Machine learning is a type of artificial intelligence (AI) that provides computers the ability to learn without being explicitly programmed by people to do so. On a basic level, machine learning uses algorithms to give computers the ability to learn data, find patterns, and make predictive outputs. The problem, however, is that a traditional machine learning model assumes that the data will be similar to the data that it was trained on, which isn’t always the case.
Continuous machine learning (CML) flips this issue on its head by monitoring and retraining models with updated data. The goal of CML is to mimic a human's ability to acquire and fine-tune information continuously. In this article, we will go into detail about what exactly CML is, the challenges involved with it, and why it's essential for the advancement of AI.
Definitions – CI, CT, CD, and CML
Before we get into the details of continuous machine learning, it's important to first define the DevOps technologies that it relies on – CI, CT, and CD.
Continuous Integration (CI)
The process of automating the integration of code changes from numerous contributors into a single software project is known as continuous integration (CI). It's a fundamental DevOps best practice that allows developers to regularly combine code changes into a single hub where builds and tests are subsequently performed. Before integrating new code, automated tools are tested to ensure its accuracy.
The CI method relies heavily on a source code version control system. In addition, other checks, such as automated code quality tests, syntax style review tools, and others, are added to the version control system.
Continuous Training (CT)
As stated earlier, machine learning (ML) models assume that data will always be similar to the data that it was trained with. However, it is not always so. Namely, the majority of models operate in environments where data is changing rapidly and where "concept drifts" are likely to occur, which may have a negative effect on the accuracy and dependability of the models' predictions.
To prevent “concept drifts” from happening, models need to be monitored and retrained when the data becomes too inaccurate.
This is where the concept of continuous training comes in. CT is a component of the MLOps practice model. It aims to retrain the model automatically and constantly in order to respond to changes in the data. This methodology prevents a model from becoming unreliable and inaccurate.
Continuous Delivery (CD)
The goal of continuous delivery is to provide a dependable and repeatable method for releasing software into production. Continuous Delivery for Machine Learning (CD4ML) extends this approach by enabling a cross-functional team to develop machine learning applications based on code, data, and models that advance in small, safe increments that can be replicated and reliably released at any time.
Continuous Machine Learning (CML)
Now that we have a firm grasp of the concepts above, we can look a little closer at continuous learning machine learning.
CML, short for continuous machine learning, is an open-sourced Continuous Integration (CI) and Continuous Delivery (CD) library for machine learning. Generally speaking, it can be used to automate portions of your machine learning process, such as model training and evaluation, comparing ML trials throughout your project history, and monitoring changes in datasets. It operates on the basis of the MLOps ecosystem, which allows you to use your favorite DevOps tools on machine learning projects.
If you are familiar with Netflix’s recommender system, which has an “Up Next” feature that plays shows similar to the ones you’ve recently watched, then you have seen a CML model in action. In order to keep up with the never-ending supply of new programs, as well as changing preferences and trends of Netflix customers, the incoming data must be constantly entered. And the model must continuously update to have the ability to recommend relevant shows or movies.
Why Do We Need CML?
The Importance of CML – The Rise of CML AI
CML is an important step forward in the development of artificial intelligence. Technically speaking, it’s a significant improvement over narrow AI, which is a goal-oriented artificial intelligence designed to perform a singular task. A model needs to have the ability to learn from a constant stream of data, much like humans do, and CML makes that possible. Furthermore, with continuous machine learning, ML models become more adaptable and scalable, which are two major qualities of intelligent machines.
CML and Adaptability
CML helps with continuous improvements. CML's most basic application is in circumstances where the data distributions remain constant but the data is continuous. For example, Recommendation or Anomaly Detection systems constantly have incoming data, and it's important for models to learn from this data and refine their prediction models for the most accurate results.
Most of the time, gathering a large and representative dataset is incredibly difficult, and it is even more challenging when the semantics of the data are continuously changing. Although typical ML models can be retrained, this can get complicated when the data changes, as well as factors over which we have no control. However, this is where CML models are successful; when new incoming data is much different from the data the ML model was trained with.
CML and Scalability
Like the human mind, CML models should be able to filter and process data quickly to reduce the amount of data that needs to be stored and maintained. According to IDC, the rate of data production will grow to 160 ZB by the year 2025. For perspective, data was produced at a rate of 16 ZB per year in 2017. Once data production increases to that extreme, only 3% to 12% of that data will be able to be stored.
So, being able to process data quickly is going to be essential to prevent lost data. CML aims to filter data and drastically reduce the computational work needed to process it. Therefore, CML may be one of the best options for storing and processing data in the future.
