If you have been keeping up with your reading, you have probably the term ‘MLOps’ and other variants such as ‘ModelOps’ and ‘AIOps’ begin to pop up more frequently.
MLOps stands for ‘Machine Learning Model Operations’ and it is being touted as the best way for ML teams to eliminate many of the ML model development pain points experienced during the machine learning lifecycle and deliver more powerful ML models more easily.
As we experienced with DevOps, the popularity and growing importance of MLOps as a discipline has led to the emergence of several MLOps tools.
Just like DevOps tools enable software engineers to automate and apply best practices to the software development process, MLOps tools enable ML engineers and teams to automate and apply best practices to the ML model development process. There is a plethora of MLOps tools available on the market today, each with their own unique features, functionality, capabilities, and intended use case.
In this guide, we are going to look at the role MLOps tools play in the machine learning lifecycle, why and when to use them, the different types of tools that are available, and provide guidance on what you should be looking for when testing out MLOps tools for yourself.
Before we dive into MLOps tools, it makes sense to backtrack a little and briefly introduce the idea behind MLOps.
MLOps is a core function of machine learning engineering that is focused on streamlining the process of deploying ML models into production and then monitoring and maintaining them once there. It is a collaborative movement that involves everyone from across the machine learning value chain – business leaders, data scientists, IT departments, and DevOps engineers – to achieve this goal.
Although MLOps is a relatively new discipline, it is quickly gaining traction as the best approach for the creation and deployment of high-quality ML models and AI solutions. By adopting an MLOps approach, ML teams can collaborate more easily and reduce model development and production times through the implementation of continuous integration and continuous deployment (CI/CD) practices that are supplemented with monitoring, validation, and governance.
A solid approach to MLOps will help with:
MLOps has been described by some as the union of DevOps, machine learning, and data engineering all in one. Built on the existing approach of DevOps, MLOps tools and solutions are designed to increase re-usability, enable automation, and manage processes like experiment tracking, data drift, model versioning, CI/CD, and continuous training. They also enable ML teams to extract better insights from their machine learning projects.
Producing robust ML models is extremely difficult. The machine learning lifecycle consists of many complex components. Data ingestion, data preparation, model training and tuning, model deployment, model monitoring; there is a huge amount of work that goes into producing even the simplest machine learning solution. Model production also requires a strong level of collaboration across teams, including data science, data engineering, ML engineering, and IT.
Owing to the inherent complexities involved, ML model development requires stringent operational management to keep all these processes in sync and working together. MLOps is the process that encompasses all of this (and then some) to drive continuous improvement of the machine learning lifecycle and help ML teams deploy the very best models that they can.
In short, yes—MLOps is necessary for all machine learning teams.
Building a machine learning model that can predict what you want it to predict from the data that you have trained it with is relatively easy. However, building a model that is fast, reliable, and makes accurate predictions with a low failure rate both at scale and while serving a large user group is not so easy.
This is where MLOps comes in and is why it has grown so rapidly alongside the proliferation of ML-powered solutions.
We can summarize the necessity of MLOps as a process as follows:
In addition to the above, the process of developing, deploying, and continuously improving machine learning models often leads to huge ongoing maintenance costs because:
It is for these reasons that MLOps has become a necessity for any organization that wishes to deploy robust and reliable machine learning solutions.
MLOps tools help machine learning teams to ensure they keep on top of their projects, ensure transparency, implement automation, and maintain collaboration. Together, these factors help support the production of robust ML models that can perform their intended function. In essence, MLOps tools achieve this by facilitating technical knowledge sharing and the exchange of information between all stakeholders and those involved in ML value chains.
MLOps tools also help ML teams to resolve some of the challenges that arise when implementing MLOps best practices. As we have already touched upon, however, there is no one-size-fits-all solution available; all MLOps tools, platforms, and solutions have different features, strengths, weaknesses, and use cases.
MLOps tools can be split up into three major categories that solve three key challenges:
In addition to individual MLOps tools, there are ‘MLOps platforms’ that provide a full suite of features and services for end-to-end machine learning lifecycle management. We will first explore the three different categories of MLOps tools here before covering end-to-end MLOps platforms later in the article.
Data labeling: Data labeling– also known as data annotation, tagging, or classification – is the process that is used to label large volumes of data. This data can then be used to train machine learning algorithms via the supervised learning process, enabling them to make more accurate predictions.
Data version control: Also known as data versioning, data version control simplifies the management of different versions of datasets and allows teams to store them in a way that is both organized and accessible. Doing so gives ML teams more insight into how changes to data impact model performance, understand how datasets evolve, and avoid problems like training-serving skew.
