Thanks to its numerous benefits, organizations are increasingly adopting machine learning (ML) into their product development processes. However, deploying a successful model isn't always easy, with Deeplearning.ai reporting that only 22% of companies using ML have successfully deployed a model. This is because ML involves using real-world data, which is in a constant state of entropy, leading to a more complicated and longer deployment process.
Therefore, there is a need for standard practices and guidelines that guide the ML process to help produce more reliable and performant models. The result is MLOps, a set of practices to deploy and maintain machine learning models in production reliably and efficiently. MLOps combines machine learning and the continuous development practice of DevOps in software development.
MLOps facilitates collaborations and efficient communication between operations and engineering professionals (such as data scientists and ML engineers) in the ML process, making it easier to align business and engineering needs with the models. MLOps involves the application of automation tools, workflows, and procedures in the model-building lifecycle to increase production speed and create a product that meets quality, compliance, and business needs.
This article explores how MLOps affects models in the ML lifecycle.
How MLOps Effects Model Results
MLOps applies to the entire ML lifecycle and includes phases like data gathering, model creation, orchestration, deployment, and governance. MLOps implementation can take various forms, either manually or through continuous integration and continuous deployment (CI/CD). Manual implementation involves script writing and doesn't typically include CI/CD pipelines. Unfortunately, this method can drastically reduce a model’s ability to adapt to data changes, causing the model’s performance to degrade.
The automated CI/CD approach to MLOps is far better and involves the following:
- Development and experimentation: Data scientists and engineers try to optimize algorithms by trying different combinations of features and hyperparameters in this stage. The output is usually source code. The code may be a collection of smaller modules used to create other components for easier management.
- Pipeline CI/CD and testing: Reiterative testing of the source code occurs in this stage to detect bugs or errors and test performance and accuracy as the code moves through various environments, ensuring the model performs as expected. Any new code triggers a code test in this stage to ensure code quality and keep the pipeline running.
- Continual learning in production using automation: Because engineers continuously feed data to a model, an ML pipeline should trigger automatic retraining and retest the model with new data. This practice ensures models perform optimally over the long term.
- Model continuous delivery and monitoring: Organizations offer the deployed model as part of a service to produce predictions and constantly monitor the performance of predictions.
MLOps Versus DevOps
Although MLOps and DevOps share common ground, employing automation and enabling collaboration between teams to create better products, they differ in their development processes, monitoring tools, and infrastructure. However, for MLOps, a crucial part of the lifecycle involves more complex data processing to meet the requirements of ML.
Although MLOps and DevOps employ the code-validate-deploy model building lifecycle, MLOps includes a data component that essentially introduces multiple steps (data cleaning, transformation, and analysis) into the lifecycle. Typically, DevOps involves creating a product that undergoes routine, conventional testing like unit and integration tests. For MLOps, in addition to code tests, other reiterative tests like model training and validation must be run, which prolongs the development time.
Model drifts resulting from a change in production environments and adding new data may reduce the model’s accuracy. Employing tools and procedures that ensure continuous monitoring and retraining of models with new data is vital to producing accurate models. Applying standard site reliability engineering (SRE) practices for traditional DevOps ensures that products operate optimally, as software products don’t depreciate as quickly as an ML model.
The deployment process is complex for MLOps. The development lifecycle implements the CI/CD pipeline for DevOps and validates code with tests. For MLOps, in addition to CI/CD pipelines, there are other steps like retraining models, validating models, model serving, maintenance, and monitoring model performance. This process means a more extended and complicated deployment time for MLOps.
Tools and Infrastructure
DevOps and MLOps rely on cloud tools and infrastructure to perform optimum operations. For example, MLOps relies on deep learning tools and frameworks, cloud storage options for storing massive datasets, GPUs for training deep learning models, and some DevOps tools. On the other hand, DevOps typically involve tools like servers, infrastructure as code (IaC) tools, and CI/CD tools to hasten the product’s lifecycle.
How MLOps Affects Performance
MLOps can be applied in organizations in various ways. For example, data scientists can use it for preventative maintenance in industrial systems and fraud detection in financial systems. In e-commerce, ML helps predict user behavior and patterns and uses these findings to target customers and offer product recommendations. However, models must be accurate to make correct predictions that meet business needs.
MLOps consist of three essential components: ML, data engineering, and deployment. All of these components play a role in the final quality of the deployed model.
Improved Data Usability
The accuracy of an ML model is only as good as the quality of data fed into the model. As an essential component of MLOps, data engineering ensures that only high-quality and meaningful data goes into the training data set. Data engineering helps identify and integrate data sources, clean and transform data into usable formats, document the metadata of datasets to help identify patterns, and ensure models meet data privacy and compliance regulations.
