Machine Learning Life Cycle

What is the Machine Learning Life Cycle?

The machine learning life cycle refers to the sequence of steps or stages involved in creating, deploying, and maintaining machine-learning models. The common phases of machine learning are a structured approach that ensures the efficiency and effectiveness of a machine-learning project.

This AI lifecycle not only encompasses model development but also data preparation, evaluation, deployment, and monitoring. While training is at the heart of creating a machine-learning model, the entire life cycle extends well beyond it.

Example of the ML Model Lifecycle

Before diving deeper, let’s run through an example of the entire process to give a high-level overview. We’ll use a company looking to employ a machine-learning model to predict sales based on historical data; we’ll call them Company A. The stages might look like the following:

  • Problem definition: Company A begins by defining the project. They want to anticipate future sales to manage inventory more effectively.
  • Data collection: Next, it’s time to gather historical sales data, which includes factors that may affect sales, such as holidays, promotions, or local events.
  • Data cleaning and preprocessing: Irrelevant or corrupt data is removed, and missing values are addressed. Outliers may need to be removed or labeled as such.
  • Feature engineering: New features, such as average sales during holidays, are created to enhance the model’s prediction capability.
  • Model selection and training: A few different algorithms are tested, and Company A chooses a regression model, which is then trained using a subset of the data.
  • Model evaluation: The model’s predictions are compared with actual sales from a different subset of data to evaluate its accuracy.
  • Deployment: The model is deployed into the production environment once it is performed as expected.
  • Monitoring and maintenance: As the model encounters new data, its performance is continuously monitored throughout its life cycle. If there’s a drop in accuracy, the model might be re-trained, tweaked, or replaced altogether.

The above steps for a machine-learning project give you a high-level view of the entire process. Be aware that each of these steps can be time- and resource-intensive, even if they may seem fast at first glance.

The Typical Machine Learning Life Cycle

A machine learning’s life cycle includes the steps from conception to real-world action while ensuring its effectiveness, relevance, adaptability, and longevity. So, let’s dive deeper into the life cycle of machine learning so you can start preparing for your next project.

Objective Definition and Strategy Planning

Before diving into algorithms and datasets, the cornerstone of any successful machine-learning project lies in a well-defined objective. Understanding the problem you want to solve is not just about pinpointing what you want the ML model to achieve but also why and how it aligns with the organization’s broader objectives.

This process involves discussions with stakeholders, establishing the right KPIs, and understanding potential roadblocks. Additionally, an effective strategy includes considerations for scalability, adaptability, and deployment.

With the increasing ubiquity of machine learning, ethical implications come into focus. It is important to address potential biases in data or algorithms, privacy concerns, transparency, and the societal implications of automated decisions.

Data Gathering and Preprocessing

Data is at the heart of the system, from training to future fine-tuning. The quality and quantity of this data can make or break a project. Gathering data involves sourcing from databases, sensors, user interactions, or external data providers.

But gathering isn’t the end of the story. Raw data often comes with noise, which are errors, missing values, or irrelevant information.

Preprocessing is the next step, which makes sure that the data aligns with the model’s requirements, making it more digestible. Preprocessing involves cleaning data, normalizing scales, and handling outliers, among other possible tasks depending on the use case.

This phase’s overall goal is to ensure the data used to train the machine learning model is in the best possible state to produce ideal results.

Feature Engineering and Model Selection

Feature engineering focuses on maximizing the extraction of this information for the learning algorithm. This phase might involve creating new combinations of attributes, transforming variables to better represent underlying patterns, or using industry-specific knowledge to add more data points.

Next, it’s time to explore the options and choose the right algorithm. Whether the task is to classify emails as spam or predict stock prices, the nature of the problem dictates the algorithm selection.

Your decision is based on the data’s characteristics, the problem type, and the desired outcome. Some aspects to consider are:

  • Tool and infrastructure selection: A brief overview of popular tools and platforms (like TensorFlow, PyTorch, and Scikit-learn) and the criteria for choosing between cloud providers or on-premise solutions is a great place to start.
  • Scalability and efficiency: Address the challenges and best practices of scaling ML solutions, optimizing compute resources, and ensuring models can handle real-world loads.
  • Version control and experiment tracking: Like software development has Git, machine-learning projects also need versioning for data, models, and experiments. Tools like MLflow or DVC can be introduced in this context.

Training and Evaluation

With data ready and an algorithm chosen, it’s to start the training phase. Here, the algorithm “learns” from the data, adjusting its internal parameters to best predict the desired outcome.

However, a model’s performance in training doesn’t necessarily translate to real-world success. As such, it’s crucial to evaluate results throughout the training process.

The model’s true capabilities are assessed using separate test datasets or techniques like cross-validation. Metrics such as accuracy, precision, recall, or mean squared error can be used depending on the problem type. Should the model have lackluster results, it’s back to the drawing board, tweaking features or even reselecting models.

Deployment and Continuous Monitoring

It’s time for real-world deployment after the model has been thoroughly evaluated. Deployment can range from integrating the model into a web application, embedding it in IoT devices, or even making it available as an API.

However, stay aware that the world is dynamic. Data may change over time as user behaviors change. The result is that new patterns might emerge. A once-perfect model might start showing cracks.

As a result, ongoing monitoring is required to ensure the right performance. The model can be recalibrated, retrained, or tweaked to ensure it remains at its peak efficacy by keeping an eye on the model’s performance and gathering new data.

Additionally, it’s crucial to measure the model’s impact. Discuss methods to quantify benefits in financial terms, operational efficiency, or user satisfaction. KPIs chosen in the first phase can help quantify performance.