13 MLOps Best Practices to Cut Deployment Time and Boost Model ROI

When a machine learning model is performing well in a controlled environment, it can be exciting. But moving that model to production, where it will make real-world predictions, can be a daunting challenge. Often, the transition between the testing and production environments reveals significant differences. These discrepancies can create unexpected errors that undermine the model's performance and threaten the project's success. MLOps best practices provide a roadmap for smoothly deploying AI models from research to production and maintaining them over time. This article will outline essential MLOps best practices to help you achieve your goals, like seamlessly deploying AI models at scale with speed, reliability, and minimal operational friction. We will also touch upon the difference between AI Inference vs Training.

One of the most effective ways to implement MLOps Best Practices and improve AI model deployment is to use AI inference APIs. These tools can help you achieve your objectives by smoothing out the deployment process so you can return to what matters most: your business.

What is MLOps, and What Makes It Unique?

Machine learning operations (MLOps) is now a core focus for many data leaders and practitioners, with interest increasing significantly in the past two years. This meteoric rise is driven by a significant challenge many organizations face; investment in machine learning and AI is not delivering the promised return on investment.

In 2019, Venturebeat reported that only 13% of machine learning models make it to production. While a recent poll by KDNuggets suggests an improvement in 2022, 35% of responders still cited technical hurdles preventing the deployment of their models.

How MLOps Boosts AI Deployment and ROI

McKinsey's State of AI 2021 report shows that AI adoption is up to 56% from 50% in 2020. As organizations ramp up investments in their machine learning systems and talent, the need to efficiently deploy and extract value from models becomes more apparent.

This is where MLOps comes in. Organizations that adopt MLOps successfully are seeing better returns on their investments. By adopting MLOps, data teams reduce the time needed to prototype, develop, and deploy machine learning systems.

What is MLOps? The Key to Efficient Machine Learning Operations

MLOps is a set of tools, practices, techniques, and culture that ensure machine learning systems' reliable and scalable deployment. To reduce technical debt, MLOps borrows from software engineering best practices such as:

• Automation and automated testing
• Version control
• Implementation of agile principles
• Data management

Machine learning systems can incur high levels of technical debt, as the models data scientists produce are a small part of a larger puzzle—one that is comprised of infrastructure, model monitoring, feature storage, and many other considerations.

What Makes MLOps Unique? The Complexities of Machine Learning Deployment

Productionizing and scaling machine learning systems bring in additional complexities. Software engineering mainly involves designing well-defined solutions with precise inputs and outputs.

On the other hand, machine learning systems rely on real-world data that is modeled through statistical methods. This introduces further considerations that need to be taken into account, such as:

• Data: Machine learning takes in highly complex input data, which must be transformed so that machine learning models can produce meaningful predictions.
• Modeling: Developing machine learning systems requires experimentation. To experiment efficiently, tracking data changes and parameters of each experiment is essential.
• Testing: Going beyond unit testing, where small, testable parts of an application are tested independently, machine learning systems require more complex tests of both the data and model performance. For example, testing whether new input data share similar statistical properties to the training data.
• Model Drift: Machine learning model performance will always decay over time. The leading cause of this is two-fold:
  • Concept Drift: The properties of the outcome we’re trying to predict change. A great example was during the COVID-19 lockdowns, where many retailers experienced an unexpected spike in products such as toilet rolls. How would a model trained on standard data handle this?
  • Data Drift: Where properties of the independent variables change due to various factors, including:
    • Seasonality
    • Changing consumer behavior
    • Release of new products
• Continuous Training: Machine learning models must be retrained as new data becomes available to combat model drift.
• Pipeline Management: Data must go through various transformation steps before it is fed to a model and should be regularly tested before and after training. Pipelines combine these steps to be monitored and maintained efficiently.

Principles for Successful MLOps

The Iterative-Incremental Process in MLOps

MLOps isn't a single process, but a set of processes and practices that aim to support the reliable development and delivery of machine learning applications. The complete process includes three broad phases:

Phase 1: Designing The ML-Powered Application

The first phase centers on deeply understanding the business context and data landscape, laying the foundation for a machine learning-powered application. At this stage, we:

• Identify the target user
• Define the problem to be solved
• Design an ML solution aligned with business goals

Most projects fall into two categories:

• Enhancing user productivity
• Increasing application interactivity

We begin by defining and prioritizing ML use cases, focusing on a single use case at a time, which is considered best practice. The design phase also includes analyzing the data required to train the model and establishing functional and non-functional requirements. These insights inform the architecture of the ML system, guide the serving strategy, and shape a comprehensive test suite to ensure the model’s reliability and performance.

Phase 2: ML Experimentation and Development

The second phase, ML Experimentation and Development, focuses on validating the feasibility of machine learning for the defined problem by developing a proof-of-concept model. This stage is iterative by design, involving continuous refinement across core activities:

• Selecting or adapting the correct algorithm
• Conducting robust data engineering
• Shaping the model architecture

The objective is to produce a high-quality, stable ML model that meets production standards. Each iteration sharpens the model’s performance and resilience, ensuring that what’s delivered is functional and production-ready.

Phase 3: ML Operations

The primary focus of the “ML Operations” phase is to deliver the previously developed ML model in production by using established DevOps practices such as:

• Testing
• Versioning
• Continuous delivery
• Monitoring

Interdependencies Across Phases

All three phases are interconnected and influence each other. For example, the design decision during the design stage will propagate into the experimentation phase and finally influence the deployment options during the final operations phase.

