Machine learning (ML) models are almost always developed in an offline setting, but they must be deployed into a production environment in order to learn from live data and deliver value.
A common complaint among ML teams, however, is that deploying ML models in production is a complicated process. It is such a widespread issue that some experts estimate that as many as 90 percent of ML models never make it into production in the first place.
For the relatively few ML models that do make it to the production stage, ML model deployment can take a long time, and the models require constant attention to ensure quality and efficiency. For this reason, ML model deployment must be properly planned and managed to avoid inefficiencies and time-consuming challenges.
In this blog post, we are going to explore the basics of deploying a containerized ML model, the challenges that you might face, and the steps that can be taken to make the process more efficient.
The goal of building a machine learning application is to solve a problem, and a ML model can only do this when it is actively being used in production. As such, ML model deployment is just as important as ML model development.
Deployment is the process by which a ML model is moved from an offline environment and integrated into an existing production environment, such as a live application. It is a critical step that must be completed in order for a model to serve its intended purpose and solve the challenges it is designed for. Deployments create an online learning machine methodology where a model is trained and continuously updated with new data as it becomes available.
The exact ML model deployment process will differ depending on the system environment, the type of model, and the DevOps processes in place within individual organizations. That said, the general deployment process deployed into a containerized environment can be summarized into four key steps, which we will cover later.
Before we dive into the steps involved in deploying ML models, it’s important to consider why so many ML teams cite deployment as a pain point.
The truth is that many ML teams embark on machine learning projects without a production plan in place. This approach is risky and invariably leads to problems when it comes to deployment. It’s important to remember that developing ML models is expensive, both in terms of time and money, so embarking on a project without a plan is never a good idea.
While it would be impossible for us to tell you how to plan your own ML project — there are simply too many variables at play — we can highlight three important things that you should consider during the planning stage. These are:
We don’t need to tell you that your ML model will be of little use to anyone if it doesn’t have any datasets to learn from. As such, you will likely have a variety of datasets covering training, evaluation, and testing. Having these is not enough, though; you must also consider storage.
Consider questions like:
Storage: It makes sense to store your data where model training will take place and where results will be served. Data can be stored either on-premises, in the cloud, or in a hybrid environment, with cloud storage generally used for cloud ML training and serving.
Size: The size of your data is also important. Larger datasets require more computing power for processing and model optimization. If you are working in a cloud environment, then this means that you will need to factor in cloud scaling from the start, and this can get very expensive if you haven’t thoroughly pre-planned and thought through your needs.
Retrieval: How you will retrieve your data (i.e., batch vs real-time) must be considered before designing your ML model.
Even with the best datasets in the world, your ML model isn’t going to train and deploy itself. For this, you will need the right frameworks, tools, and software, which can be anything from programming languages like Java to frameworks and cloud platforms like TensorFlow, AWS, and Qwak.
Consider questions like:
Machine learning projects are dynamic and constantly evolving. Feedback and iteration form important parts of development and deployment processes, and you must consider these from the outset.
Consider questions like:
Getting continuous feedback from a ML model in production can alert you to issues like performance decay, bias creep, and training-serving skew. This ensures that such issues can be rectified before they have an impact on the model and the end-user.
Deployed ML models provide incremental learning for online learning machines that adapts models to changing environments to make predictions in near realtime. As we alluded to above, the general ML model deployment process can be summarised in four key steps:
To deploy a machine learning application, you first need to build your model.
ML teams tend to create several ML models for a single project, with only a few of these making it through to the deployment phase. These models will usually be built in an offline training environment, either through a supervised or unsupervised process, where they are fed with training data as part of the development process.
When a model has been built, the next step is to check that the code is of a good enough quality to be deployed. If it isn’t, then it is important to clean and optimize it before re-testing. And this should be repeated where necessary.
Doing so not only ensures that the ML model will function in a live environment but also gives others in the organization the opportunity to understand how the model was built. This is important because ML teams do not work in isolation; others will need to look at, scrutinize, and streamline the code as part of the development process. Therefore, accurately explaining the model’s production process and any results is a key part of the process.
Containerization is an important tool for ML deployment, and ML teams should put their models into a container before deployment. This is because containers are predictable, repetitive, immutable, and easy to coordinate; they are the perfect environment for deployment.
Over the years, containers have become highly popular for ML model deployment because they simplify deployment and scaling. ML models that are containerized are also easy to modify and update, which mitigates the risk of downtime and makes model maintenance less challenging.
The key to successful ML model deployment is ongoing monitoring, maintenance, and governance. Merely ensuring that the model is initially working in a live setting is not enough; continuous monitoring helps to ensure that the model will be effective for the long term.
Beyond ML model development, it is important for ML teams to establish processes for effective monitoring and optimization so that models can be kept in the best condition. Once continuous monitoring processes have been planned and implemented, issues like data drift, inefficiencies, and bias can be detected and rectified. Depending on the ML model, it may also be possible to regularly retrain it with new data to avoid the model drifting too far away from the live data.
ML model development is invariably resource-intensive and complex. Taking a model that has been developed in an offline environment and integrating it into a live environment will always bring with it new risks and challenges, including:
What if we told you that deploying your ML models could be as easy as following three simple steps? It’s true! Here are our tips for deploying your own model and avoiding many of the challenges at the same time:
The first step is to figure out which deployment method you want to use. There are two main ones: batch inference and online inference.
Batch inference: This method runs periodically and provides results for the batch of new data generated since the previous run. It generates answers with latency and is therefore useful where model results are not needed immediately or in real-time. The main benefit of batch inference is the ability to deploy more complex models.
Online inference: Also known as real-time inference, this method provides results in real-time. While this sounds like the better method, it has an inherent latency constraint that limits the type of ML models that can be deployed using it. Since results are provided in real-time, it is not possible to deploy complex models with online inference.
When deciding which method to use, consider questions like:
It is possible to manage the deployment and testing of a single, small model manually. For larger or multiple models at scale, however, you should automate.
This will enable you to manage individual components more easily, ensure that ML models will be automatically trained with data that is of consistently high quality, run automatic testing (e.g., of data quality and model performance), and scale models automatically in response to current conditions.
As we have already covered, a successful deployment process lives and dies with continuous monitoring and improvement. This is because ML models degrade over time, and continuous monitoring means you can highlight potential issues such as model drift and training-serving skew before they cause damage.
Qwak enables machine learning 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 started with the Qwak platform, commitment-free today!
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