Machine Learning Pipelines
- [Machine Learning Pipeline in Production - Wikipedia]
- Overview
Data science is an interdisciplinary field focused on extracting knowledge from typically large data sets and applying the knowledge and insights from that data to solve problems in a wide range of application domains.
This area includes preparing data for analysis, formulating data science questions, analyzing data, developing data-driven solutions, and presenting research results to inform high-level decision-making in a wide range of application areas.
As such, it combines skills from computer science, statistics, information science, mathematics, data visualization, information visualization, data sonification, data integration, graphic design, complex systems, communications and business.
A machine learning (ML) pipeline is a way to code and automate the workflow required to generate ML models. A ML pipeline consists of sequential steps that perform everything from data extraction and preprocessing to model training and deployment.
- Stages of ML Pipelines
A data pipeline in ML is a method for gathering and managing datasets needed for model training. The data pipeline ingests raw data from various sources and ports it to a data store, like a data lake or data warehouse, for analysis. The data is usually processed before it flows into a data repository.
The main purpose of a data pipeline is to get the data into a form that your model can digest and understand. The underlying architecture of your pipeline will vary depending on the sources and data types you are drawing from.
Data pipelines consist of three essential elements: a source or sources, processing steps, and a destination.
A pipeline consists of sequential steps that perform everything from data extraction and preprocessing to model training and deployment. Typical stages include:
- Data collection
- Data preprocessing
- Construct datasets
- Model training and refinement
- Evaluation
- Deployment to production
Pipelines help automate the entire MLOps workflow, from data collection, EDA, and data enhancement to model building and deployment. Copying, tracking, and monitoring are also supported after deployment.
Workflow focuses on how a project goes through a series of status changes during its life cycle. A pipeline focuses on the end-to-end process of moving a project through a series of stages or tasks.
- Workflows and Data Pipelines in Machine Learning
Workflow involves sequencing and dependency management of processes. Workflow dependencies can be technical or business-oriented. A data pipeline is a series of processes that migrate data from a source to a destination database.
A machine learning (ML) pipeline is a way to automate the workflow of producing a machine learning model. Pipelines are a crucial component of the modern data science workflow. They help automate the process of building, training, and deploying machine learning models.
ML workflows define which phases are implemented during a machine learning project. The typical phases include data collection, data pre-processing, building datasets, model training and refinement, evaluation, and deployment to production.
Here are some basic steps in a machine learning pipeline:
- Data preprocessing: Preparing the ingested data for use in model training. This includes cleaning, transformation, and integration.
- Model deployment: Putting a trained machine learning model into production and tracking its performance.
- Model evaluation: Evaluating the performance of the trained model instance.
- Model training: Training the model based on the data you have collected.
- Model training and tuning: Tuning the parameters after evaluation.
- Hyperparameter tuning: Searching for the optimal set of values that minimize the validation error.
- Model selection: Selecting a well-fitting model.
Other steps in a ML pipeline include: Data collection, Feature engineering, Data ingestion.
[More to come ...]
