12 Essential MLOps Best Practices & Tips

Create a Clear Project Structure

An effective project development plan depends on a well-organized codebase. You can follow the practices below to enhance codebase navigation for all team members.

• Employ a standardized folder structure.
• Adhere to meaningful naming conventions.
• Utilize consistent file formats.

These practices promote smooth collaboration, facilitate code reuse, and simplify project maintenance. Additionally, a robust workflow is pivotal to streamlined team operations. To do so, you can follow the practices below.

• Implement clear guidelines for code reviews.
• Effectively utilize version control systems.
• Establishing well-defined branching strategies.

Adherence to these guidelines regularly minimizes conflicts and ensures seamless collaboration. Furthermore, readily accessible documentation of the established workflow empowers all team members with the necessary knowledge.

Code Quality Analysis

High-quality code typically meets three essential criteria: It performs its intended function, is free of defects or issues, and is easy to read, maintain, and expand. These criteria are especially critical for machine learning systems due to the CACE (Change Anything, Change Everything) principle, which emphasizes how a minor change in one area can have significant implications elsewhere.

Let’s take an example of a telecommunications company that uses an ML model to predict customer churn as an example. During the feature engineering stage, a bug in the code might result in improper data transformation, which will ultimately produce flawed features for the ML model.

If adequate code quality checks are not conducted, this issue might go unnoticed during development and testing. Once the defective feature is deployed in production, it can negatively impact the trained model’s predictions.

Ultimately, it results in the wrong identification of customers at risk of churn, financial losses, and reduced customer satisfaction. Robust code quality testing, like unit testing, ensures that critical functions like these work as intended.

However, maintaining code quality requires much more than unit testing. Tools like Linters and code formatters enforce a consistent coding style in ML projects. Hence, teams can easily identify and address defects before they reach production.

These tools also streamline the code review process and help detect issues such as duplicate or dead code. To maximize effectiveness, we advise you to include code quality checks as the initial step in a pipeline triggered by a pull request.

Automated Dataset Validation

It is important to check data quality rigidly before training any machine learning (ML) model. These checks identify and address issues such as missing values, duplicates, inconsistencies, and violations of predefined rules or business logic. You need to ensure data accuracy to minimize the risk of introducing errors or biases into the model.

Hence, it further enhances the overall performance of the model. Effective model training and evaluation require carefully dividing the dataset into distinct training, validation, and testing subsets.

This strategy mitigates the risk of overfitting, a phenomenon in which the model performs well on the trained data but poorly on new data. To maintain representative class distributions within each subset, stratified sampling techniques are often employed.

Collaborative Work Environment

A collaborative work environment is paramount for successful MLOps. Organizations can foster open communication, knowledge sharing, and teamwork among data scientists, engineers, and other stakeholders.

Moreover, clear communication channels, such as project management tools and instant messaging platforms, are necessary to prevent misunderstandings and delays.

Furthermore, knowledge sharing should be encouraged through documentation, presentations, and peer learning sessions to boost the team’s awareness. Also, it’s a good practice to utilize collaborative tools like Git, cloud-based platforms, and shared documentation platforms. This will further enhance collaboration and efficiency.

Lastly, constructive feedback should be taken through code reviews and regular retrospectives to identify and address issues proactively. Organizations can prioritize these best practices to achieve faster iteration, improved efficiency, and more successful ML projects.

Application Monitoring

This is another crucial best practice in MLOps that ensures the performance, reliability, and trustworthiness of machine learning models in production. It involves continuously tracking the behavior of deployed models and the applications they serve to identify anomalies, performance bottlenecks, or potential issues.

Monitoring helps detect data drift, concept drift, or changes in user behavior that might impact the model’s accuracy or relevance over time. By implementing robust monitoring tools and strategies, teams can proactively address challenges. Effective application monitoring supports model health and provides insights into real-world usage.

Experiment Tracking

Data scientists and developers frequently explore various model architectures for different business applications. It often conducts numerous experiments before selecting the most suitable candidate for production.

That’s why it’s crucial to maintain a comprehensive record of all relevant information to manage the iterative process. This information may include scripts, datasets, model architectures, and experimental results. This documentation facilitates informed decision-making when choosing the optimal deployment model.

