Machine learning pipelines offer many benefits.

• Modularization: Pipelines enable you to break down the machine learning process into modular, well-defined steps. Each step can be developed, tested and optimized independently, making it easier to manage and maintain the workflow.
   

• Reproducibility: Machine learning pipelines make it easier to reproduce experiments. By defining the sequence of steps and their parameters in a pipeline, you can recreate the entire process exactly, ensuring consistent results. If a step fails or a model's performance deteriorates, the pipeline can be configured to raise alerts or take corrective actions.
   

• Efficiency: Pipelines automate many routine tasks, such as data preprocessing, feature engineering and model evaluation. This efficiency can save a significant amount of time and reduce the risk of errors.
   

• Scalability: Pipelines can be easily scaled to handle large datasets or complex workflows. As data and model complexity grow, you can adjust the pipeline without having to reconfigure everything from scratch, which can be time-consuming.
   

• Experimentation: You can experiment with different data preprocessing techniques, feature selections, and models by modifying individual steps within the pipeline. This flexibility enables for rapid iteration and optimization.
   

• Deployment: Pipelines facilitate the deployment of machine learning models into production. Once you've established a well-defined pipeline for model training and evaluation, you can easily integrate it into your application or system.
   

• Collaboration: Pipelines make it easier for teams of data scientists and engineers to collaborate. Since the workflow is structured and documented, it's easier for team members to understand and contribute to the project.
   

• Version control and documentation: You can use version control systems to track changes in your pipeline's code and configuration, ensuring that you can roll back to previous versions if needed. A well-structured pipeline encourages better documentation of each step.

Machine learning technology is advancing at a rapid pace, but we can identify some broad steps involved in the process of building and deploying machine learning and deep learning models. 

1. Data collection: In this initial stage, new data is collected from various data sources, such as databases, APIs or files. This data ingestion often involves raw data which may require preprocessing to be useful.
    

2. Data preprocessing: This stage involves cleaning, transforming and preparing input data for modeling. Common preprocessing steps include handling missing values, encoding categorical variables, scaling numerical features and splitting the data into training and testing sets.
    

3. Feature engineering: Feature engineering is the process of creating new features or selecting relevant features from the data that can improve the model's predictive power. This step often requires domain knowledge and creativity.
    

4. Model selection: In this stage, you choose the appropriate machine learning algorithm(s) based on the problem type (e.g., classification, regression), data characteristics, and performance requirements. You may also consider hyperparameter tuning.
    

5. Model training: The selected model(s) are trained on the training dataset using the chosen algorithm(s). This involves learning the underlying patterns and relationships within the training data. Pre-trained models can also be used, rather than training a new model.
    

6. Model evaluation: After training, the model's performance is assessed using a separate testing dataset or through cross-validation. Common evaluation metrics depend on the specific problem but may include accuracy, precision, recall, F1-score, mean squared error or others.
    

7. Model deployment: Once a satisfactory model is developed and evaluated, it can be deployed to a production environment where it can make predictions on new, unseen data. Deployment may involve creating APIs and integrating with other systems.
    

8. Monitoring and maintenance: After deployment, it's important to continuously monitor the model's performance and retrain it as needed to adapt to changing data patterns. This step ensures that the model remains accurate and reliable in a real-world setting.

Machine learning lifecycles can vary in complexity and may involve additional steps depending on the use case, such as hyperparameter optimization, cross-validation, and feature selection. The goal of a machine learning pipeline is to automate and standardize these processes, making it easier to develop and maintain ML models for various applications.

The history of machine learning pipelines is closely tied to the evolution of both machine learning and data science as fields. While the concept of data processing workflows predates machine learning, the formalization and widespread use of machine learning pipelines as we know them today have developed more recently.

Early data processing workflows (Pre-2000s): Before the widespread adoption of machine learning, data processing workflows were used for tasks such as data cleaning, transformation and analysis. These workflows were typically manual and involved scripting or using tools like spreadsheet software. However, machine learning was not a central part of these processes during this period.

Emergence of machine learning (2000s): Machine learning gained prominence in the early 2000s with advancements in algorithms, computational power and the availability of large datasets. Researchers and data scientists started applying machine learning to various domains, leading to a growing need for systematic and automated workflows.

Rise of data science (Late 2000s to early 2010s): The term "data science" became popular as a multidisciplinary field that combined statistics, data analysis and machine learning. This era saw the formalization of data science workflows, including data preprocessing, model selection and evaluation, which are now integral parts of machine learning pipelines.

Development of machine learning libraries and tools (2010s): The 2010s brought the development of machine learning libraries and tools that facilitated the creation of pipelines. Libraries like scikit-learn (for Python) and caret (for R) provided standardized APIs for building and evaluating machine learning models, making it easier to construct pipelines.

Rise of AutoML (2010s): Automated machine learning (AutoML) tools and platforms emerged, aiming to automate the process of building machine learning pipelines. These tools typically automate tasks such as hyperparameter tuning, feature selection and model selection, making machine learning more accessible to non-experts with visualizations and tutorials. Apache Airflow is an example of an open-source workflow management platform that can be used to build data pipelines.

Integration with DevOps (2010s): Machine learning pipelines started to be integrated with DevOps practices to enable continuous integration and deployment (CI/CD) of machine learning models. This integration emphasized the need for reproducibility, version control and monitoring in ML pipelines. This integration is referred to as machine learning operations, or MLOps, which helps data science teams effectively manage the complexity of managing ML orchestration. In a real-time deployment, the pipeline replies to a request within milliseconds of the request.