You’re evaluating AI models. How do you know which factors to prioritize?

According to my perspective, maximum ML model deals with imbalance dataset and which is quite obvious. So for that dataset, balance accuracy and AUC-ROC could be one of the potential matrices for evaluating the minor class. Along with that I would say PPV is also supportive criteria for selecting the right model.

When evaluating AI models, pinpointing my objectives is the crucial first step. I ask myself, What's the endgame? Whether it's a chatbot that needs to deliver swift, coherent responses or a facial recognition system where speed and security cannot be compromised, my goals dictate the benchmarks. This clarity guides me to select appropriate metrics—accuracy for dialogue systems, robustness and speed for recognition technologies. This goal-oriented approach ensures my evaluation process is not just a series of tests but a strategic pathway to achieving specific, desired outcomes with my AI models.

To effectively evaluate AI models, start with defining clear objectives. What is the aim of your model, and what outcomes are you seeking? The goals you set will determine the criteria and metrics vital for assessment. For instance, accuracy, responsiveness, and interaction quality are crucial for chatbots, while speed, security, and robustness take precedence in face recognition systems. Identifying these priorities based on your specific goals is critical for a targeted evaluation, ensuring the model aligns with its intended purpose and delivers the anticipated value.

1️⃣ Purpose-Driven Evaluation: Start by asking, "Why AI?" Identify what AI brings to the table that traditional methods cannot. Is it scalability, accuracy, or the ability to unearth insights from vast datasets? 2️⃣ AI as a Solution Enhancer: Recognize that AI should augment or enhance your solution, not just serve as a technological showcase. 3️⃣ Outcome-Oriented Goals: Define what success looks like in tangible terms. If AI is applied to product recommendations, success might be measured by increased average order value 4️⃣ Benchmarking Against Non-AI Solutions: Evaluate what AI models offer above and beyond existing solutions. 5️⃣ Iterative Goal Refinement: Treat your goals as dynamic, evolving with the project's progression.

When evaluating AI models, focus on factors like accuracy, speed, and simplicity. Consider how well the model performs its task, how quickly it does so, and how easy it is to understand. Also, think about any ethical or legal concerns and how the model might impact people.

Selecting the right metrics is crucial for objectively comparing AI models, reflecting the project's specific goals. Metrics quantify aspects like performance or efficiency, including accuracy, F1-score, ROC curve, latency, and more. The choice of metrics should align with your problem domain and model type. For classification, accuracy and F1-score gauge prediction correctness, while regression tasks might call for mean squared error or R-squared to assess data fit. This careful selection ensures evaluations are tailored, meaningful, and directly linked to the project's success criteria, guiding informed decision-making in model selection.

For missions like Freeform/Structured Prompt Engineering, Retrieval-Augmented Generation (RAG), and Knowledge Extraction, choose evaluation methods tailored to each task's specifics: Freeform/Structured Prompt Engineering: Use human evaluation for creativity, relevance, and clarity, supplemented with NLP metrics for lexical diversity and coherence. RAG: Employ Precision, Recall, F1 for retrieval accuracy, and BLEU/ROUGE for text quality. A/B testing compares RAG with baseline models. Knowledge Extraction: Precision, Recall, F1 for entity accuracy, and Hit@K for knowledge base completion. Include qualitative assessments for practical usability.

Some metrics are underrated, such as nDCG and MAP. Make sure to understand these metrics well, as they may be the best measures for evaluating your model.

Choose metrics that directly align with your model's purpose. For example, using accuracy for a spam filter model, or F1 score for a sentiment analysis model.

Selecting relevant metrics is crucial in objectively evaluating and comparing AI models to ensure they align with your specific goals. Metrics, numerical values gauging aspects like performance or efficiency, must be tailored to the problem domain, data type, and model nature. For classification tasks, accuracy, precision, recall, and F1-score are commonly used to assess predictive capability. In regression scenarios, mean squared error, mean absolute error, or R-squared are preferred to evaluate model fit. By choosing metrics pertinent to your objectives, you enable a focused, objective assessment of each model’s effectiveness in addressing the task at hand.

Evaluating models against test data, distinct from training and validation sets, is key. This data mirrors real-world scenarios your models will face, offering insights into their generalization capabilities. Utilizing chosen metrics, like accuracy, facilitates a direct comparison, allowing for a ranking based on performance. For instance, models can be ordered by their accuracy on test data, pinpointing the top performer. This method provides a clear, objective basis for selecting the model best suited to navigate the complexities of unseen data, ensuring a choice that balances precision with practical applicability.

Comparing models on test data, separate from training and validation sets, is crucial for evaluating generalization to new, unseen data. This test data should closely mirror real-world data your models will face in production. Using previously selected metrics, models are assessed on this test data, providing a clear basis for comparison and ranking. For instance, with accuracy as a metric, models can be ranked by their accuracy scores on test data, allowing selection of the top-performing model. This process ensures a robust evaluation, identifying the model most likely to perform well in real-world scenarios.

Three crucial considerations for your test data: 1. If your data is imbalanced, ensure enough cases of the minority class are in your test set. 2. Your test data must, in the plainest of terms, be indicative of the data your model expects to receive at inference 3. Guard against data leakage, where part of your training data ends up in the testing data

Divide your data into training, validation, and test sets. Train on the training set, validate on the validation set to fine-tune hyperparameters, and finally evaluate performance on the unseen test set. This provides a more robust estimate of generalizability.

Assessing data quality is paramount in evaluating AI models. ✅Prioritize factors like completeness, consistency, accuracy, and relevance of data. ✅Conduct thorough data profiling, validation, and cleansing processes. ✅Leverage statistical analysis and domain expertise to identify anomalies and outliers. ✅Collaborate with domain experts and stakeholders to ensure data relevance and reliability. ✅Regularly monitor and update datasets to maintain high quality over time.