Automation in MLOps

The maturity of an ML process is defined by how automated its data, model, and code pipelines are. Greater automation accelerates model training cycles and reduces manual intervention. The core goal of MLOps is to streamline the deployment of ML models, integrating them seamlessly into software systems or exposing them as services.Calendar events, data changes, code updates, or system monitoring can trigger automation. Rigorous automated testing ensures:

• Early detection of issues
• Enabling faster fixes
• Continuous improvement

Progressive Automation in MLOps: From Manual Workflows to CI/CD Integration

To adopt MLOps, we see three levels of automation, starting at the initial level with manual model training and deployment and ending with the automatic running of both ML and CI/CD pipelines.

1. Manual Process

This is a typical data science process performed at the beginning of implementing ML. It is experimental and iterative. Every step in each pipeline, such as data preparation and validation, model training, and testing, is executed manually. The standard way to process this is to use Rapid Application Development (RAD) tools, such as Jupyter Notebooks.

2. ML Pipeline Automation

The next level includes automatically executing model training. We introduce continuous training of the model here. Whenever new data is available, the process of model retraining is triggered. This level of automation also includes data and model validation steps.

3. CI/CD Pipeline Automation

In the final stage, we introduce a CI/CD system to perform fast and reliable ML model deployments in production. The core difference from the previous step is that we now automatically build, test, and deploy the Data, ML Model, and the ML training pipeline components. The MLOps stages that reflect the process of ML pipeline automation are explained in the following table:

Here's a breakdown of the outputs generated at each stage of a typical MLOps workflow:

• Development & Experimentation (ML algorithms, new ML models): Source code for pipelines encompassing data extraction, validation, preparation, model training, model evaluation, and model testing.
• Pipeline Continuous Integration (Build source code and run tests): Pipeline components ready for deployment, including packages and executables.
• Pipeline Continuous Delivery (Deploy pipelines to the target environment): Confirmation of successful pipeline deployment with the new model implementation.
• Automated Triggering (Pipeline is automatically executed in production. Schedule or trigger are used): A trained model stored within the model registry.
• Model Continuous Delivery (Model serving for prediction): A deployed model prediction service (e.g., the model exposed as a REST API endpoint).
• Monitoring (Collecting data about the model performance on live data): A trigger to execute the pipeline or to initiate a new experiment cycle based on performance analysis.

After analyzing the MLOps Stages, we might notice that the MLOps setup requires several components to be installed or prepared.

Here's a description of the key components typically found in an MLOps setup:

• Source Control: Systems for versioning code, data, and ML model artifacts, enabling tracking of changes and collaboration.
• Test & Build Services: Utilizing Continuous Integration (CI) tools for:
  • Ensuring the quality and reliability of all ML artifacts (code, data schemas, models, etc.).
  • Building deployable packages and executables for ML pipelines.
• Deployment Services: Employing Continuous Delivery (CD) tools to automate the deployment of ML pipelines to the designated target environment (e.g., staging, production).
• Model Registry: A centralized repository for storing and managing trained ML models, often including versioning and metadata.
• Feature Store: A system for managing and serving preprocessed input data as features, designed for consistent consumption during both model training and online model serving.
• ML Metadata Store: A system for tracking crucial metadata associated with model training runs, such as:
  • Model name and version.
  • Training parameters and hyperparameters.
  • The specific training dataset used.
  • The test dataset used for evaluation.
  • Performance metric results.
• ML Pipeline Orchestrator: Tools and platforms responsible for automating and managing the execution of the various steps within ML experiments and production pipelines.

Continuous X

To understand model deployment, we define ML assets, including the trained model, its parameters and hyperparameters, training scripts, and the associated training and testing data.

Key concerns at this stage of this artifact are the:

• Identity
• Components
• Versioning
• Dependencies

The target deployment environment can range from microservices to infrastructure components. A deployment service must support orchestration, logging, monitoring, and alerting to ensure that models, code, and data are delivered and operated reliably in production.

Core Pillars of the MLOps Lifecycle

MLOps represents an engineering culture and set of practices that enable scalable, maintainable ML systems.

Key pillars include:

• Continuous Integration (CI): Goes beyond traditional software CI by validating not only code and components, but also data and model artifacts.
• Continuous Delivery (CD): Focuses on automating the deployment of ML training pipelines and model serving infrastructure.
• Continuous Training (CT): A core ML-specific practice that enables automated model retraining and re-deployment in response to data drift or new data availability.
• Continuous Monitoring (CM): Tracks production data quality, model performance, and business-level impact to ensure ongoing relevance and reliability.

Versioning in MLOps

The versioning aims to treat ML training scripts, models, and data sets for model training as first-class citizens in DevOps processes by tracking ML models and data sets with version control systems.

The common reasons when ML model and data changes (according to SIG MLOps) are the following:

• ML models can be retrained based on new training data.
• Models may be retrained based on new training approaches.
• Models may be self-learning.
• Models may degrade over time.
• Models may be deployed in new applications.
• Models may be subject to attack and require revision.
• Models can be quickly rolled back to a previous serving version.
• Corporate or government compliance may require an audit or investigation of the ML model and data; hence, we need access to all versions of the productionized ML model.
• Data may reside across multiple systems.
• Data may only reside in restricted jurisdictions.
• Data storage may not be immutable.
• Data ownership may be a factor.

