ML Engineering
The discipline of applying engineering principles to develop, deploy, and maintain machine learning systems.
ML Model
The mathematical and algorithmic representations used in machine learning for tasks like classification and regression.
ML Ops (Machine Learning Operations)
The set of practices and tools for managing and deploying machine learning models.
ML algorithm selection
The process of choosing the most suitable machine learning algorithm for a given problem.
ML deployment automation
Automating the process of deploying machine learning models into production environments.
ML deployment strategies
Strategies and approaches for deploying machine learning models in real-world applications.
ML feature engineering
The process of creating new features from existing data to improve the performance of machine learning models.
ML interpretability techniques
Methods for understanding and explaining the decisions made by machine learning models.
ML model accuracy improvement
Techniques and strategies for enhancing the accuracy and performance of machine learning models.
ML model comparison
The evaluation and comparison of multiple machine learning models to determine the best-performing one.
ML model deployment
The process of deploying trained machine learning models for use in production systems.
ML model evaluation
The assessment of the performance and effectiveness of machine learning models using various metrics.
ML model explainability
The ability to interpret and explain the decisions and predictions made by machine learning models.
ML model fine-tuning
The process of adjusting hyperparameters and model configurations to optimize performance.
ML model governance
The establishment of policies and processes to ensure the responsible and ethical use of machine learning models.
ML model management
The ongoing maintenance, monitoring ,and updating of machine learning models in production.
ML model optimization
Techniques for improving the efficiency and resource utilization of machine learning models.
ML model performance evaluation
The assessment of a machine learning model's performance using metrics and testing.
ML model scalability
The ability of a machine learning model to handle increasing amounts of data or user interactions.
ML model validation
The process of testing and verifying the accuracy and reliability of machine learning models.
ML model versioning
The practice of keeping track of different versions of machine learning models to ensure reproducibility.
ML workflow automation
Automating the steps involved in designing, training, and deploying machine learning models.
Machine Learning Collaboration Tool
Tools and platformsthat facilitate collaboration among teams working on machine learning projects.
Machine Learning Collaboration Tool
Tools and platformsthat facilitate collaboration among teams working on machine learning projects.
Machine learning tools
Software and libraries used to develop, train, and deploy machine learning models.
Model Deployment
The process of making a trained machine learning model available for prediction or inference in a productionenvironment, typically involving deploying the model to a server or cloud-based infrastructure for serving predictions
Model Deployments
The process of making machine learning models available for use in real-world applications.
Model Evaluation:
The process of assessing the performance of a trained machine learning model using various metrics, such as accuracy, precision, recall, F1 score, etc., to measure its effectiveness in making predictions or classifications.
Model Experiments
Systematic tests and iterations performed on machine learning models to improve their performance.
Model Governance
The practice of establishing policies, guidelines, and controls for managing machine learning models throughout their lifecycle, including model development, deployment, monitoring, and retirement, to ensure compliance, security, and reliability.
Model Monitoring
The process of tracking and measuring the performance, behavior, and health of deployed machine learning models in production, to detect and resolve any issues or deviations from expected behavior.
Model Registry
A centralized repository or catalog that stores metadata, configuration, and artifacts of machine learning models, such as trained models, hyperparameters, and associated documentation, to enableeasy discovery, sharing, and versioning of models.
Model Retraining
The process of periodically updating and retraining machine learning models with new data to ensure that the modelremains accurate and relevant over time, accounting for changes in data distribution or business requirements.
Model Serving
The process of making machine learning modelsavailable for prediction or inference by receiving input data, processing itthrough the model, and returning the model's predictions or outputs to therequesting system or application.
Model Sharing
The practice of sharing trained machine learning models with other team members or the community.
Model Tuning
The process of optimizing hyperparameters or model architecture to improve the performance and accuracy of a machine learning model, often involving techniques such as grid search, random search, or Bayesian optimization.
Model Versioning
The practice of keeping track of different versions or iterations of machine learning models, including their trained parameters, hyperparameters, and associated code, to enable reproducibility, comparison, and rollback of model versions.
Model interpretability tools
Tools that provide insights into how machine learning models make decisions.
Models:
Mathematical representations or algorithms that aretrained on data to make predictions, classifications, or generate insights.