MLOps: A Beginner‘s Guide to Machine Learning Operations

Machine learning (ML) has the potential to transform every industry by automating decisions and uncovering new insights from data. However, organizations face significant challenges in deploying and managing ML models in production to achieve a return on their investments. According to a 2020 survey by Algorithmia, 55% of organizations take more than a month to deploy a single model, and 40% of organizations spend more than $1 million annually on infrastructure and people to deploy models [source].

The root cause is a disconnect between the data scientists who develop models and the software engineers responsible for deploying and maintaining them in production environments. This is where MLOps comes in. MLOps (short for Machine Learning Operations) is an emerging practice that aims to unify ML system development and ML system operations by adopting DevOps practices like continuous integration and delivery (CI/CD), monitoring, and automation.

By streamlining the entire ML lifecycle, from data preparation to model deployment and monitoring, MLOps helps improve the velocity, quality, and reliability of deploying ML models into production. This beginner‘s guide will introduce you to key MLOps concepts and best practices, survey popular tools, and explain how MLOps can help your organization unlock the business value of machine learning.

The Challenge of Deploying ML Models to Production

Data scientists typically spend the majority of their time on offline experimentation – training and tuning models on historical data to achieve the best accuracy. However, developing a model is only a small part of the actual work required to deploy a model into production and have it drive business decisions.

Research by Google found that only a small fraction of a real-world ML system is composed of the ML code, as shown in the diagram below. The required surrounding infrastructure is vast and complex [source].

Source: Hidden Technical Debt in Machine Learning Systems, NIPS 2015

Data scientists are not typically trained in software engineering best practices like version control, testing, and CI/CD. As a result, they often develop models in a way that is not easily reproducible or scalable for a production environment.

Common issues include:

• Custom data preprocessing and feature engineering steps not automated or documented
• Hardcoded file paths and configurations that only work on a local environment
• Unstable or outdated dependencies on open-source libraries
• Lack of monitoring for data drift or model performance degradation over time

Meanwhile, software engineers tasked with deploying models often don‘t have in-depth knowledge about the model‘s internals, intended use cases, or potential failure modes. Deployment becomes a slow, error-prone process where the model doesn‘t behave as expected in production.

MLOps Principles & Best Practices

MLOps aims to unify the development and operation of ML systems by applying DevOps practices and promoting collaboration between data scientists and engineers. Key principles include:

• CI/CD for ML Pipelines: Automate building, testing, and deploying models with CI/CD pipelines to enable rapid iteration and reduce risk. Use orchestration tools like Apache Airflow or AWS Step Functions to create reproducible workflows.

• Versioning: Version the data, models, and code used to preprocess data and build models. Tools like Data Version Control (DVC) and MLflow model registry help track artifacts‘ lineage.

• Modularization: Encapsulate data preprocessing, feature engineering, and model code into modular, reusable components. Containerize model code and dependencies for consistency across environments.

• Testing: Write unit and integration tests for ML pipelines to verify functionality and catch errors early. Validate data schemas, check for data drift, and set accuracy thresholds to act as gates in deployment pipelines.

• Monitoring: Track model performance live in production, monitoring for divergence between predictions and actual values. Use statistical techniques to detect data drift and set up automated alerts.

• Governance: Establish cross-functional teams to oversee the production ML lifecycle. Create a model inventory with documentation on intended uses and update processes. Review models for fairness, security, and compliance.

Deploying ML Models to Production

There are several approaches to deploying ML models into a production system to serve predictions:

• Batch Inference: For non-urgent, high-volume predictions, run the model on a schedule (e.g. daily) on a large batch of data. Write results to a database or file store for downstream consumption.

• Real-time Inference: For low-latency predictions, deploy the model as a web service or API endpoint using a framework like Flask or FastAPI. Optionally, use a model serving platform like Seldon or KFServing to autoscale the API and handle advanced requirements like A/B testing or multi-armed bandits.

• Streaming Inference: To generate predictions on a continuous flow of data, deploy the model on a stream processing framework like Kafka Streams or Flink. The model processes each new record as it arrives.

• Edge Inference: To make predictions on resource-constrained devices like IoT sensors, mobile phones, or robots, optimize the model to run on the edge. Use libraries like TensorFlow Lite or CoreML to compress model size and runtime.

• Embedded Inference: If you need the lowest latency and control over hardware, compile the model to run directly on a CPU or GPU without dependencies on higher-level languages. Options include NVIDIA‘s TensorRT or Apache TVM.

According to the 2021 Kaggle State of Data Science survey, almost half of data scientists deploy models as REST APIs, followed by 25% deploying to batch production systems [source].