Analogously to the best practices for developing reliable software systems, every ML model specification (ML training code that creates an ML model) should undergo a code review phase and be versioned in a VCS to make the training of ML models auditable and reproducible.

Experiments Tracking

Machine learning development is inherently experimental and research-driven. Unlike traditional software engineering, where features are built and tested linearly, ML development involves running multiple training experiments in parallel to identify the most effective model.

To manage this complexity, teams often isolate experiments using separate Git branches, each producing a trained model. These models are then evaluated against defined metrics to determine which will be promoted to production.

Tools like DVC (Data Version Control) extend Git to support this workflow by versioning data and models alongside code. Similarly, Weights & Biases (wandb) provides automated tracking of hyperparameters and performance metrics, streamlining the comparison and reproducibility of experiments.

Testing in MLOps

The complete development pipeline includes three essential components:

• The data pipeline
• The ML model pipeline
• The application pipeline

should be policy-compliant (e.g., GDPR). These requirements should be programmatically checked in both development and production environments.

Feature creation code should be tested by unit tests (to capture bugs in features).

Tests for Reliable Model Development

We need to provide specific testing support for detecting ML-specific errors.

Business-Centric Evaluation of ML Models

Effective ML model testing should go beyond standard loss metrics (e.g., MSE, log-loss) and ensure alignment with business goals such as revenue, retention, or user engagement. It's critical to validate that improvements in algorithmic performance translate into meaningful business impact.

One approach is to measure the correlation between loss metrics and business KPIs through small-scale A/B testing, including comparisons with intentionally degraded models to benchmark performance.

Model Staleness Test

The model is defined as stale if the trained model does not include up-to-date data and/or does not satisfy the business impact requirements. Stale models can affect the quality of prediction in intelligent software. Action: A/B experiment with older models. Including the range of ages to produce an Age vs. Prediction Quality curve to facilitate the understanding of how often the ML model should be trained.

Evaluating Model Complexity vs. Performance Gains

Assessing the cost of more sophisticated ML models.

Action:

By this separation, we distinguish three scopes for testing in ML systems:

• Tests for features and data
• Tests for model development
• Tests for ML infrastructure

Features and Data Tests

• Data validation: Automatic check for data and features schema/domain.
  • Action: Calculate statistics from the training data to build a schema (domain values). This schema can be used as an expectation definition or semantic role for input data during the training and serving stages.
• The feature importance test is used to understand whether new features add predictive power. Action: Compute correlation coefficient on feature columns.
  • Action: Train model with one or two features. Action: Use the subset of features “One of k left out and train a set of different models. Measure data dependencies, inference latency, and RAM usage for each new feature. Compare it with the predictive power of the newly added features.
• Drop out unused/deprecated features from your infrastructure and document it.
• Features and data pipelines ML model performance should be compared to the simple baseline ML model (e.g., linear model vs neural network).

  Ensuring Robust Model Validation with Independent Test Sets

  Validating the performance of a model. It is recommended that the teams and procedures for collecting the training and test data be separated to remove the dependencies and avoid false methodology propagating from the training set to the test set (source).
  
  Action: Use an additional test set that is disjoint from the training and validation sets. Use this test set only for a final evaluation.

  Bias and Fairness Evaluation in ML Models

  Fairness/Bias/Inclusion testing for the ML model performance. Action: Collect more data that includes potentially under-represented categories.
  
  Action: Examine input features if they correlate with protected user categories.

  Unit Testing in Machine Learning Pipelines

  Conventional unit testing for any feature creation, ML model specification code (training), and testing.

  ML infrastructure tests

  Training the ML models should be reproducible, which means that training the ML model on the same data should produce identical ML models.

  Managing Non-Determinism in ML for Effective Diff-Testing

  Diff-testing of ML models relies on deterministic training, which is hard to achieve due to the non-convexity of the ML algorithms, random seed generation, or distributed ML model training.
  
  Action: determine the non-deterministic parts in the model training code base and minimize non-determinism.

  
  Stress Testing and Resilience Validation for ML APIs

  
  Test ML API usage. Stress testing.
  
  Action: Unit tests to randomly generate input data and train the model for a single optimization step (e.g., gradient descent).
  
  Action: Conduct crash tests for model training. After a mid-training crash, the ML model should be restored from a checkpoint.

  Validating Algorithmic Soundness through Iterative Loss Checks

  Test the algorithmic correctness. Action: Unit test that the ML model training is not intended to be completed but to be trained for a few iterations and ensure that loss decreases while training.
  
  Avoid: Diff-testing with previously built ML models is challenging because such tests are hard to maintain.

  End-to-End Integration Testing for ML Pipelines

  
  Integration testing: The full ML pipeline should be integrated.
  
  Action: Create a fully automated test that regularly triggers the entire ML pipeline. The test should validate that the data and code successfully finish each stage of training and that the resulting ML model performs as expected. All integration tests should be run before the ML model reaches the production environment.

  Pre-Deployment Model Validation and Regression Testing

  Validating the ML model before serving it.
  
  Action: Setting a threshold and testing for slow degradation in model quality over many versions on a validation set.
  
  Action: Setting a threshold and testing for sudden performance drops in a new version of the ML model.

  Canary Testing and Model Parity Verification

  
  ML models are trained before serving.
  