Also, in regards to scalability, CML enables multimodal-multitask learning. As humans, we process multiple streams of new data about multiple topics, all the while solving multiple problems. Traditionally ML methods would require a model that can successfully learn about various topics with many different streams of data to be retrained every time there is new information from one of the streams of data, which is not realistic for scalability. However, continuous machine learning has the potential to enable alternative asynchronous training with many tasks, with the model only being updated on real-time data available from multiple sources at once.
Continuous Machine Learning Main Challenges
So, when is continuous machine learning a problem? Although it has many benefits and is important for the advancement of AI, CML also has some challenges associated with it.
Not Knowing What the Problem is
Being aware of the problem at hand is a challenge that can be applied to any software engineering effort, and a machine learning project is not excluded from that. If you are working in applied machine learning, defining the problem is extremely important since it will influence the decisions you make about the technologies, data sources, and people that will be involved in the development of your product.
This issue explains why only 20% of large corporations' AI pilots reach the production stage, and many of them fail to serve the needs of their consumers as it was intended to during development. There are two possibilities for this statistic: one being that the wrong problem is being addressed, and the other being because not all variables are accounted for during the development.
Gathering the Necessary Data
When it comes to continuous machine learning, one of the most difficult aspects is obtaining and organizing the data necessary to train models. The opposite of this is true for scientific research, where training data is often accessible and the aim is to develop the most appropriate machine learning model.
According to Rochwerger and Pang's **Real World AI, "when developing artificial intelligence in the real world, the data utilized to train the model is much more essential than the model itself.” In academia, data science PhDs spend the majority of their time and effort developing new models. However, in the case of continuous machine learning, it’s the opposite. Data used to train models in academics is solely intended to demonstrate that the model is functional, not to address real-world issues. The collection of high-quality and precise data that can be utilized to train a functioning AI model is very difficult to come by in the real world.
Public datasets are not helpful for training models in many applied machine learning applications, such as continuous machine learning, since they are not well-curated. Either you collect your own data, or you purchase it from a third party. Neither option is ideal. Both alternatives have their own set of difficulties.
Maintaining Machine Learning Models
A machine learning model is a prediction machine that searches for patterns in data collected from various sources around the world and predicts future outcomes based on current observations. As the environment changes around us, so do the data patterns. ML models trained on historical data gradually become obsolete. As stated earlier, machine learning models work in dynamic data environments where data is constantly changing. “Concept drifts” are likely to happen, which would negatively impact the models’ accuracy if it’s not corrected, which is why it’s called “continual” learning.
Unfortunately, AI requires human intervention, constant maintenance, management, and course correction in order to provide meaningful output. For example, during the COVID-19 pandemic, many machine models malfunctioned during the shutdown due to the major shift from the norm.
According to Harvard Business Review, the impact on consumer behavior produced an unforeseen problem: an information gap, as data collected prior to the crisis could no longer be used to predict future patterns accurately. The crucial component for retail customer loyalty programs, AI-driven product suggestions, and a wide range of critical business choices had a serious quality problem. Because of this, the ML models were required to be retrained.
To summarize, ensuring that you have the infrastructure and processes in place to acquire and update your models on a continuous basis is the key to ensuring that your machine learning model will be effective in the long run.
Why Should ML Models be Retrained?
As we near the end of this piece, here is a recap of some of the top reasons why ML models should be retrained:
- Periodic retraining keeps an ML model up to date on the latest data.
ML models should be retrained on a regular basis. However, if there is no concept drift or significant reason for retraining, such as the above-mentioned pandemic, this could be very costly in the long run.
- Sometimes ML models dip below an acceptable threshold.
A major issue with this is that it takes a while to determine the ground truth, otherwise known as accurate data.
- Data can become too dissimilar from the data the ML model was originally trained on.
To prevent this from happening, it’s essential to keep the team or individual who is aware of the initial data input in the loop.
To sum it all up, machine learning is the foundation of machine intelligence. However, traditional machine learning models assume that all data is similar to the data they were trained with, but that is not the case for most real-world AI applications. Continuous machine learning breaks out of the narrow AI concept and provides the ability for machines to gather data and fine-tune it, just as a human would do throughout their lifespan.
Some challenges come with continuous machine learning, such as figuring out the problem to address, gathering necessary data, and maintaining the ML models. However, the ability to interpret new data and change outputs according to trends and new information is a crucial part of the advancement of artificial intelligence.