Understanding the machine learning life cycle
Deep learning is a subset of the wider machine learning category. The main characteristic that sets it apart from other machine learning algorithms is the foundational building block called neural networks. As deep learning has advanced tremendously since the early 2000s, it has made many previously unachievable feats possible through its machine learning counterparts. Specifically, deep learning has made breakthroughs in recognizing complex patterns that exist in complex and unstructured data such as text, images, videos, and audio. Some of the successful applications of deep learning today are face recognition with images, speech recognition from audio data, and language translation with textual data.
Machine learning, on the other hand, is a subset of the wider artificial intelligence category. Its algorithms, such as tree-based models and linear models, which are not considered to be deep learning models, still serve a wide range of use cases involving tabular data, which is the bulk of the data that’s stored by small and big organizations alike. This tabular data may exist in multiple structured databases and can span from 1 to 10 years’ worth of historical data that has the potential to be used for building predictive machine learning models. Some of the notable predictive applications for machine learning algorithms are fraud detection in the finance industry, product recommendations in e-commerce, and predictive maintenance in the manufacturing industry. Figure 1.1 shows the relationships between deep learning, machine learning, and artificial intelligence for a clearer visual distinction between them:
Figure 1.1 – Artificial intelligence relationships
Now that we know what deep learning and machine learning are in a nutshell, we are ready for a glimpse of the machine learning life cycle, as shown in Figure 1.2:
Figure 1.2 – Deep learning/machine learning life cycle
As advanced and complex the deep learning algorithm is compared to other machine learning algorithms, the guiding methodologies that are needed to ensure success in both domains are unequivocally the same. The machine learning life cycle involves six stages that interact with each other in different ways:
- Planning
- Data Preparation
- Model Development
- Deliver Model Insights
- Model Deployment
- Model Governance
Figure 1.2 shows these six stages and the possible stage transitions depicted with arrows. Typically, a machine learning project will iterate between stages, depending on the business requirements. In a deep learning project, most of the innovative predictive use cases require manual data collection and data annotation, which is a process that lies in the realm of the Data Preparation stage. As this process is generally time-consuming, especially when the data itself is not readily available, a go-to solution would be to start with an acceptable initial number of data and transition into the Model Development stage and, subsequently, to the Deliver Model Insight stage to make sure results from the ideas are sane.
After the initial validation process, depending again on business requirements, practitioners would then decide to transition back into the Data Preparation stage and continue to iterate through these stages cyclically in different data size milestones until results are satisfactory toward both the model development and business metrics. Once it gets approval from the necessary stakeholders, the project then goes into the Model Deployment stage, where the built machine learning model will be served to allow its predictions to be consumed. The final stage is Model Governance, where practitioners carry out tasks that manage the risk, performance, and reliability of the deployed machine learning model. Model deployment and model governance both deserve more in-depth discussion and will be introduced in separate chapters closer to the end of this book. Whenever any of the key metrics fail to maintain themselves to a certain determined confidence level, the project will fall back into the Data Preparation stage of the cycle and repeat the same flow all over again.
The ideal machine learning project flows through the stages cyclically for as long as the business application needs it. However, machine learning projects are typically susceptible to a high probability of failure. According to a survey conducted by Dimensional Research and Alegion, covering around 300 machine learning practitioners from 20 different business industries, 78% of machine learning projects get held back or delayed at some point before deployment. Additionally, Gartner predicted that 85% of machine learning projects will fail (https://venturebeat.com/2021/06/28/why-most-ai-implementations-fail-and-what-enterprises-can-do-to-beat-the-odds/). By expecting the unexpected, and anticipating failures before they happen, practitioners can likely circumvent potential failure factors early down the line in the planning stage. This also brings us to the trash icon bundled together in Figure 1.2. Proper projects with a good plan typically get discarded only at the Deliver Model Insights stage, when it’s clear that the proposed model and project can’t deliver satisfactory results.
Now that we’ve covered an overview of the machine learning life cycle, let’s dive into each of the stages individually, broken down into sections, to help you discover the key tips and techniques that are needed the complete each stage successfully. These stages will be discussed in an abstract format and are not a concrete depiction of what you should ultimately be doing for your project since all projects are unique and strategies should always be evaluated on a case-by-case basis.
