Revolutionizing the Machine Learning Lifecycle: A Comprehensive Survey of MLOps

MLOps: Automating the Machine Learning Lifecycle

MLOps is an emerging discipline that focuses on automating the entire machine learning lifecycle. In this comprehensive survey of MLOps, we explore its significance and various features.

Model Requirements Analysis

Before starting a machine learning project, stakeholders need to analyze and identify model requirements. This involves considering factors like business value, model quality, human value, and ethics. Stakeholders should define objectives, assess tools, involve relevant stakeholders, and determine necessary functions.

Data Collection and Preparation

Data preparation is crucial for high-quality data in machine learning tasks. This phase covers data collection, discovery, augmentation, and the ETL process. It emphasizes data quality checking, cleaning, merging, and conducting exploratory data analysis.

Feature Engineering

Feature engineering improves predictive modeling performance. This involves techniques like feature selection, extraction, construction, scaling, labeling, and imputation. Specific algorithms like PCA, ICA, and standardization and normalization are mentioned.

Model Training

The model training phase covers different types of machine learning models and model selection. It explores methods like cross-validation, bootstrapping, and hyperparameter tuning.

Model Evaluation

Model evaluation assesses a model’s performance using metrics like accuracy, precision, recall, F-score, and AUC. Both performance and business value should be considered.

System Deployment

System deployment involves selecting an ML model operating platform, integration, testing, and releasing to end users. Deployment strategies like canary and blue-green deployment are explained, along with tips for a smooth process.

Model Monitoring

Model monitoring is significant in ML systems. The paper explores drift detection, model quality, compliance, system logging, and model explanation. It emphasizes monitoring changes in data distribution, ensuring model performance, complying with standards, and achieving transparency.

Conclusion

The survey concludes by discussing the future of MLOps and challenges in scalability and reliability. Continuous monitoring and maintenance of ML models are crucial for long-term success.

In summary, this comprehensive survey provides valuable insights into MLOps pipelines, challenges, best practices, and various stages of the machine learning process. It aims to help researchers and practitioners better understand MLOps and its practical implications.

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