Define Machine Learning Inference

Machine learning inference involves deploying a trained machine learning model to analyze new, real-world data and generate predictions or decisions. The machine learning algorithm (“ML Model”) has a process that feeds new data into the model, which is then processed, and an output is produced. The new data can originate from various sources, such as sensors, databases, or real-time streams. The model's output can be utilized to make informed decisions, optimize processes, or gain valuable insights. To implement machine learning inference, three essential components are required: a data source, an inference engine, and a data destination. The data source provides the new data, the inference engine processes the data using the trained model, and the data destination receives the ML model's output.

Machine learning (ML) models undergo a two-phase lifecycle: training and inference. During the training phase, the model is developed by feeding it a specific dataset to learn patterns and relationships within the data. In the inference phase, the trained model is deployed to analyze new, real-world data and generate predictions or decisions. This process, often referred to as "scoring," involves the model processing the input data and producing an output, which can be a numerical score, a classification, or another form of prediction.

Typically, DevOps engineers or data engineers are responsible for deploying ML or AI models into production. However, in some cases, data scientists who train the models may be involved in the deployment process. This can lead to challenges, as data scientists may not possess the necessary system deployment skills. To ensure successful ML deployments, close collaboration between different teams is crucial. Emerging practices like MLOps are helping to streamline this process by providing a structured approach to deploying, maintaining, and updating ML models in production.

Machine Learning Inference Deployment

To deploy a machine learning inference environment, you need three main components in addition to the model:

1. Data Source: This is where the new data originates. It could be a variety of sources, such as sensors, databases, or real-time data streams.
2. Inference Engine: This is the software system that hosts the trained machine learning model. It accepts new data from the data source and processes it using the model.
3. Data Destination: This is where the model's predictions or decisions are sent. It can be a database, a visualization tool, or a system that takes action based on the results.

In machine learning inference, data sources capture real-time data from various mechanisms, such as sensors or databases. This data is then fed into a host system, which processes it using a machine learning model. The model generates an output, which is delivered to designated data destinations for further analysis or action.

There are a variety of data sources used, such as Apache Kafka clusters storing IoT device data, web application log files, point-of-sale (POS) machine data, or web applications collecting user clicks. These sources capture real-time data and feed it into the machine learning model for analysis.

The host system serves as the infrastructure to deploy and execute the ML model. It receives data from various sources, processes it, and feeds it into the model. Once the model generates an output, the host system delivers the result to designated destinations. This host system can be a web application accepting data via REST APIs or a stream processing application handling high-volume data streams from Apache Kafka.

Traditionally, DevOps or data engineers handle the deployment of ML or AI models into production environments. However, data scientists, who often train these models, may be involved, which can sometimes lead to challenges due to potential skill gaps in system deployment. To ensure successful ML deployments, effective collaboration between diverse teams is essential. Emerging practices like MLOps provide a structured framework for deploying, maintaining, and updating ML models, streamlining the process and mitigating potential issues.