What are the criteria for evaluating an applied ML model?

AI solutions should directly contribute to business success and strategies and need to be align with business needs and business challenges. Each AI initiative shall ensure optimal use of time, money and personal. Only aligned initiatives bring value to the organization and increase user adoption and acceptance.

It's essential to consider its alignment with business goals and stakeholder needs, assess the quality and relevance of the data used, and analyze the model's performance and accuracy using suitable metrics. Interpretability is crucial for trust and fairness, and explainable models facilitate better insights. Furthermore, the scalability and robustness of the model ensure it meets deployment needs effectively, adapting to varying conditions and maintaining compliance. These aspects comprehensively guarantee the model's reliability and value delivery to users.

Business alignment in the context of evaluating applied machine learning models involves ensuring that the model is closely tied to the specific needs and objectives of the business. This includes setting clear and measurable goals, such as enhancing customer satisfaction, cutting costs, boosting revenue, or improving efficiency. Additionally, it involves identifying key performance indicators (KPIs) that enable the tracking and communication of the model's impact on these business outcomes. For example, when assessing a model designed to predict customer churn, metrics like accuracy, recall, precision, or F1-score can be used to gauge its performance.

The criteria for evaluating an ML model requires both standard ML evaluation using common model performance metrics such as ROC-AUC,F-1, Adjusted R2, MAE etc. The ML model evaluation cannot end with only the standard ML metric evaluation especially when the model solves a business problem. For business and industry it is important to consider evaluating the model on a metric that makes business sense such detection rate, alert rate ,false positive ratio etc. This all depends on domain expertise and what metric really is important to the business to the business to optimize. In other words the metric for evaluation must align with business objective. In highly regulated industries model interpretability is also very important.

Journey start from Aligning with top who is funding your work! An aligned ML model is more likely to deliver meaningful business value and be a strategic asset rather than a technical novelty, in my view. In wholesome the business alignment is tied to the return on investment (ROI) and the model's value proposition. An effective & aligned ML model should be able to demonstrate its potential to deliver tangible business value. In longer-term success, ML models should be scalable and enable seamless integration with existing business processes & systems. The alignment make sure that the model can grow with the business and that it doesn't create bottlenecks or compatibility issues.

Implementing cross-validation techniques is a great way to assess the model's generalizability across different subsets of the data. Regularly updating and re-evaluating the dataset is essential to account for dynamic changes in the real-world context, ensuring the ongoing relevance and effectiveness of your machine learning model. For instance, when predicting customer churn in a subscription-based service, employing cross-validation can help identify patterns that were consistently indicative of churn across diverse user segments, enhancing the model's predictive accuracy in varied scenarios.

Data quality is of paramount importance in developing reliable and valid machine learning models. It entails ensuring that the data used for model training and testing is accurate, complete, consistent, and representative of the real-world context. It is essential to address issues like missing values, outliers, noise, bias, or imbalance that could impact model performance. Employing suitable data cleaning, preprocessing, transformation, and augmentation techniques is vital to enhance data quality and mitigate the risk of overfitting or underfitting in your model.

Another thing I want to add here is to keep the data relevant and specific to the use case. If you try to generalize too much, it'll add more complexity and increase the data size to gain high accuracy level.

One thing that I see over and over again being ignored is data leakage, when applying any sort of transformation to your data you should not apply it to the entire dataset, only to your training data, then apply them to your test set based on the training set values, otherwise you are leaking information from your test set into your training set. There are lots of implications with this, but that is a conversation for another time.

The training data for your model should represent the problem to be solved example being, for a retail industry the seasonal data does not represent the true data or the real time scenario. The true data depends on the type of business the industry deals with. Data should be consistent, clean, diverse and balanced.

A great step up are "ensemble methods". That is combining multiple models to enhance predictive power and resilience. Ensemble techniques, like Random Forests or Gradient Boosting, often outperform individual models by aggregating diverse perspectives, mitigating biases, and providing a more robust solution. In a fraud detection scenario, employing an ensemble of decision trees can not only significantly improved accuracy but also effectively addressed the challenge of imbalanced data, as it captured nuanced patterns of both fraudulent and legitimate transactions, resulting in a more reliable fraud detection model.

Model selection is a critical aspect of machine learning evaluation. It involves choosing the appropriate machine learning algorithm and architecture based on several factors. These factors include the model's complexity, scalability, interpretability, robustness, and compatibility with your data and goals. To make an informed choice, you should compare various models by evaluating their performance on validation and test datasets, using metrics relevant to your specific problem domain and task. For instance, in the case of a classifier model, metrics like accuracy, precision, recall, F1-score, ROC curve, or confusion matrix can be used to compare and select the most suitable model

To evaluate a model, there should be sufficient data to split into testing and training sets. The model needs to be trained using the training dataset and tested using the testing dataset to compute RMSE of the model for making predictions on the unseen test dataset In other words, the purpose of dividing the data into training and testing sets is for the model to train itself on the training set, using the outcome to supervise the learning. After the model learns to generalise patterns on the training set, its outcome is used to predict the result on the unseen test data. This means that the RMSE model is calculated on the test data to check how well the model fits the training dataset

At this point in the game, model selection I feel it is a relatively easy task to manage if you have enough time and resources to try different models, but I feel that instead of using that much time trying to find the best model that will give you 0.01 increase in performance, use that time to improve your features, features can give you greater jumps in performance if done correctly.

