Data Extraction and Analysis

The initial stage involves gathering data from various sources, which is crucial for the machine learning task. The goal here is to select the appropriate data for the intended analysis.

Following this, exploratory data analysis (EDA) is conducted to gain insights into the data. This stage serves two primary purposes:

1. To understand the data structure and distribution of the input/label data for the model.
2. To pinpoint any necessary data preparation and feature engineering steps essential for executing the machine learning task.

Data Preparation and Model Training

Data preparation encompasses cleaning the data—addressing missing values and eliminating irrelevant information—along with splitting the data into training, validation, and test sets. Additionally, this step involves creating new features to enhance the model’s predictive capability. The outcome is a set of clean data formatted correctly for model input.

Next, various algorithms are employed using the prepared data to train machine learning models. During this phase, hyperparameter tuning is often performed to explore the hyperparameter space and identify the optimal model. The result is a model artifact that includes the best-found model's architecture and weights.

Model Evaluation and Validation

Once the best model is identified, it is evaluated using the test data prepared earlier. Before this evaluation, it’s essential to establish one or more metrics to assess the model’s performance.

The selected model must demonstrate adequate performance to qualify for production deployment. This typically involves ensuring it outperforms a baseline, which could be the performance of the existing process it aims to improve.

Model Deployment and Monitoring

1. Online predictions via a REST API, allowing applications to send input data and receive predictions.
2. Embedded in an edge device, with predictions calculated locally.
3. A batch prediction process where the model is run on compute resources for predictions based on input data.

To ensure the model remains effective, its performance is continuously monitored. Should the evaluation metrics fall below a predetermined threshold, it may signal the need to revisit the entire process for another iteration.

The cyclical nature of this process illustrates how automating these steps can significantly boost efficiency, consistency, and scalability. Generally, organizations that can automate a larger portion of these steps will find their machine learning processes maturing rapidly. This automation allows for more efficient experimentation and quicker deployment of validated models, while also minimizing the risk of human error.

Levels of MLOps Automation

There are three distinct levels of MLOps, each characterized by different degrees of automation in the machine learning process. Each level presents its own unique challenges and features. In future articles, I will delve deeper into the characteristics and challenges associated with these three levels.