Although machine learning (ML) is no longer brand new, the technology—a subcategory of artificial intelligence (AI)—continues to develop at breakneck speed. One of the most important developments assisting the rise of machine learning is the concept of MLOps.
What is MLOps?
MLOps, short for "machine learning operations," is an integral part of ML engineering. It specifically focuses on promoting the efficient development, training, testing, production, deployment, and maintenance of machine learning models.
Similarities to DevOps
The term naturally draws comparisons to the more familiar DevOps. Given that MLOps serves a purpose similar to DevOps—a prominent framework for modern application development—for the expert personnel who create machine learning models, this makes perfect sense.
In DevOps, the point of following its various principles is to bring agility, flexibility, and greater creativity to software and application development—and also unify developers and engineers with a single set of priorities. MLOps aims to do much the same for the process of creating and deploying machine learning models. In fact, it counts the DevOps fundamentals of continuous delivery (CD) and continuous integration (CI)—which enable "always-on" performance and testing through sophisticated automation—among its key tenets.
That being said, MLOps often involves more pre-deployment testing than its app-centric counterpart. MLOps followers may also spend more time on post-production monitoring and data governance than app developers would. Lastly, machine learning is in a more nascent stage than software or app development. As such, data scientists have influence over the direction and implementation of MLOps, though they work alongside numerous others: machine learning engineers, data engineering and analysis experts, and developers who create apps driven by ML algorithms and models.
What are the essential components of MLOps?
While different organizations or professionals may not use identical terminology for every process, the following gives a general idea of the steps within MLOps and how they unfold.
Data gathering and preparation
Because no machine learning system can function as it's intended to without a foundation of data, that is exactly what the initial MLOps steps focus on.
Relevant historical and new data are collected and ingested, and then data exploration—also known as exploratory data analysis (EDA)—begins. By the standards of some data science practices, exploration is relatively uncomplicated: After relevant data for the model has been extracted from appropriate sources, data scientists or analysts compile it into data sets, usually presenting it as tables or other rudimentary visualizations. Data scientist and analyst teams will also be looking for basic but important aspects of the data sets, ranging from size and accuracy to correlations between sets and any obvious and noteworthy patterns—or indications of patterns.
Data preparation comes next. The data that will factor into the ML model being produced must first go through cleansing, and then it can be split into data sets that support the model training and testing processes. This data will also go through deduplication to eliminate redundancies and noise, and undergo transformation to create features for the model and develop feature stores for use in future projects.
Training and validation
After all introductory data-related processes are complete, the model's training can begin. A training algorithm is implemented as the catalyst for the model's intended function: For example, in a supervised machine learning project where labeled image data has been input, the algorithm might instruct it to identify and categorize those images. Training algorithms also include hyperparameters: metrics that are meant to govern how the model learns. The number of branches in a decision tree is a particularly important hyperparameter, as is learning rate.
Once training concludes, you must gauge model performance and accuracy. Evaluation involves the identification of a testing data set that can establish a baseline for the model's performance, and model validation determines whether that baseline is met, exceeded, or missed. Parameters—values generated during the training process—are very important to this phase, as they illuminate specific areas in which the model did or didn't perform in accordance with its hyperparameters. The parameters will also eventually be the rules by which the model operates.
If the model's validated accuracy is below expectations, or it produces anomalies, duplicates, and other noise, the hyperparameters, parameters, and other aspects of the training algorithm need to be adjusted.
Because it's inefficient for all of these processes to be overseen and executed manually, they are typically automated via a data pipeline, often called an ML pipeline in this context.
Deployment, monitoring, and re-training
If the model passed validation either initially or after some rounds of algorithm and/or hyperparameter tuning, it will then undergo brief eleventh-hour quality assurance checks. Provided these uncover no significant issues, the model can then be deployed in its target environment. This could be via a website or an app, through microservices with a REST API, or as an embedded function within a mobile device or one at the edge, to name a few examples.
The ML model must be monitored in its working environment just as you would keep track of any other product's performance. Training and validation stages prove the model is capable of serving its intended purpose in a vacuum, whereas real-world deployment means collecting new data. Thus, you must establish a basis for monitoring.
- For example, say a model developed for your bank predicts fluctuations in interest for variable-rate mortgages over particular loan terms.
- Its projections are based on tracking current variable-rate interest figures and the metrics affecting them. It also looks at newly submitted mortgage applications and data from a simple "interest calculator" app on the bank's website, where users get an estimate based on the financial information they provide.
- Examining the model's accuracy across different time periods would serve as a straightforward monitoring method. Alternatively, you could check to see if incomplete applications or calculator queries are skewing the model.
- You must also monitor performance KPIs like traffic, latency, and saturation.
