The MLOps Inmates Run the Asylum with Unsupervised Machine Learning
Simply put, unsupervised learning refers to a machine learning technique where the user does not supervise the model. Rather, it allows the model to discover previously unknown patterns and information on its own, so the models learn patterns and groups in a data set without human intervention. Compared to supervised learning, which involves labeled training sets, unsupervised learning uses unlabeled datasets. However mathematically, it’s the process of observing a number of instances of a vector and learning the probability distribution for each instance of that vector.
Unsupervised learning is a technique being applied in a variety of areas. With clustering, data points are grouped based on whether they contain similar features or characteristics (occasionally including irrelevant data). Cluster analysis does a detailed search for these characteristics which can help identify data points that are similar to each other, and different from other data points. Clustering can be a critical tool for anomaly detection. The association rule on the other hand can use points from a database that occur together to analyze a market and learn how different products that are purchased relate to one another. Association rules can help identify patterns of user behavior (which actions often occur together). To take advantage of the value that unsupervised learning can provide, we need to assess the potential challenges to qualify proper model deployment solutions.
Model Deployment With Your Eyes (Partially) Closed
One application where unsupervised methods can be valuable is with next-generation network architectures like the Internet of Things (IoT). IoT can be considered an abstraction of physical and virtual devices that form a semi-physical framework. Such a network can analyze and transmit collected data from a range of applications, but IoT needs ML to make intelligent decisions with said data. The key challenge that IoT must deal with is the extremely large scale of IoT deployments.
Unsupervised ML models can analyze high volumes of IoT data and have the capability to both label and identify anomalies with a given dataset with the correct model deployment solution. Anomaly detection capabilities can be very useful in diverse sectors, for example, finance. You can take inputs such as the transaction history of bank accounts, including the type of transaction and amounts of transactions, to identify fraudulent transactions. The healthcare sector also benefits as it is cluttered with unlabelled data, so unsupervised models can help detect, segment, and classify images. Cluster analysis can identify information among the unlabeled data to identify corresponding factors of different condition stages.
It is recommended that when using unsupervised ML for feature extraction, given the scale of the IoT operation, be sure to employ a load-balancing algorithm (like a restricted Boltzmann machine).
With the benefits and intriguing results of unsupervised learning, there also exist some potential pitfalls and caveats. Different unsupervised ML techniques may have excellent results in some applications while performing poorly in others. A major drawback of unsupervised learning is that it can often be difficult to get precise information regarding data sorting. For example, hierarchical clustering doesn’t work as well when the shape of the clusters is “hyperspherical”, but K-Means clustering is found to work well with this structure. It is important to choose the best technique for the task as parameter optimization cannot be overlooked with unsupervised models. Also, some models operate as a “black box”, which makes the use of unsupervised ML unsuitable for IoT applications where explainability is critical for the operational success of large-scale networks. Users should be able to monitor their models, particularly for applications where optimization decisions are made autonomously by the model.
Avoid an Unsupervised Model Deployment Selection
In production, IoT devices collect tons of data making it extremely hard to label, so unsupervised ML models have been found very useful in these types of applications. Wallaroo is a solution that can help with this problem by providing a way to run models at lightning speeds with its enhanced compute engine, as well as assays to monitor the model’s environment, which can provide tips on whether a model needs to be retrained or the environment data needs to be analyzed. Wallaroo is also designed to support the last mile of ML with its model insights framework, which allows for the creation and scheduling of monitoring tasks (Assays) via the Wallaroo Model Operations Center, providing real-time insights, especially for applications in IoT where there are multiple models running in production.
Data teams also need to build business capabilities around data science that are core to the business but invest in technologies that automate the rest of MLOps. For example, there’s often no gain in business value for hiring dedicated ML engineers for each line of business, as it creates increased cost and decreased productivity if anything. Instead, an enterprise is typically better when it has a standardized platform for deploying and managing ML models in production that is agnostic to the team that developed it, or the model-building frameworks used. While hiring in data science and MLOps will continue to be difficult, businesses can start delivering immediate value from their ML with even a limited team of data scientists. By understanding the different functions required to build and operationalize ML and then identifying those that can be automated an organization can be greater than the sum of its parts.
Talk to an expert about the easiest way to deploy, run and observe your ML models in production and at scale or try Wallaroo CE yourself and optimize your MLOps with the simplicity, speed, and efficiency of Wallaroo.