Continuous Testing and Monitoring
Reiterative testing and constant monitoring are vital to the ML pipeline. The ML workflow helps ensure the deployed model operates optimally and fulfills business requirements. It involves feeding data into the model, standardizing and identifying features that may improve model accuracy, and performing reiterative tests until the final model performs according to expectations.
Monitoring helps catch a drop in performance, which may result from data drift. Data drift occurs when the data used to train models changes or becomes outdated. Causes of data drifts could be the addition of new products or a change in customer experience. Some of the biggest changes in data result from world events and economics. For example, COVID-19 and the subsequent economic downturn caused notable shifts in a variety of data.
An efficient deployment pipeline employs logs and continuous reports to observe models in production and monitor performance. This pipeline also uses scheduling and automation to retrain datasets as new data gets reintroduced to the model, ensuring the models' long-term accuracy and performance.
Clear Business Objectives and Goals
MLOps helps establish a clear line of communication and an open workflow between operations and engineering teams. An open workflow with good communication helps facilitate a clear understanding of needs like cost and resource planning. Additionally, as business needs evolve and dependencies change, team members can communicate changes effectively and adapt the model according to evolving needs and key point indicators (KPIs).
Improves Accessibility with Feature Stores
Inaccessibility to raw data is a common issue faced by most ML teams. For data scientists, checking data validity, accuracy, and quality and sharing it with other ML team members can be a problem.
A feature store sits between data sources and ML models and enables easier collaboration, reduces the chances of data duplication, and ensures regulatory compliance. In addition, feature stores load data from multiple sources, allow data transformation and storage, and improve collaboration between ML teams. Members can easily share feature sets that work with other models or team members. Feature sets also ensure consistency across models and a smoother deployment process.
MLOps Best Practices
Setting up a successful MLOps strategy isn’t straightforward. You must consider the data, pipeline management, modeling and model drift, and continuous training. Here are some best practices to get your MLOps running and performing optimally.
Establish a Clear Naming Convention for Your ML Projects
Most ML projects involve numerous variables, which may become challenging to understand as projects grow in complexity. Establishing a straightforward naming convention from the start ensures ML engineers and data scientists understand the roles of each variable as the project size increases. Additionally, clear naming conventions make it easier to onboard new team members.
Employ Automation for Your Processes
Employing continual learning with new data ensures a model's long-term performance. Engineers can automate data ingestion, validation, experimentation, feature engineering, and model testing processes. Automation also reduces the risk of human errors and gives data scientists more time to experiment with models.
The quality of the model relies on the quality of the data fed into the model. Therefore, validating data at the point of entry is vital to a model’s success, as inaccurate data lead to misleading predictions and model drift. Validating data entails detecting errors and checking that the statistical properties of new data match those in the training data set.
Experiment Frequently and Track Results
To do the experimentation crucial to the ML lifecycle, models need a central store to store models and be able to backtrack to older versions of a model if the need arises. This practice is the model repository concept. A model repository contains all the models in a centralized location. To deliver the best results, data scientists and engineers should experiment with feature selection, machine learning models, and corresponding hyperparameters combinations and track the results of each blend. Tracking experimentation results is vital, as it helps engineers roll back to older experiment combinations and helps with model auditing and versioning. Data scientists and engineers should also track the data used to train the model in case of an audit or understanding model bias.
Perform Regular Code Checks
Code checks at each step of the ML process help identify and eliminate bugs before production and remove unnecessary code. A code check reviews if the code performs as expected, contains no bugs, and is easy to maintain. A good practice ensuring good code quality involves triggering a code check as the first step in a pipeline after making a pull request.
Monitor Model Performance
Organizations must guarantee the ML lifecycle operates optimally and monitor their models' performance in production. Monitoring performance metrics like latency, scalability, accuracy, and service downtime is vital to measure how the model performs against business goals.
Don't Forget the Cost
The ML training and deployment cycle uses resources like CPU, GPU, I/O, and memory, all of which cost money. So, to maximize resources and optimize operations, engineering teams should understand the model's needs. Employing an MLOps platform with Mission and clearly understanding MLOps needs can help organizations plan a cost-effective MLOps strategy.
The number of organizations adopting AI and ML continues to grow. According to McKinsey’s report, AI adoption grew from 50 percent in 2020 to 56 percent in 2021. Adopting ML in business helps improve customer experiences by applying prediction, which can boost revenue. However, implementing ML can be challenging because it involves many moving parts.
MLOps helps establish a clear set of rules and practices that helps foster collaboration and communication between ML operations and engineering teams while optimizing the ML lifecycle.
Adopting an efficient MLOps strategy can be challenging and time-consuming. However, organizations can leverage cloud solutions like Mission to design and build well-architected MLOps models for their needs. Check out Mission to learn more about MLOps.