It's crucial to weigh trade-offs and consider constraints. You might need to balance factors like performance against efficiency or accuracy against interpretability. Additionally, constraints such as budget, time, resource availability, regulatory compliance, and ethical considerations play a significant role in decision-making. Understanding and prioritizing these factors according to your specific context and goals are essential. For instance, under tight budget and time constraints, opting for a simpler, faster model might be more practical than a complex one with marginally better accuracy. This evaluative approach ensures that model selection aligns with both operational requirements and strategic objectives.

In evaluating AI models, prioritizing factors involves a nuanced understanding of trade-offs and constraints. By quantifying metrics such as accuracy, latency, interpretability, and compliance, you can perform a cost-benefit analysis to determine the optimal balance. Overcoming this challenge requires leveraging techniques like sensitivity analysis and optimization algorithms to navigate the complex landscape of competing priorities.

When evaluating AI models, consider trade-offs: 1. Performance vs. Interpretability: Balance accuracy with model complexity. 2. Model Complexity vs. Computational Resources: Assess resources needed for complex models. 3. Bias vs. Fairness: Mitigate bias without sacrificing performance. 4. Robustness vs. Generalization: Ensure models perform well in various scenarios. 5. Scalability vs. Efficiency: Optimize resource usage while scaling. 6. Ethical and Regulatory Constraints: Ensure compliance with ethical and legal standards. 7. Cost-Benefit Analysis: Weigh benefits against development and maintenance costs.

Generally, when dealing with multiple constraints, it is best to define a minimum acceptable threshold for metrics like inferencing time, compute, memory. These thresholds need to be defined first, and with clear communication and understanding between all stakeholders. This, I feel, "frees" you to maximize on model metrics like accuracy and precision in development, and continue to do so iteratively.

I meticulously weigh factors such as performance, efficiency, accuracy, and interpretability against constraints like budget, time, resources, regulations, and ethical standards. It's crucial to strike a balance that aligns with the problem context and priorities. For instance, in scenarios with limited budget and time, opting for a simpler and faster model over a complex and slower one may be preferable, even if the latter boasts slightly higher accuracy.

The step almost everyone forgets to do when it comes to AI models is to be constantly testing and monitoring them... especially when they're in production. The cause of so many problems with ML models is that they "Drift" over time. The data changes in one way or another, which causes data drift. The model's performance changes in other ways based on user behavior and model configuration (or moderation) challenges, resulting in "model drift". What is most concerning, especially, is if you're using a third-party model that you don't control, such as LLM / GPT models from other companies. Their behavior sometimes changes on a daily basis. This can cause major problems for apps that depend on them. This means constant evaluation is a MUST.

When evaluating AI models in production, prioritize factors that impact performance and alignment with business objectives. Consider model accuracy, speed, and reliability under real-world conditions. Monitor how the model adapts to new data and its scalability to handle increased loads. Assess the model's impact on operational efficiency and decision-making quality. Also, evaluate the cost of running the model in production, including computational resources and maintenance needs. This comprehensive assessment ensures the model delivers value and meets the practical demands of your business environment.

Just before production, I would always UAT the model on unseen data to gauge its ability to handle real-world scenarios beyond the training data. This helps avoid overfitting, where the model performs well on training data but poorly on unseen data.

Factor to consider: Data drift is when the data being fed into the model changes over time. Tools to identify and remedy it include Databricks and Seldon Alibi. You can also consider using scikit-multiflow which has excellent functionality for handing data streams.

Like @ron Schmelzer explained AI ML DL must be tested daily. There should be carefully selected test data sets including fringe data sets to test that runs the black box through all its paces. This needs a dedicated development team round the clock and weekends too, change control, rollback plans, backup restore plans. Failover from bad production AI to good production AI in another datacenter for a acute problem and a means to normalize the databases later. The trash in trash out with AI much more dangerous.

Evaluate the conceptual soundness of the design choice. For instance, one could evaluate whether it is sound to use AI for a certain use case (or a more simple analytical technique could do the job at a lower cost).

This is a monster of a question/article to be honest. It honestly depends on the use case and the branch of AI. But some elements can be generalized, like: 1. Ethical considerations - including data bias 2. Explainability 3. Scalability 4. ESG impact - from power to social impact 5. Cost effectiveness 6. Accuracy and performance Now each one of the above has a mile long sheet of examples and samples and methods and "newly developing best practices"

In evaluating AI models, a critical yet less discussed factor to prioritize is the model's contextual adaptability across diverse, real-world scenarios. This includes its ability to handle data shifts, anomalies, and emerging patterns without significant degradation in performance. Often, models are assessed in controlled conditions or on historical datasets that may not fully capture future complexities or the dynamic nature of real-world data. Prioritizing a model's resilience to changes and its capacity to maintain accuracy and reliability in unforeseen situations ensures long-term effectiveness and utility, safeguarding against overfitting to current data trends and enhancing its sustainable application in evolving environments.

In the AI maze, remember we're creating solutions for humans, not just numbers. Consider fairness and bias in AI, not just accuracy. Our models could perpetrate discrimination without us realizing it. And AI results should be digestible for people, not just numerical triumphs. AI's real strength lies in improving lives, ensuring fairness and clarity. Let’s hold our models to this humane standard.

When evaluating AI models, prioritize factors based on the model's intended use case and the specific problems it aims to solve. Accuracy is crucial, but also consider the model's fairness and bias, to ensure equitable outcomes. Efficiency and scalability are important for deployment in varied environments, while interpretability and explainability enhance trust and user understanding. Lastly, compliance with regulatory and ethical standards safeguards against legal and social risks. Balancing these factors according to your project's priorities ensures a well-rounded and effective AI solution.