Monitoring ML Models in Production

Once a model is deployed, it‘s critical to monitor its performance over time to detect issues like data drift or model staleness. ML models often decline in accuracy as the world changes, leading to unreliable predictions.

Types of model decay include:

• Data drift: The statistical properties of the input data change over time, causing the model‘s accuracy to decrease. For example, a fraud detection model may stop working if the distribution of normal user behavior evolves.

• Concept drift: The statistical properties of the output change over time. The mapping from input to output has evolved. For example, consumer preferences for certain products may shift due to a pandemic.

To detect model decay, you should log the model‘s predictions and compare them to ground truth labels collected later. You can then visualize the divergence in the model‘s accuracy or F1 score over time.

Source: Monitoring Machine Learning Models in Production, Chip Huyen 2020

Open-source tools like Evidently or Prometheus with Grafana enable you to track data drift and model performance over time. They also let you set up automated alerts if metric thresholds are exceeded. Some commercial MLOps platforms provide deeper model monitoring capabilities, like Fiddler, Arthur, and WhyLabs.

When significant data drift or model decay is detected, you should retrain the model on more recent data to adapt to the evolving environment. MLOps teams often automate the retraining process by triggering pipeline runs when certain drift thresholds are exceeded or on a regular schedule (e.g. weekly).

Organizational Challenges & Best Practices

Adopting MLOps requires changes to team structure and skillsets in addition to new tools and processes. Key organizational best practices include:

• Cross-functional collaboration: Foster ongoing communication between data scientists, data engineers, ML engineers, and DevOps. Consider embedding data scientists into product teams or creating a dedicated MLOps function.

• Reskilling: Provide data scientists with software engineering training on topics like version control, unit testing, and API design. Help software engineers learn statistical concepts needed to work with data and models.

• Standardization: Create templates and best practices for ML projects, including standard project structure, naming conventions, and deployment processes. Standardization helps reduce duplication and increase efficiency.

According to the 2021 State of MLOps report by Comet, 52% of companies organized their teams in a hybrid fashion, with a centralized team enabling other teams. The top three challenges to further MLOps adoption were lack of skilled people, lack of alignment between teams, and governance challenges [source].

ROI of MLOps

Adopting MLOps practices and tools represents an investment, but the benefits can far outweigh the costs. Key advantages include:

• Faster time to value: Companies adopting MLOps deploy models 3-5x faster on average and update models 5-10x more frequently [source].

• Improved model performance: By monitoring and retraining models more frequently, companies can minimize model decay and maintain high prediction accuracy. One large retailer increased revenue by $25 million by reducing model error rate from 5% to 2% [source].

• Reduced risk: MLOps helps organizations govern models for security, fairness, and regulatory compliance. Automated testing and monitoring reduces the risk of unexpected model failures or data leakage.

• Resource efficiency: Automation helps data scientists and engineers be more productive while reducing costly errors. Standardized tooling and infrastructure also lowers maintenance overhead.

The Future of MLOps

As ML becomes more widely adopted, the MLOps ecosystem is rapidly evolving. Key trends shaping the future include:

• Convergence with DataOps: DataOps applies DevOps practices to data pipelines, focusing on automating and monitoring the data used to train ML models. MLOps platforms are increasingly integrating with data catalogs and quality tools.

• Emergence of Feature Stores: Reusable feature stores provide a centralized place to store and access features for training models. They aim to bring DevOps-like collaboration and automation to the feature engineering process.

• Continuous Learning: Expect to see more tools for automating model retraining and tuning hyperparameters in production as new data arrives. This allows models to continuously adapt to evolving environments.

• Explainable AI: As ML is applied to high-stakes decisions, regulators are drafting requirements for explaining model predictions. MLOps platforms will provide more tooling for generating model explanations and assessing fairness.

• Deployment Standardization: Technologies like Kubernetes and Kubeflow are emerging as standard ways to deploy and orchestrate ML pipelines across cloud and on-premise environments. Innovations in hardware like GPUs and TPUs are also making real-time inference faster and cheaper.

Conclusion

MLOps is a set of practices and tools that helps organizations develop, deploy, and maintain machine learning models in production reliably and efficiently. Adopting MLOps can increase the velocity of model updates, improve model performance, and reduce risk.

However, implementing MLOps requires significant changes to team structure, skillsets, and tooling. Organizations need to invest in cross-functional collaboration, standard processes, and integrated ML platforms.

The MLOps ecosystem is still nascent but evolving quickly. As ML becomes more widely adopted, expect to see continued innovation in areas like feature stores, continuous learning, and deployment infrastructure. Organizations that embrace these changes will be well-positioned to unlock the full business potential of machine learning.