Reproducibility

Machine learning (ML) reproducibility emphasizes capturing every aspect of the ML workflow, including model artifacts and results, for complete replicability. These artifacts serve as a comprehensive record, enabling stakeholders to trace the entire model development journey.

This principle mirrors the use of Jupyter Notebooks in software development, where developers document and share their code with colleagues. However, current MLOps practices often lack this level of systematic documentation.

Addressing this gap requires a central repository to store artifacts generated at each stage of model development. Reproducibility is crucial for several reasons.

Firstly, it allows data scientists to demonstrate how model results were achieved transparently. Secondly, it empowers validation teams to recreate the same results, ensuring accuracy and reliability independently.

Lastly, a central repository facilitates collaboration by enabling other teams to easily access and utilize existing models as a foundation for further development rather than starting from scratch. This accelerates progress and prevents duplication of effort

Airbnb’s Bighead is a prime example of a platform that prioritizes reproducibility. This end-to-end ML platform ensures that every model is reproducible and iteratively improvable.

Incorporate Automation into Your Workflows

Machine learning success relies on a mature MLOps practice, and automation is the basis of this maturity. Advanced automation streamlines many aspects, but numerous tasks within ML systems remain manual, including data cleansing, feature engineering, and data splitting.

This manual involvement increases the risk of errors. Continuous training, where automated pipelines handle data analysis, ingestion, feature engineering, and model testing, is a foundational step toward MLOps automation.

This approach mitigates model drift and ensures ongoing model performance. However, it’s important to note that MLOps isn’t very distinct from DevOps and data engineering principles. An ML pipeline arranges the flow of data through the entire model lifecycle, from ingestion to deployment.

By automating data validation, model training, and evaluation, organizations empower data scientists with significant resource savings and accelerated MLOps processes. These productized pipelines become reusable assets, facilitating rapid and accurate predictions of new data across various projects and phases.

Evaluate MLOps Maturity

Assess your MLOps maturity regularly to identify areas for improvement and track progress. Use existing MLOps maturity models, such as those provided by Microsoft, to evaluate your current state and pinpoint areas for enhancement.

This structured approach will help prioritize your efforts and ensure consistent progress towards your MLOps goals. Based on your maturity assessment, establish specific, measurable, achievable, relevant, and time-bound (SMART) goals and objectives for your team.

Clearly communicate these goals with your team and stakeholders to improve teamwork and a shared understanding of the desired outcomes. MLOps is an ongoing journey, so you need to refine MLOps practices continuously. This is implemented to ensure they align with the latest best practices and technologies.

Furthermore, encourage open communication and feedback from your team to identify areas for improvement. Review and adjust your MLOps processes regularly to adapt to evolving needs and maintain a robust and efficient machine learning workflow.

 Automate Model Deployment

This is another crucial aspect of MLOps, as it streamlines the process of moving models from development to production. Organizations can achieve this through various tools and techniques, such as:

• Containerization: Technologies like Docker package models and their dependencies in portable containers ensure consistency across different environments.
• Orchestration: Tools like Kubernetes can be used to manage and scale containerized models in production.
• Continuous Integration and Continuous Deployment (CI/CD): Implementing CI/CD pipelines automates the testing, building, and deployment of models, reducing manual effort and increasing efficiency.

CI/CD Pipeline Automation

CI/CD pipelines are essential for automating the model development and deployment process. They typically involve the following steps:

• Code Versioning: Storing code in a version control system like Git allows for tracking changes and collaboration.
• Automated Testing: Running tests on the code and models to ensure quality and identify potential issues.
• Continuous Integration: Automatically build and test the code whenever changes are made.
• Continuous Deployment: Automatically deploy the tested and validated models to production.

Control Data Labeling

Data labeling is also a critical step in the machine learning process, as it provides the necessary information for training models. To ensure data quality and consistency, it’s important to:

• Establish Clear Guidelines: Define clear guidelines for labelers, including instructions, examples, and quality standards.
• Use Multiple Labelers: Having multiple labelers annotate the same data can help identify inconsistencies and improve accuracy.
• Implement Quality Control Mechanisms: Regularly review labeled data to identify and correct errors.
• Utilize Active Learning Techniques: Prioritize labeling the most informative data points to improve efficiency and reduce costs.