Feature engineering: Feature engineering is used to extract useful features from datasets to create better training data for your ML models. However, feature engineering tools take this a step further by automating and accelerating the process. While some feature engineering tools are very powerful, it is usually necessary to make improvements to the machine-generated feature engineering results by applying human domain knowledge.
Testing & experimentation tracking: ML model development requires machine learning teams to run several experiments with different iterations of their model(s), model parameters, and/or training data. This is a complex ongoing process that often involves multiple versions of the same model, and experiment tracking MLOps tools save all the necessary information about the various tests and experiments that have been completed.
Model Deployment: ML model deployment tools enable ML teams to integrate their models into a production environment. Deployment is a critical part of the development process that enables models to make predictions even during the development phase.
Model Monitoring: ML model monitoring is a core component of every successful machine learning project. This is because ML models begin to decay after they are deployed due to changes in input data over time. Model monitoring tools are therefore used to detect anomalies and data drifts, enabling ML teams to be notified of performance issues so that they can then be rectified before they evolve into more serious issues.
As we mentioned earlier, there are also many cloud-based MLOps platforms that cover the entire ML model development cycle from end-to-end.
These platforms provide data scientists and software engineers with a collaborative environment that encapsulates the above features and then some, all in one place. In doing so, MLOps platforms facilitate data exploration, feature engineering, model management, model transitioning (deployment, monitoring), and more.
Creating an MLOps environment is a delicate task. Just like putting together a puzzle or building a structure out of LEGO, each of the tools you use has its own purpose. Its own capabilities. That is why it is important to figure out your desired business outcomes and the state of your existing tech stack before you implement any MLOps tools or attempt to build your MLOps environment. By understanding your goals, your team will be more empowered to select the very best MLOps tools for the task at hand and ensure that your MLOps environment is built to be as robust and operate as seamlessly as possible while aligning with these goals.
Once this has been established, it is crucial for ML teams to conduct constant evaluations of their MLOps environment and the overarching goals of the business. Doing so enables teams to identify areas where opportunities for continual improvement exist, as well as ensure that business needs are always being met.
When putting together a puzzle, you need to ensure that the pieces you are using fit together. The same is true when it comes to MLOps; when building a data processing model, all your parts must fit together and work cohesively to ensure the very best results. Let’s say an organization exclusively operates in a cloud environment like Amazon AWS or Microsoft Azure, they’ll only be able to use MLOps tools that can run in this environment while also working with other cross-platform tools like Kubernetes.
It is likely that MLOps will not make up the entirety of an organization’s data science environment, so it is also important to understand which parts of it will require the implementation of MLOps tools, and which parts can be run via more traditional methods. Those which are priorities for automation can help inform this decision.
It is also important to consider the complexity of MLOps tools and how this stacks up to your team’s level of expertise. Some MLOps tools, especially those which are open source, are much trickier to use than paid tools and platforms. If your organization doesn’t have a lot of ML expertise available, paid platforms and tools are likely to be the best option. However, if complexity is not a limiting factor, it might be worth considering open-source tools instead.
Although paid tools offer the benefit of a pre-packaged and more streamlined solution, open-source tools are far more flexible but require more expertise and knowledge to use. ML teams need to consider their level of relevant experience in deciding which route to go down when considering MLOps tools in turn.
Adopting MLOps, particularly when you use a comprehensive MLOps platform like Qwak, allows ML teams and the wider business to unlock several benefits and advantages for all stakeholders. These include:
With so many MLOps tools and platforms available on the market, it can be difficult to sort through them all. It can even cause frustration, with many engineers asking themselves, “What MLOps tool features should I be looking for?”
To make the process easier for you, we have put together a list of core capabilities and features that an MLOps tool or platform must include for it to be a worthy candidate. Perhaps unsurprisingly, they echo many of the benefits that we have just covered. These are:
Let’s go through each of these in turn.
Data sits at the heart of any machine learning project. And, as you know, ML teams must always spend time carefully preparing datasets before they can be used in the development of a machine learning model.
Data extraction, data validation, and data preparation are all critical data management processes that support the development of robust machine learning models. Without thorough data processing and data management, it is virtually impossible for machine learning algorithms to learn the relationship between input data and target variables to enable the desired outcome.
Therefore, one of the first things you should be looking for in an MLOps tool or platform is features and functionality that enable comprehensive data processing and data management.
The output of a machine learning project is the result of several rounds of iterating and testing, as well as its interactions with components such as data, code, and other models. When machine learning teams uncover flaws in their models, however, they must backtrack and correct each version of their code. In cases where there are several flaws or where a flaw has gone unnoticed for a prolonged period of time, correcting it can present a huge technical challenge. This is where version tracking comes in.
