9 MLOps Best Practices and How to Apply Them

Machine learning operations (MLOps) is the process of standardizing, optimizing, and automating the development and deployment of machine learning (ML) models in large-scale production environments. Its practices manage ML models throughout the data science lifecycle, including model monitoring and maintenance after deployment.

As you may have heard before, deploying an ML model takes a lot of time and is extremely complicated, and only very few ML models can go into production successfully. As challenging as it is, there’s one workaround, which is to adopt good MLOps practices.

In this article, we’re going to discuss 9 MLOps best practices you should follow and brief you on how to apply them.

Let’s start unraveling each of them now!

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, which renders them easier to manage for team members.

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 it easier to cooperate, reuse code, and oversee the project.
• Build a well-defined workflow for your team to adhere to. It’ll include guidelines for code reviews, a version control system, and branching techniques. Also, make sure everyone follows these standards to promote harmonious teamwork and reduce conflicts.
• Make a documentation of 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. Code Quality Checks

Typically, high-quality code meets these three criteria:

• Do what it is supposed to do
• Not contain defects or problems
• Maintain easy to read, maintain, and expand

Because of the CACE (Change Anything Change Everything) principle, these three identifiers are particularly significant for ML systems.

Take a telecommunications company’s customer churn prediction model as an example.

During the feature engineering stage, a bug in the code can cause an improper transformation, resulting in faulty features employed by the ML model. This problem may go undetected during the development and testing step if there aren’t enough code quality checks performed. When the defective feature is launched in production, it impairs the trained ML model predictions, leading to incorrect identification of consumers at risk of churn. This might culminate in financial losses as well as lower customer satisfaction. Code quality checks (unit testing in this example) make sure that essential functions like this keep performing as intended.

However, code quality tests go beyond unit testing. Applying linters and code formatters to your machine learning project might also help your team enforce a particular code style. This way, you’re able to find and fix defects before they affect production, expedite the code review process, and quickly identify code smells such as duplicate or dead code.

It’s recommended to implement this code quality check as the initial stage of a pipeline triggered by a pull request.

3. Validate Data Sets

Building high-quality machine learning models necessitates data validation. When using appropriate methodologies for training and validating data sets, the finished machine learning models can produce more accurate predictions. In addition, to prevent the ML model performance from declining over time, it’s crucial to identify flaws in the data sets during data preparation.

Data validation tasks include:

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

When a dataset expands in size and contains training data in various forms and from many different sources, data validation gets more complex. Therefore, automated data validation tools can improve the overall performance of an ML system.

TensorFlow Data Validation (TFDV), for example, is a tool that developers often use to automatically generate schemas for data cleaning or detecting abnormalities in data, tasks that are laborious in traditional validation processes.

4. Encourage Experimentation and Tracking

One essential component of the machine learning lifecycle is experimentation. Data scientists perform experiments using various combinations of scripts, data sets, models, model architectures, hyperparameter values, etc. Since there are numerous ideas to try out when testing, it’s crucial to record every experiment iteration to determine the best combination.

In conventional notebook-based experimentation, data engineers manually track model performance metrics and details. This may lead to inconsistencies and a higher chance of human errors. Additionally, time-consuming, manual execution stands in the way of quick testing.

Although Git is widely used for tracking code, version control of the various experiments that ML engineers conduct still proves to be a challenging task. A more effective solution here is to use a model registry to track performance and store models.

Furthermore, tracking experiments allows data teams to rapidly bring back models if necessary, improving the entire model auditing process. It also saves a significant amount of manual labor for the data science team, freeing up more time for experimentation. All of this greatly enhanced the reproducibility of the final results.

Remember to empower your colleagues to share their experiment results and insights with the rest of the team. This will promote cooperation, reveal potential improvements, and maintain a common understanding of the project’s progress and goals.

5. Application Monitoring

An ML model’s accuracy starts decreasing when it uses input data prone to errors. To guarantee that the data sets that enter the ML model are clean throughout business operations, monitoring ML pipelines is a must.

For real-time model performance degradation detection and timely implementation of required upgrades, it’s best to automate continuous monitoring (CM) tools while deploying machine learning models into production. Apart from overseeing data set quality, these tools can monitor model evaluation metrics like response time, latency, and downtime.

An e-commerce site that frequently hosts a sale is a case in point. Assume the website generates user recommendations using ML algorithms. However, a bug in the ML system causes it to create irrelevant recommendations. As a result, the website’s conversion rate falls dramatically, affecting the whole business. However, such issues can be prevented if we implement data audits and monitoring tools carefully after deployment.

6. Reproducibility

In machine learning, reproducibility is the ability to preserve every aspect of the ML system by reproducing model artifacts and results exactly as they are. The involved stakeholders can follow these artifacts as road maps to navigate the complete process of developing an ML model. This is similar to the software code tracking and sharing tool used by developers – 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. Because of its ability to demonstrate how the model generated results, reproducibility is especially crucial for data scientists. With this, model validation teams can reproduce the 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 makes sure that no one’s work goes to waste and 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.

7. 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. However, numerous tasks within machine learning systems are still performed manually. Data cleansing and transformation, feature engineering, splitting training and testing data, building model training code, and more. Due to this manual process, data scientists might face a higher likelihood of errors and waste time that could be better allocated to experimentation.

Continuous training, in which data teams set up pipelines for data analysis, ingestion, feature engineering, model testing, etc, is a typical example of automation. It prevents model drift and is often regarded as an initial stage of machine learning automation.

Data engineering and DevOps pipelines aren’t really different from MLOps ones. An ML pipeline is a set of procedures that manage the flow of input data into and out of a machine learning model.

With such automation in data validation, model training, or even testing and evaluation, data scientists can significantly save resources and speed up MLOps processes. It’s because this productized automated ML pipeline can be reused again and again in the future for other projects/phases to produce accurate predictions on new data.

8. 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 make use of 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.

In light of the results of your MLOps maturity assessment, you should establish goals and objectives that are specific for your team to strive toward. These objectives ought to be measurable, attainable, and in accordance 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, and there is room for improvement. Therefore, you should always be evaluating and improving your ML system to satisfy the most recent best practices and technologies. Plus, don’t forget to encourage your team to propose feedback and suggestions.

9. Monitor Expenses

ML projects demand lots of resources, like computer power, storage, and bandwidth. Hence, it’s of great importance to keep track of your resource usage to ensure you’re staying within budget and making the most of what you have. By utilizing various tools and dashboards, you can track key usage metrics such as CPU and memory utilization, network traffic, and storage usage.

Optimizing resource allocation permits you to cut expenses and increase the efficiency of your machine learning project. To make sure that your resources are used effectively and efficiently, employ tools and strategies like auto-scaling, resource pooling, and workload optimization. Plus, review and modify your resource allocation plan regularly following the requirements and usage patterns of your ML project.

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

Ready to Apply MLOps Best Practices?

It’s inevitable for most companies to invest in MLOps these days, which allows efficient deployment of ML models in production and ensures reliable model performance over time.

By following these nine MLOps best practices, your organization can successfully employ ML models to achieve greater returns on investment.

Looking for help with your MLOps process? Neurond’s MLOps AI service is your way to go. Our services range from MLOps consulting to full development, empowering you to bring ML models to production, launch products, and make updates much faster. Our experts, with unrivaled expertise and experience, create tailor-made solutions by applying these best practices to meet your unique needs.

Whether you’re an enterprise seeking to automate your ML process or a startup in need of a scalable AI solution, we can help! Contact us now!