AUTOMATE IT!! Model Selection and Model Optimization via hyperparameter tuning process using autoML libraries like TPOT, auto-sklearn, and FLAML. Specifically: - Use TPOT's TPOTClassifier/TPOTRegressor to automate pipeline generation and hyperparameter tuning. - Employ auto-sklearn's fit() and fit_ensemble() to efficiently search over multiple algorithms and parameters. - Leverage FLAML's fit() and BlendSearch to find the best performing model based on time budget. These autoML libraries utilize techniques like genetic programming, Bayesian optimization, and multi-fidelity optimization under the hood to intelligently explore combinations of algorithms and hyperparameters. This can save significant time compared to manual tuning.

Techniques like model pruning are a great way to streamline the complexity of the chosen model, eliminating redundant connections and reducing computational overhead, leading to a more efficient and deployable solution. Pruning techniques, such as weight pruning or neuron pruning, help achieve a balance between model accuracy and computational resources. For instance, in natural language processing tasks, applying weight pruning to a neural network used for sentiment analysis can not only significantly reduced the model's size but also maintain its predictive accuracy. This optimization facilitated faster inference times and will enable the model for more practical for real-time applications, without compromising its performance.

Model optimization is the process of fine-tuning a chosen machine learning model to enhance its performance and efficiency. This involves adjusting both parameters and hyperparameters. Parameters, like weights and biases, are learned during training, while hyperparameters, such as learning rate and batch size, are set before training. Optimization techniques like grid search, random search, or Bayesian optimization are employed to find the best combination of hyperparameters that minimize the model's error or loss function, ultimately improving its effectiveness.

I would say an optimized model is typically more resource-efficient. Meaning, it can make predictions faster and with fewer computational resources. Model optimization helps improve a model's ability to generalize. Meaning, the model that generalizes well can perform accurately on unseen data, which is important for any real-world applications. During model optimization, there are elements of feature engineering. Here, optimizing the selection & transformation of the features could lead to a more effective model that captures relevant patterns in the data. Optimization may consider the trade-off between model complexity & interpretability. As we know, the complex models can be optimized for performance,but they may be less interpretable.

The application limits must be identified and an active control of being within the limits should be put in place before start of commercial operation.

One important thing to consider before deploying AI models is whether the business expects the prediction in real-time or in a periodic manner. The design of the system will be different based on these scenarios. For real-time predictions, the system need to be highly scalable to spawn multiple model replicas to simultaneously serve sudden large number of requests without a sharp increase in response time. The excess replicas will be removed as the request count goes down to prevent unnecessary resource cost. As for the cases where the result is queried at specific times/intervals, a queuing mechanism can be used to store incoming request data in a queue and prediction will be done by taking the data from the queue in batches.

With continuous integration and continuous deployment (CI/CD) pipelines which can automate the deployment process, and ensuring a seamless and efficient transition from development to production. CI/CD practices facilitate regular updates, reducing downtime and allowing for rapid adaptation to evolving requirements. For example, in an e-commerce recommendation system, employing CI/CD pipelines can enabled the automatic deployment of model updates based on user interactions and feedback. This will not only enhance the relevance of product recommendations but also ensures a dynamic and responsive system that could quickly adapt to changing user preferences and market trends.

Model deployment marks the culmination of a machine learning project, as it involves making the model accessible to end-users or customers. Key considerations include assessing how well the model integrates with existing systems, its user-friendliness, ease of maintenance, and update capabilities. Real-world monitoring is crucial to track the model's performance and behavior, while gathering feedback from users and stakeholders is essential. Techniques like logging, testing, debugging, and A/B testing help identify and rectify any issues or errors that may emerge during deployment, ensuring a successful and functional integration into production environments.

In the deployment phase, transparency is key, especially in critical fields like healthcare/finance. We CANNOT have black-box models; people need to understand how decisions are made. Tools like LIME or SHAP can demystify model predictions, translating complex processes into PLAIN language. Equally important is ensuring fairness. We’ve got to be vigilant, constantly checking for bias and correcting it when we find it. It’s not just about building trust; it’s about doing the RIGHT thing. Also, a SOLID alerting system keeps you on your toes, ensuring good performance and that resources are used wisely.

Another focus area can be to conduct bias detection and mitigation strategies to address potential biases in the model. This will ensure fairness across diverse demographic groups. Techniques like re-sampling or adjusting loss functions can help mitigate biases, promoting equity in predictions. For example, in a recruitment model, incorporating bias detection and mitigation measures can reduce the impact of gender bias, ensuring fair evaluation of candidates. This will not only align with ethical considerations but also enhanced the model's credibility and acceptance in a diverse and inclusive hiring environment.

In my view, evaluating a model's ethical dimensions is as important as evaluating its performance, efficiency, and business alignment. ML models should be transparent & explainable. Here, evaluation includes assessing the model's ability to provide insights into how it reaches its conclusions. Evaluating the model's effectiveness in addressing bias through techniques such as debiasing, re-sampling, or fairness-aware algorithms is an important of whole evaluation process. Also, an ethical ML models must comply with legal and regulatory requirements. Its evaluation should confirm that the model operates within the boundaries of laws, and in turn, avoiding potential legal & ethical liabilities.

Model ethics is a crucial factor when assessing an applied machine learning model. It involves considering the social, legal, and ethical ramifications of the model, including fairness, transparency, accountability, privacy, and security. Ensuring that the model does not discriminate, cause harm, or mislead users, and respects their rights and values is essential. Following established best practices and guidelines for ethical and responsible machine learning, such as AI Ethics Principles, Responsible AI Framework, or ML Fairness Checklist, is imperative to uphold ethical standards in model development and deployment.