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.

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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