  Action: Testing that an ML model successfully loads into production serving and generates predictions on real-life data as expected.
  
  Action: Testing that the model in the training environment gives the same score as the model in the serving environment. A difference here indicates an engineering error.

  Monitoring

  Once an ML model is deployed to production, continuous monitoring is essential to ensure it performs as intended and maintains alignment with business objectives.
  
  The following checklist outlines key model monitoring activities, adapted from “The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction” by E. Breck et al. (2017):

  • Monitor dependency changes throughout the complete pipeline to result in notification.
  • Data version change.
  • Changes in the source system.
  • Dependencies upgrade.
  • Monitor data invariants in training and serving inputs: Alert if data does not match the schema specified in the training step.
    • Action: Tuning the alerting threshold to ensure that alerts remain helpful and not misleading.
  • Monitor whether training and serving features compute the same value. Since training and serving features might be generated in physically separated locations, we must carefully test to ensure these code paths are logically identical.
    • Action:
      • Log a sample of the serving traffic
      • Compute distribution statistics (min, max, avg, values, % of missing values, etc.) on the training and sampled serving features and ensure that they match.
  • Monitor the numerical stability of the ML model.
    • Action: trigger alerts for the occurrence of any NaNs or infinities.
  • Monitor computational performance of an ML system. Both dramatic and slow-leak regression in computational performance should be noted.
    • Action: Measure the performance of versions and components of code, data, and model by pre-setting the alerting threshold.
    • Action: These metrics help estimate cloud costs. Collect system usage metrics, such as:
      • GPU memory allocation
      • Network traffic
      • Disk usage
  • Monitor how stale the system in production is. Measure the age of the model. Older ML models tend to decay in performance.
    • Action: Model monitoring is a continuous process. Before reaching production, it is essential to identify the monitoring elements and create a model monitoring strategy.
  • Monitor the feature generation processes as they impact the model.
    • Action: Re-run feature generation frequently.
  • Monitor degradation of the predictive quality of the ML model on served data. Both dramatic and slow-leak regression in prediction quality should be notified. Degradation might happen due to changes in data or differing code paths, etc.
    • Action: Measure statistical bias (average in predictions in a data slice). Models should have nearly zero bias.
    • Action: If a label is available immediately after the prediction is made, we can measure the quality of the prediction in real-time and identify problems.

  Assessing Production Readiness with the ML Test Score

  
  The “ML Test Score” measures the overall readiness of the ML system for production. The final ML Test Score is computed:

  • For each test, half a point is awarded for executing the test manually, with the results documented and distributed. A whole point is awarded if a system is in place to run that test automatically, repeatedly.
  • Sum the score of each of the four sections individually: Data Tests, Model Tests, ML Infrastructure Tests, and Monitoring.
  • The final ML Test Score is computed by aggregating the minimum scores for each section: Data Tests, Model Tests, ML Infrastructure Tests, and Monitoring. After computing the ML Test Score, we can reason about the readiness of the ML system for production.

  Here's a description of different maturity levels for productionizing Machine Learning models:

  • 0: More of a research project than a productionized system.
  • (0, 1]: Not untested, but it is worth considering the possibility of serious holes in reliability.
  • (1, 2]: A first pass has been at basic productionization, but additional investment may be needed.
  • (2, 3]: Reasonably tested, but more of those tests and procedures may be automated.
  • (3, 5]: Strong level of automated testing and monitoring.
  • > 5: Exceptional level of automated testing and monitoring.

  Reproducibility in MLOps

  Here's a breakdown of challenges and strategies for ensuring reproducibility across various phases of a Machine Learning lifecycle:

  • Collecting Data:
    • Challenges: Generation of the training data can't be reproduced (e.g., due to constant database changes or data loading is random).
    • How to Ensure Reproducibility:
      • Always back up your data.
      • Saving a snapshot of the data set (e.g., on the cloud storage).
      • Data sources should be designed with timestamps to retrieve a view of the data at any point.
      • Data versioning.
  • Feature Engineering:
    • Scenarios (Challenges):
      • Missing values are imputed with random or mean values.
      • Removing labels based on the percentage of observation.
      • Non-deterministic feature extraction methods.
    • How to Ensure Reproducibility:
      • Feature generation code should be taken under version control.
      • Require reproducibility of the previous step "Collecting Data".
  • Model Training / Model Build:
    • Challenge: Non-determinism.
    • How to Ensure Reproducibility:
      • Ensure the order of features is always the same.
      • Document and automate feature transformation, such as normalization.
      • Document and automate hyperparameter selection.
      • For ensemble learning: document and automate the combination of ML models.
  • Model Deployment:
    • Challenges:
      • Training the ML model with a software version different from the production environment has been performed.
      • The input data the ML model requires is missing in the production environment.
    • How to Ensure Reproducibility:
      • Software versions and dependencies should match the production environment.
      • Use a container (Docker) and document its specification, such as image version.
      • Ideally, the same programming language is used for training and deployment.

  Loosely Coupled Architecture (Modularity)

  In Accelerate, Gene Kim and colleagues emphasize that high performance in software delivery is achievable across diverse system architectures, provided those systems and the teams that develop and maintain them remain loosely coupled.
  
  This architectural principle is critical; it enables teams to test and deploy individual components or services independently. As a result, organizations can scale effectively without compromising agility or productivity.

