of the model lifecycle – from problem formulation to continuous monitoring. Make ethical AI a shared responsibility.
MLOps enables not just the rapid scaling of ML but also responsible scaling – unlocking innovation together with trust and transparency.
Gear Up for MLOps
As organizations hurry to capitalize on AI‘s potential, MLOps delivers the blueprint for scaling and sustaining the promise of ML in the real world. Adopting these MLOps best practices is key to avoiding false starts and driving transformational outcomes.
The journey requires strategic investments and closer coordination between multiple competencies. But organizations that undergo this transformation equip themselves with durable competitive advantage as AI proliferates across industries.