Architectural Blueprints—The “4+1” View Model of Machine Learning
Architectural Blueprints—The “4+1” View Model of Machine Learning
Based on the inspiration from “Architectural Blueprints—The “4+1” View Model of Software Architecture" [1], which “presents a model for describing the architecture of software-intensive systems, based on the use of multiple, concurrent views,” we developed “Architectural Blueprints—The “4+1” View Model of Machine Learning (ML).” We can capture it in this formula: ???
ML Architectural Blueprints = {Scenarios, Accuracy, Complexity, Interpretability, Operations}
ML is a form of Artificial Intelligence (AI), which makes predictions and decisions from data. It is the result of training algorithms and statistical models to analyze and draw inferences from patterns in data, which are able to learn and adapt without following explicit instructions. However, you need to:
The Assumptions, Biases, and Constraints (ABC) of data science, Data, and Models of ML can be captured in this formula: ???
Machine Learning = {Assumptions/Biases/Constraints, Data, Models}
Architectural Blueprints—The “4+1” View Model of Machine Learning
ML Architectural Model:
To capture a machine learning architecture, we use a “4+1” ML view model. This model is composed of five main views:
Architectural Blueprints of Machine Learning: Scenarios, Accuracy, Complexity, Interpretability, and Operations Views
ML Architectural Blueprints = {Scenarios, Accuracy, Complexity, Interpretability, Operations}
Your machine learning scenarios depend on learning categories, data types, and objectives. The four major categories are supervised, semi-supervised, unsupervised, and reinforcement. Data types include discrete, continuous, qualitative, quantitative, or large data items. Your objectives can be to predicate a category or a number, divide by similarity, or identify sequences.?
Read the "Scenarios: Which Machine Learning (ML) to choose?" [2] article at https://www.dhirubhai.net/pulse/machine-learning-101-which-ml-choose-yair-rajwan-ms-dsc.
"Model fitting is a measure of how well [optimize] a machine learning model generalizes to similar [evaluation] data to that on which it was trained. A model that is well-fitted [optimal-fitted] produces more accurate outcomes. A model that is overfitted matches the data too closely. A model that is underfitted does not match closely enough."
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Accuracy is affected by your model fitting. And, model fitting depends on Bias-Variance Trade-off in machine learning. Balancing bias and variance can solve overfitting and underfitting.
Read the "Accuracy: The Bias-Variance Trade-off" [3] article at https://www.dhirubhai.net/pulse/accuracy-bias-variance-tradeoff-yair-rajwan-ms-dsc.
Computational complexity of an algorithm is a fundamental concept in computer science. It is necessary to be taken into account because it affects the amount of resources required to run your model. These required resources are estimated for time, space, and sample complexities.
These estimated computational complexities are approximated for a variety of different machine learning algorithms during Training and Prediction.
Read the "Complexity: Time, Space, & Sample" [4] article at https://www.dhirubhai.net/pulse/complexity-time-space-sample-yair-rajwan-ms-dsc.
In order to understand and trust your model prediction/decision and reasoning, there are two tantamount and equivalent factors that need to be considered: Interpretability and Explainability.
Interpretability is “The ability to determine and observe cause and effect from a machine learning.”
Explainability is “The ability to justify the reason and its importance of a machine learning results.”
Read the “Interpretability: "Seeing Machines Learn” – Understanding ML models' prediction/decision" [5] article at https://www.dhirubhai.net/pulse/interpretabilityexplainability-understanding-models-rajwan-ms-dsc. ?
In machine learning, ML Operations (MLOps) and Continuous Machine Learning (CML) are a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. MLOps includes techniques and tools for implementing and automating ML pipelines: Continuous Integration (CI), Continuous Delivery/Deployment (CD), Continuous Training/Testing (CT), and Continuous Monitoring (CM) - MLOps (CI/CD/CT/CM).
Read the "Operations: ML Operations (MLOps), Continuous ML & AutoML" [6] article at https://www.dhirubhai.net/pulse/ml-operations-mlops-continuous-automl-yair-rajwan-ms-dsc.
Next, read the "Data Science Approaches to Data Quality: From Raw Data to Datasets" article at?https://www.dhirubhai.net/pulse/data-science-approaches-quality-from-raw-dataset-yair-rajwan-ms-dsc.
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Next, read the "Data Science Approaches to Data Quality: From Raw Data to Datasets" article at?https://www.dhirubhai.net/pulse/data-science-approaches-quality-from-raw-dataset-yair-rajwan-ms-dsc
Read the "Machine Learning 101 – Which Machine Learning (ML) to choose?" article at?https://www.dhirubhai.net/pulse/machine-learning-101-which-ml-choose-yair-rajwan-ms-dsc