  Benefits of Loosely Coupled Architectures in Scalable and Agile Teams

  This approach significantly influences teams’ ability to independently test and deploy their applications on demand without the need for orchestration with other services.
  
  A loosely coupled architecture empowers teams to operate autonomously, reducing dependencies on other teams for support or services. This independence accelerates workflows and enhances the organization’s overall agility, enabling faster value delivery.

  Achieving Modularity in ML Systems: Structuring Projects for Better Independence and Scalability

  In ML-based software systems, achieving loose coupling between machine learning components can be more difficult than traditional software components. ML systems have weak component boundaries in several ways. For example, the outputs of ML models can be used as inputs to another ML model, and such interleaved dependencies might affect one another during training and testing. Basic modularity can be achieved by structuring the machine learning project. To set up a standard project structure, we recommend using dedicated templates such as:

  • Cookiecutter Data Science
  • Project Template
  • The Data Science Lifecycle
  • Process Template PyScaffold

  ML-based Software Delivery Metrics (4 metrics from “Accelerate”)

  In the most recent study on the state of DevOps, the authors highlighted four key metrics that reflect the effectiveness of software development and delivery within high-performing organizations:

  • Deployment Frequency
  • Lead Time for Changes
  • Mean Time to Restore
  • Change Fail Percentage

  These metrics have proven valuable in assessing and enhancing the delivery of ML-based software.
  
  The following table provides the definitions of each metric and outlines their relevance to MLOps.

  Deployment Frequency:

  • DevOps: How often does your organization deploy code to production or release it to end-users?
  • MLOps: ML Model Deployment Frequency depends on:
    • Model retraining requirements (ranging from less frequent to online training), considering:
      • Model decay metric.
      • New data availability.
    • The level of automation of the deployment process, which might range between manual deployment and a fully automated CI/CD pipeline.

  Lead Time for Changes:

  • DevOps: How long does it take to go from code committed to code successfully running in production?
  • MLOps: ML Model Lead Time for Changes depends on:
    • Duration of the explorative phase in Data Science to finalize the ML model for deployment/serving.
    • Duration of the ML model training.
    • The number and duration of manual steps during the deployment process.

  Mean Time To Restore (MTTR):

  • DevOps: How long does it generally take to restore service when a service incident or a defect that impacts users occurs (e.g., unplanned outage or service impairment)?
  • MLOps: ML Model MTTR depends on the number and duration of manually performed model debugging and deployment steps. If the ML model should be retrained, then MTTR also depends on the duration of the training. Alternatively, MTTR refers to the duration of the rollback of the ML model to the previous version.

  Change Failure Rate:

  • DevOps: What percentage of changes to production or released to users result in degraded service (e.g., lead to service impairment or service outage) and subsequently require remediation (e.g., require a hotfix, rollback, fix forward, patch)?
  • MLOps: The ML Model Change Failure Rate can be expressed as the difference between the currently deployed ML model performance metrics and the previous model's metrics, such as Precision, Recall, F-1, accuracy, AUC, ROC, false positives, etc. ML Model Change Failure Rate is also related to A/B testing

  Measure the above four key metrics to improve the effectiveness of the ML development and delivery process. A practical way to achieve such effectiveness is to implement the CI/CD pipeline and adopt test-driven development for:

  • The Data
  • ML Model
  • Software Code pipelines.

  Related Reading

  • Model Inference
  • AI Learning Models
  • MLOps Architecture
  • Machine Learning Best Practices
  • AI Infrastructure Ecosystem

  5 Principles for Successful MLOps

  

  1. Understand Your MLOps Maturity: What’s Your Level?

  MLOps adoption is not straightforward. Organizations don’t simply start using a new tool and unlock better machine learning model operations overnight. Instead, MLOps implementation requires organizational changes that happen over time. Leading cloud providers like Microsoft and Google look at MLOps adoption through a maturity model. Before you can improve your machine learning operations, you must assess your organization’s current level of MLOps maturity. From there, you can identify what needs to change and create a plan to get to the next level.

  When to Invest in a Feature Store

  Understanding how maturity levels can help organizations prioritize MLOps initiatives is also essential. For example, a feature store is a single repository for data that houses commonly used features for machine learning.
  
  Feature stores are helpful for relatively data-mature organizations where many disparate data teams need to use consistent features and reduce duplicate work. If an organization only has a few data scientists, a feature store isn’t probably worth its time.

  2. Apply Automation in Your Processes: Let the Machines Do the Work

  Automation goes hand-in-hand with the concept of maturity models. Advanced and widespread automation facilitates an organization's increasing MLOps maturity. In environments without MLOps, many tasks within machine learning systems are executed manually. These tasks can include:

  • Data cleaning and transformation
  • Feature engineering
  • Splitting training and testing data
  • Writing model training code, etc.

  By doing these steps manually, data scientists introduce more margin for error and lose time that could be better spent on experimentation.

  Automating MLOps to Prevent Model Drift

  Continuous retraining is an excellent example of automation, where data teams can set up pipelines for:

  • Data ingestion
  • Validation
  • Experimentation
  • Feature engineering
  • Model testing and more

  Often seen as one of the early steps in machine learning automation, continuous retraining helps avoid model drift.