When version tracking is implemented, ML teams can more easily rectify errors, which in turn simplifies the retraining and output reproduction processes. In many ways, version tracking and storage are key to the development of robust models. In some industries, particularly those that are heavily regulated such as legal and healthcare, you must be able to maintain a lineage of models that have been deployed into production—at least, this is the case in most developed markets. MLOps tools with version tracking and storage make this easy and can help with regulatory compliance as well as model development.
Building models, training them, and then deploying them is, as we mentioned in the above point, an iterative process. It is also one that involves many stakeholders, each with their own roles and who use their own systems, tools, and environments.
For many organizations that are just starting out in model development, the model training and deployment process is handled manually because they have not yet set up their automated machine learning pipelines. While manual deployment does have its (albeit very limited) use cases, relying on manual processes in the long-term or at scale can lead to friction which in turn puts the reliability of the model and the overall stability of the machine learning ecosystem at stake.
For these reasons, most MLOps tools and platforms will offer a method for streamlining model training and production deployments and scaling models in response to evolving needs.
ML model validation is typically carried out alongside model development and is measured by using quantitative measures that analyze predictions against observations. Some examples of these measures include F1 scores, Area Under the Curve (AUC), and Receiver Characteristic Operator (ROC) curves. If a model fails to reach the required metrics with new data, it must be sent back to the development phase so that it can be further developed and tuned.
The model validation phase is important because it helps to reduce biases and improve model reliability before it is deployed to a production environment. By continuously validating a model and testing it against new data, you can ensure that it performs as intended and fits your desired use case(s).
Just because your team has successfully designed its ML model, that doesn’t mean the process is over. Even after deployment, you must constantly modify and update your model and integrate any changes into the live environment as quickly and seamlessly as possible. This is where continuous integration and continuous delivery (CI/CD) come in, which helps ML teams to automate the integration and delivery of modifications and updates.
Continuous integration helps to ensure that any changes made to your ML model are always being tested for quality. Once changes have been tested, approved, and consolidated, continuous delivery ensures that the most up-to-date version of the model is automatically deployed into production. This process streamlines the delivery of new code and creates more visibility between internal stakeholders
In machine learning environments, it is important that day-to-day operations are closely monitored, and that metrics are tracked. Doing so helps to ensure the predictive accuracy and overall performance of models in production.
For instance, ongoing monitoring helps to avoid the challenge of model drift. As training data that is fed into the model changes and evolves over time, model output is susceptible to change and may no longer match the situation or serve its intended use case. This is what is meant by drift, and it can lead to outdated, inaccurate, and misleading predictions. To prevent drift, MLOps tools and platforms should include features that automatically monitor for drift by comparing live traffic against previous results and alert ML teams if it is detected.
The amount of automation of an ML team’s data, model, and code pipelines is proportionate to the maturity of their machine learning process. The object of MLOps is to automate the deployment of machine learning models into the core software system or as its own service component. In other words, to automate the full machine learning workflow steps with minimal (or, even better, no) manual intervention required.
In adopting MLOps, it is generally accepted that there are three levels of automation. This begins with manual model training and deployment all the way up to automated CI/CD pipelines.
1. Manual processes: This is the typical data science process for smaller and less mature teams, or the process that is applied during the first stages of applying machine learning. Manual processes have an experimental and iterative nature, and every step in the pipeline—data prep, validation, model training, testing—is carried out manually. ML teams tend to rely on Rapid Application Development (RAD) tools to do this.
2. Pipeline automation: Pipeline automation is the next level up from an entirely manual process and includes the automation of model training. In pipeline automation, continuous training is introduced which retrains models automatically whenever new data becomes available, helping to prevent problems such as data drift from occurring. Pipeline automation might also include data and model validation.
3. CI/CD automation: The final stage of automation—which is generally achieved through the use of MLOps platforms—introduces a CI/CD system that automates ML model deployments in production and does so quickly and accurately. The difference between pipeline automation and CI/CD automation is that the latter builds, tests, and deploys data, ML model, and ML training pipeline components.
MLOps is far more than just another tool; it is a vital process that sits at the core of the workflow of pretty much every ML market leader. As we have explored, MLOps is all about the implementation of a comprehensive system that enables machine learning teams to optimize and drive continuous improvement in their ML environments, from development to deployment and beyond.
To choose the right MLOps platform and tools, it is important that ML teams understand not just the organization’s mission and long-term goals but also its current data science environment and the value that MLOps could deliver.
Qwak is the full-service machine learning platform that enables teams to take their models and transform them into well-engineered products. Our cloud-based platform removes the friction from ML development and deployment while enabling fast iterations, limitless scaling, and customizable infrastructure.
Want to find out more about how Qwak could help you deploy your ML models effectively and efficiently? Get in touch for your free demo!