If monitoring discovers significant patterns of subpar performance, the model will be retrained using new—and, ideally, higher-quality—data.
As with training and validation, the practice of MLOps ensures these processes are automated as much as possible to reduce the burden on engineers, analysts, developers, and data scientists.
How can MLOps benefit the modern enterprise?
Standardization is arguably the most important benefit of MLOps. This framework helps enterprise IT, dev and data professionals who work on ML initiatives follow a conveniently uniform approach to the life cycle of each ML project. It makes the development of machine learning algorithms and models more efficient and collaborative, and does so without ever sacrificing the quality and integrity of the models being produced—or slowing down the pace of model production. Because machine learning is being adopted across numerous industries, a standardized framework makes sense.
MLOps also helps bring some much-needed clarity to a process that might otherwise become excessively complex or even unwieldy: The creation of a machine learning model begins with various data ingestion, processing, and analysis steps and culminates in model monitoring that is overseen by ML engineers. The data, dev, and IT/engineering teams handle some stops along the path to model production alone and some together, but MLOps ensures all staff involved in a given ML initiative stay on the same page the whole time.
Additional notable benefits of MLOps
The automation and clarity of workflow that MLOps brings to the modeling process helps increase efficiency and efficacy for all phases of the model development life cycle.
MLOps cannot function without collaboration, so it's designed to encourage it. There can be something of an adjustment period at first, which is hardly uncommon when implementing a new development framework of any kind. But each ML team using these practices will develop a greater understanding of and appreciation for what their collaborators do.
The principles of MLOps can be applied to large, small, and medium-sized ML initiatives, because the key processes don't change with product size.
More effective data use
MLOps allows for the development and deployment of algorithms that can find actionable insights within immense and complex data sets—especially unstructured data—that analysts, scientists, and engineers can't efficiently analyze.
This means stronger analytics to support improved processes, which can help improve bottom-line KPIs in the long run ranging from productivity to customer satisfaction.
Strong quality control
Machine learning projects are inherently complex and can spin off the rails without a firm foundation. MLOps provides this foundation through its standardized framework and as a result of the thorough testing, validation, governance, and retraining steps that are essential aspects of the process.
Potential MLOps challenges
Few technical processes are foolproof, and those in nascent, still-growing fields are even less likely to be fault-free. MLOps is not without its potential pitfalls.
There can be a stark contrast between developing code and models in the all but hermetically sealed production environment and deploying it within a model in a real-world setting. The world in which the model is deployed may change in ways the model doesn't account for.
The resulting phenomenon, often called model drift or concept drift, can lead to inaccurate or otherwise faulty performance.
Discrepancies that arise before deployment—such as hyperparameter optimization that somehow isn't reflected in the finalized model—will also cause operational problems.
Generally speaking, machine learning models aren't significantly more or less vulnerable to cyberattacks than other applications or artificial intelligence (AI) models. But they often handle personally identifiable information (PII), so the consequences of a breach would be disastrous.
Data scientists and analysts, ML engineers, and software developers are all critical to MLOps. Because they come from different backgrounds, if team members aren't willing to meet one another halfway, interpersonal conflicts can arise and impact model development and production.
Best practices to realize MLOps' full potential
Implementing specific MLOps best practices will be essential for mitigating the issues detailed above and generally keeping the process on track.
Establish a strong ML pipeline
The automation capabilities of an ML-specific data pipeline can accelerate various critical processes—including CD and CI. An experienced data engineer is invaluable to the creation of a reliable MLOps pipeline.
Set up frequent retraining
Ensure there are as few times as possible where the ML model is inaccurate by regularly retraining it. Be proactive and set up a schedule for this; don't just wait for the model to encounter a snag in deployment.
Encourage knowledge sharing
For example, software engineers should help data scientists improve their raw coding skills, and establish repositories like feature stores and model registries to keep track of data. Meanwhile, a data scientist can persuade their dev and engineering colleagues to consider theoretical and exploratory aspects of data science and—when feasible—use this to fuel outside-the-box thinking.
Utilize critical support technologies
A multi-cloud deployment serves as a strong foundation for MLOps. This is not only due to its inherent elasticity and the availability of low-cost object storage, but also its value as a development environment—similar to its value for DevOps. Additionally, this type of cloud infrastructure is ideal for setting up data lakes, which are essential to ML projects because of their heavy use of unstructured data.
Teradata VantageCloud, the data and analytics platform designed for cloud-native deployment, can effectively manage these vast data stores, processing, cleansing, and integrating data from disparate sources to optimize it for ML model development. The solution's advanced analytical capabilities—powered by its built-in AI/ML-driven ClearScape Analytics™ engine—are well equipped to oversee the training, validation, deployment, performance, and retraining phases of MLOps.