  3. Prioritize Experimentation and Tracking: Get Your Data Ducks in a Row

  Experimentation is a core part of the machine learning lifecycle. Data scientists experiment with datasets, features, machine learning models, corresponding hyperparameters, etc. There are many "levers" to pull while experimenting. Tracking each iteration of experiments is essential to finding the right combination. In traditional notebook-based experimentation, data scientists track model parameters and details manually. This can lead to process inconsistencies and an increased margin for human error. Manual execution is also time-consuming and is a significant obstacle to rapid experimentation.

  4. Go Beyond CI/CD: Think Outside the Box

  We've looked at CI/CD in the context of DevOps, but these are also essential components for high-maturity MLOps. When applying CI to MLOps, we extend automated testing and validation of code to apply to data and models. Similarly, CD concepts apply to pipelines and models as they are retrained. We can also consider other "continuous” concepts:

  • Continuous Training (CT): We've already touched on how increasing automation allows a model to be retrained when new data becomes available.
  • Continuous Monitoring (CM): Another reason to retrain a model is decreasing performance. We should also understand whether models are still delivering value against business metrics. Applying continuous concepts with automated testing allows rapid experimentation and ensures minimal errors at scale.

  5. Adopt Organizational Change: Change Your Company Culture

  Organizational change must happen alongside evolution in MLOps maturity. This requires process changes that promote collaboration between teams, resulting in a breaking down of silos.
  
  In some cases, restructuring overall teams is necessary to facilitate consistent MLOps maturity. Microsoft's maturity model covers how people's behavior must change as maturity increases.

  Breaking Silos with Collaboration and Automation

  Low-maturity environments typically have data scientists, engineers, and software engineers working in silos. As maturity increases, every member needs to collaborate. Data scientists and engineers must partner to convert experimentation code into repeatable pipelines, while software and data engineers must work together to integrate models into application code automatically.
  
  Increased collaboration makes the entire deployment process less reliant on a single person. It ensures that teamwork is implemented to reduce costly manual efforts. These different areas of expertise come together to develop the level of automation that high-maturity MLOps requires. Increased collaboration and automation are essential in reducing technical debt.

  13 MLOps Best Practices to Cut Deployment Time and Boost Model ROI

  

  1. Create a Well-defined Project Structure

  First things first, it’s always better to have a well-organized structure. It allows smooth navigating, maintaining, and scaling of projects, making them easier for team members to manage.
  
  Project structure here means we must comprehend the project from beginning to end, from the business problem to the production and monitoring needs.
  
  Here are some suggestions to help you optimize your project structure:

  • Utilize a consistent folder structure, naming conventions, and file formats to guarantee your team members can quickly access and understand the codebase’s contents. This also makes cooperating, reusing code, and overseeing the project easier.
  • Build a well-defined workflow for your team to adhere to. It should include guidelines for code reviews, a version control system, and branching techniques. Ensure everyone follows these standards to promote harmonious teamwork and reduce conflicts.
  • Document your workflow and ensure all team members can easily access it.

  Even though building a clear project structure can be a hassle, it would benefit your project in the long run.

  2. Adhere to Naming Conventions for Code

  Naming conventions aren’t new. For example, Python’s recommendations for naming conventions are included in PEP 8: Style Guide for Python Code.
  
  As machine learning systems grow, so does the number of variables. So, if you establish a straightforward naming convention for your project, engineers will understand the roles of different variables and conform to this convention as the project grows in complexity.

  Example naming conventions:

  • tname_merge and intermediate_data_name_featurize follow an easily recognizable naming convention.

  Looking closely, you’ll see:

  • Variable and function names are in lowercase and separated by underscores:
    • Storage_client
    • Publisher_client
    • Subscriber_client
  • Constants are in uppercase and separated by underscores:
    • PROJECT_ID
    • TOPIC_NAME
    • SUBSCRIPTION_NAME
    • FUNCTION_NAME
  • Classes follow the CapWords convention:
    • PublisherClient
    • SubscriberClient
  • Indentation is done using four spaces.

  Adhering to PEP 8 naming conventions makes the code more readable and consistent, making it easier to understand and maintain.

  3. Code Quality Checks

  Alexander Van Tol’s article on code quality puts forward three agreeable identifiers of high-quality code:

  • It does what it is supposed to do
  • It does not contain defects or problems
  • It is easy to read, maintain, and extend

  The CACE (Change Anything, Change Everything) principle makes these three identifiers significant for machine learning systems.

  The Impact of Code Quality in ML

  Consider a customer churn prediction model for a telecommunications company. During the feature engineering step, a bug in the code introduces an incorrect transformation, leading to flawed features used by the model.
  
  This bug can go unnoticed during development and testing without proper code quality checks. Once deployed in production, the flawed feature affects the model’s predictions, resulting in the inaccurate identification of customers at risk of churn. This could potentially lead to financial losses and decreased customer satisfaction.

  Code Quality Checks in MLOps

  Code quality checks—such as unit testing—ensure crucial functions perform as expected. Quality checks go beyond unit testing. Your team can benefit from:

  • Linters and formatters to enforce a consistent code style.
  • Bug detection before issues reach production.
  • Code smell detection (e.g., dead code, duplicate code).
  • Faster code reviews, boosting the CI process.

  Best Practice: Automate Code Quality Checks

  Including code quality checks as the first step of a pipeline triggered by a pull request is a good practice. The MLOps with AzureML template project provides an example.
  
  If you’d like to embrace linters as a team, here’s a great article to get you started: "Linters aren’t in your way. They’re on your side."

  4. Validate Data Sets

  Building high-quality machine learning models necessitates data validation. ML models can produce more accurate predictions using appropriate training methodologies and validating datasets.
  
  Additionally, it’s crucial to identify flaws in datasets during data preparation to prevent model performance decline over time.

  Key data validation tasks:

  • Finding duplicates
  • Managing missing values
  • Filtering data and anomalies
  • Removing unnecessary data bits

  Challenges in Data Validation

  Data validation becomes increasingly complex as datasets expand, containing training data in various forms and from multiple sources. Automated data validation tools help improve the overall performance of ML systems.

  5. Encourage Experimentation and Tracking

  Experimentation is a crucial component of the machine learning lifecycle. To determine the best combination, data scientists test various scripts, datasets, models, architectures, and hyperparameters.

  Challenges of Traditional Experimentation

  In conventional notebook-based experimentation, data engineers manually track:

  • Model performance metrics
  • Experiment details

  This manual process can lead to inconsistencies, human errors, and slow testing cycles. While Git helps track code, it fails to handle the version control of multiple ML experiments.

  A More Effective Approach

  Using a model registry offers a better solution by:

  • Tracking model performance efficiently.
  • Storing models for easy access.
  • Enhancing model auditing with quick rollbacks.

  Benefits of Experiment Tracking

  • Saves time by reducing manual labor.
  • Boosts the reproducibility of final results.
  • Encourages collaboration, ensuring insights and improvements are shared across teams.

  Empowering your team to share experiment results and insights fosters cooperation, improves processes, and aligns project goals.

  6. Enable Model Validation Across Segments

  Reusing models is different from reusing software. You need to tune models to fit each new scenario. To do this, you need the training pipeline. Models also decay over time and need to be retrained to remain functional.
  
  Experiment tracking can help us manage model versioning and reproducibility, but validating models before promoting them into production is also essential. You can validate offline or online.

  • Produces metrics (e.g., accuracy, precision, normalized root mean squared error) on the test dataset.
  • Evaluate the model’s fitness for business objectives using historical data.
  • Metrics are compared to existing production/baseline models before promotion.

  Efficient Experiment Tracking and Metadata Management

  • Provides pointers to all models for seamless rollback or promotion.

  Online Validation (A/B Testing)

  • Establishes the model's adequate performance on live data.

  Validating Model Performance Across Data Segments

  • Ensures models meet requirements across various segments.
  • Addresses bias in machine learning systems, which is increasingly recognized in the industry.

  A popular example is the Twitter image-cropping feature, which was shown to perform inadequately for some user segments. Validating performance across different user segments helps detect and correct such biases.

  7. Application Monitoring

  An ML model’s accuracy decreases when it processes error-prone input data. Monitoring ML pipelines ensures that data remains clean throughout business operations.

  Continuous Monitoring (CM) for Real-Time Detection

  To detect real-time performance degradation and apply timely upgrades, automating CM tools is the best approach when deploying ML models into production.

  Key Monitoring Metrics

  • Data Quality Audits: Ensuring clean and reliable input data.
  • Model Evaluation Metrics: Tracking response time, latency, and downtime.

  Case Study: E-Commerce Site Recommendations

  Consider an e-commerce site that generates user recommendations using ML algorithms. A bug in the system causes irrelevant recommendations, leading to:

  • Declining conversion rates
  • Negative business impact

  Implementing data audits and monitoring tools can prevent such issues, ensuring the ML model performs optimally after deployment.

  8. Reproducibility

  In machine learning, reproducibility is preserving every aspect of the ML system by reproducing model artifacts and results exactly as they are. The stakeholders can follow these artifacts as road maps to navigate the complete ML model development process.

  This is similar to the software code tracking and sharing tool that developers use – Jupyter Notebook. Nevertheless, MLOps does not have this documentation feature. One way to address this issue is a centralized repository that gathers the artifacts at various phases of model development.

  Why Reproducibility Matters in ML

  Reproducibility is especially crucial for data scientists because it allows them to demonstrate how the model generated results. With this, model validation teams can reproduce an identical set of outcomes. Other teams can use the central repository to work on the pre-developed model and utilize it as the basis for their work instead of starting from scratch.

  This ensures that no one’s work goes to waste and that it can always be of some value. Airbnb’s Bighead, for example, is an end-to-end machine learning platform in which every ML model is replicable and iterable.

  9. Incorporate Automation Into Your Workflows

  Automation is closely related to the concept of maturity models. Advanced automation enables your organization’s MLOps maturity to grow. Numerous tasks within machine learning systems are still performed manually, such as:

  • Data cleansing and transformation
  • Feature engineering
  • Splitting training and testing data
  • Building model training code

  Due to this manual process, data scientists are more likely to make errors and waste time that could be better allocated to experimentation. Continuous training is a typical example of automation, in which data teams set up pipelines for:

  • Data analysis
  • Ingestion
  • Feature engineering
  • Model testing, etc.

  It prevents model drift and is often regarded as an initial stage of machine learning automation.

  Automated ML Pipelines for Scalable MLOps

  Data engineering and DevOps pipelines aren’t different from MLOps ones. An ML pipeline is a procedure that manages input data flow into and out of a machine learning model. With automation in data validation, model training, or even testing and evaluation, data scientists can significantly save resources and speed up MLOps processes.
  
  This productized, automated ML pipeline can be reused repeatedly for future projects or phases to produce accurate predictions on new data.

  10. Evaluate MLOps Maturity

  Conducting regular assessments of your MLOps maturity supports pinpointing areas for improvement and monitoring your progress over time. To do that, you can use MLOps maturity models, such as the one produced by Microsoft. This will assist you in setting priorities for your project and guarantee that you are moving towards your objectives.
  
  Based on your MLOps maturity assessment results, you should establish specific goals and objectives for your team to strive toward.

  Measurable Goals for MLOps Improvement

  These objectives should be measurable, attainable, and aligned with the general goal of your ML project. Share these objectives with your team and stakeholders so everyone is on the same page and has a common idea of what you are working toward.
  
  MLOps is an iterative and ongoing process with room for improvement. Therefore, you should constantly evaluate and improve your ML system to satisfy the most recent best practices and technologies. Don’t forget to encourage your team to propose feedback and suggestions.

  11. Open Communication Lines Are Important

  Implementing and maintaining a machine learning system long-term requires collaboration between various professionals:

  • Data engineers, data scientists, machine learning engineers, data visualization specialists, DevOps engineers, and software developers.
  • UX designers and Product Managers influence how the product interacts with users.
  • Managers and Business owners, whose expectations shape how team performance is evaluated.
  • Compliance professionals ensure operations align with company policy and regulatory requirements.

  Effective Collaboration in ML Teams

  For a machine learning system to consistently achieve business objectives amid evolving user and data patterns, the teams involved in its creation, operation, and monitoring must communicate effectively.
  
  Srimram Narayan explores how such multidisciplinary teams can adopt an outcome-oriented approach to business objectives in Agile IT Organization Design. Be sure to add it to your weekend reads.

  12. Monitor Expenses

  ML projects demand a lot of resources, like:

  • Computer power
  • Storage
  • Bandwidth

  Keeping track of resource usage is essential to ensure you’re staying within budget and making the most of what you have.

  Optimizing Resource Allocation in ML Projects

  Various tools and dashboards allow you to track key usage metrics such as:

  • CPU and memory utilization
  • Network traffic
  • Storage usage

  Optimizing resource allocation permits you to cut expenses and increase the efficiency of your machine learning project.
  
  Employ tools and strategies like auto-scaling, resource pooling, and workload optimization to ensure your resources are used effectively and efficiently. Also, review and modify your resource allocation plan regularly, following your ML project's requirements and usage patterns.

  Choosing the Right Cloud Platform for ML Workloads

  Cloud platforms like Google Cloud, Microsoft Azure, and Amazon Web platforms (AWS) offer scalable, reasonably priced infrastructure for your machine learning applications. Auto-scaling, pay-as-you-go pricing, and managed services are all available in cloud services for your ML workloads.
  
  To choose the best fit for your business, weigh the pros and cons of each cloud service provider and their offerings.

  13. Score Your ML System Periodically

  If you know all the practices above, it’s clear that you (and your team) are committed to instituting the best MLOps practices in your organization. You deserve some applause!

  The ML Test Score Rubric

  Scoring your machine learning system is both a great starting point for your endeavor and for continuous evaluation as your project ages. Thankfully, such a scoring system exists.
  
  Eric Breck et al.. presented a comprehensive scoring system in their paper "What’s your ML Test Score?" The scoring system is a rubric for ML production systems and covers features and data, model development, infrastructure, and monitoring.

  Related Reading

  • AI Infrastructure
  • MLOps Tools
  • AI as a Service
  • Machine Learning Inference
  • Artificial Intelligence Cost Estimation
  • AutoML Companies
  • Edge Inference
  • LLM Inference Optimization

  Why Should You Incorporate MLOps Best Practices?

  MLOps was born out of the need to deploy ML models quickly and efficiently. More models are being developed, and companies are now heavily investing in machine learning, increasing the demand for MLOps. While MLOps is still in its early stages, organizations are looking to converge around principles that can unlock the ROI of machine learning.
  
  Incorporating MLOps best practices into your organization's workflow is essential for several reasons:

  • Faster development and deployment: MLOps streamlines developing, testing, and deploying ML models by automating repetitive tasks and promoting collaboration between data scientists, ML engineers, and IT operations teams. This results in a faster time-to-market for ML solutions.
  • Improved model quality: MLOps practices emphasize continuous integration and deployment (CI/CD), ensuring that models are consistently tested and validated before deployment. This leads to improved model quality and reduced risk of errors or issues in production.
  • Scalability and reliability: MLOps best practices enable ML solutions to scale efficiently and reliably by optimizing resource utilization, handling dependencies, and monitoring system performance. This minimizes bottlenecks, failures, or performance degradation in production environments.
  • Monitoring and maintenance: MLOps emphasizes continuous model performance monitoring and proactive maintenance to ensure optimal results. By tracking model drift, data quality, and other key metrics, teams can identify and address issues before they become critical.
  • Cost optimization: By automating processes, monitoring resource utilization, and optimizing model training and deployment, MLOps practices help organizations reduce infrastructure and operational costs associated with machine learning solutions.

  Related Reading

  • LLM Serving
  • LLM Platforms
  • Inference Cost
  • Machine Learning at Scale
  • TensorRT
  • SageMaker Inference
  • SageMaker Inference Pricing

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