How can AI professionals use performance metrics to improve their work?

Choosing relevant performance metrics is vital in AI as it aligns model evaluation with specific objectives and real-world applications. For instance, in healthcare AI, emphasising recall minimises life-threatening false negatives, while in marketing, precision ensures targeted and efficient recommendations. This focus directs optimisation efforts and avoids overfitting to less pertinent criteria. Moreover, relevant metrics build user trust by demonstrating the model's practical utility and reliability, necessary for widespread adoption. Hence, the choice of metrics is not merely technical, but a strategic decision impacting the success and pragmatism of AI solutions.

This is arguably the most difficult phase, as well as the most critical one. Choosing relevant metrics shouldn't only be driven by academic research. It should also be driven by business objectives and constraints. For e.g. if the use case demands higher recall, there is no point in making F1 score a metric. Examples - Medical diagnosis for pandemic, fraud detection, etc.

In my experience, the initial metric selection is pivotal for AI projects. Tailoring metrics to your project's objectives, user expectations, and unique challenges is essential. These metrics should precisely reflect what matters most to the end-users and how the AI system influences their experience. Additionally, relevant metrics help identify critical challenges and potential risks early in development, guiding project enhancements and ensuring the project's alignment with its intended goals.

I would go with these approaches as top 2 starting points for ways to use performance metrics can help improve an AI project or AI professionals:1. create performance metrics to assess the effectiveness of AI models. By having insights on how well a model is performing is such a great way to pin point areas that need improvement or refinement. 2. Detecting "overfitting and underfitting", finding what model performs well on training data but poorly on new data (overfitting) or which models perform poorly on both training and new data (underfitting). Additionally, I would consolidate the various metrics that would be created to test models over time and develop a monitor and maintenance AI performance insights for continuous monitoring.

In my experience, it’s not just about numbers there’s insights that could truly drive better decisions in strategies. Some of the top things that I love to focus on is precision and recall this metric is crucial when you’re dealing with a lot of imbalanced data sets which is often the cause in sales and marketing precision tells you how many of your AI positive predictions are correct. A metric in AI especially for automating sales processes as an example is how much time is your AI saving your team this can be a huge selling point.

During a project at a retail analytics firm, we used AI to analyze customer behavior analytics and ecommerce transaction data. The challenge was ensuring data quality. We implemented rigorous preprocessing techniques and anomaly detection algorithms, which revealed hidden patterns in shopping habits and uncovered opportunities to optimize the CX based on eCommerce engagements and customer affinities and dimensions. This approach not only enhanced the model's accuracy but also provided deeper insights into customer preferences, driving targeted marketing strategies.

Data is the fuel for AI models and systems, and also the source for performance metrics. AI professionals should collect and analyze data from various sources, such as historical data, user feedback, or online experiments, to calculate and track the performance metrics of their AI solutions. They should also use data visualization tools, such as charts, graphs, or dashboards, to display and explore the metrics in an intuitive and interactive way.

In the 'Collect and Analyze' phase of AI development, here are a few essential steps: Data Preparation: This involves cleaning and organizing raw data. Key tasks include handling missing values, eliminating duplicates, and standardizing data formats to ensure accuracy and usability for AI models. Exploratory Data Analysis (EDA): Here, data sets are visually and statistically explored to uncover patterns, anomalies, and relationships. EDA informs model development by providing insights into data trends and guiding hypothesis formation. Feature Engineering: A critical and creative step, it transforms raw data into features that effectively represent the problem for predictive models.

IMO metrics that incorporate - recall, speed, scalability, robustness eventually matter the most. Recall measures the ratio of true positives to all actual positives (minimizing false negatives). Speed/scalability helps gauge how efficiently your model performs and adapts to larger data volumes, essential for performance. Robustness helps assess how well the model generalizes to unseen data and copes with noise or errors.

Ensure data quality by using reliable sources and robust preprocessing techniques. Analyze the data using statistical and visual methods to identify patterns and anomalies. For instance, in retail analytics, preprocessing and anomaly detection can reveal insights into customer behavior, enhancing model accuracy and marketing strategies.

While working on an AI-powered chatbot, we benchmarked its performance against industry standards and competitor products. This comparison revealed a gap in understanding informal language, leading us to integrate a more diverse natural language datasets in their LLMs. The result was a 20% improvement in user engagement, demonstrating the value of benchmarking in identifying and addressing performance gaps as well as hyper-personalization of customer engagements and support.

Vor allem ist hier der Vergleich zur überprüfung der Richtigkeit notwendig. Wenn wir anfangen uns jetzt schon auf die KI zu verlassen, da sie vermeintlich gute Ergebnisse liefert, heisst das nicht, dass sie, die KI, immer richtig liegt. Gerade aktuell ist das mit den Fakten noch so eine Sache. gerade dann wenn ich mit der nativen KI arbeite (bspw. chatGPT und der Advanced Data Analytics). Da ist vorsicht geboten. H?ufig bekommt man auch bei zweifacher Ausführung unterschiedliche Ergebnisse.

Performance metrics can help AI professionals to compare and benchmark their AI solutions against different criteria, such as baseline models, competitors, or industry standards. This can help them to identify the strengths and weaknesses of their AI solutions, as well as to discover new opportunities and challenges. For example, at Lyft, AI engineers used performance metrics to compare different algorithms for matching drivers and riders, and found that optimizing conversion rates led to more ride requests and revenue.

Benchmark your AI's performance against industry standards, competitors, and previous results. This helps identify performance gaps and areas for improvement. For example, enhancing a chatbot's understanding of informal language through diverse dataset integration can significantly improve user engagement.

To find standard metrics for a Chatbot, explore industry benchmarks, and refer to reputable sources like customer service associations, industry compliance and SLA for similar organizations. Compare your performance metrics against criteria such as accuracy, response time, and user satisfaction. Benchmark by assessing how your metrics align with established industry standards, ensuring your Chatbot meets or exceeds common expectations. Regularly review and adapt your metrics to stay in sync with evolving industry norms and user needs.

You should retrain the model with different cuts of test data vs training data to validate the accuracy of the model. This cross-validation is very critical and if you see huge swings in results you have to analyze the data for anomalies. Remember 60-80% of project effort will be spent on acquiring and cleansing the data. Collect historical data to compare predicted with actual results. Over time you should see improvements as you become more experienced. This KPI is the ultimate success indicator. User testing is also very useful and more critical if you create brand new use cases and models.

Performance metrics can help AI professionals to test and validate their AI solutions before and after deployment, and to ensure that they meet the quality and reliability requirements. They can also use performance metrics to conduct experiments, such as A/B testing or multivariate testing, to evaluate the impact of different variations of their AI solutions on the outcomes and user behavior5. For example, at Tokopedia, AI analysts used performance metrics to test different AI models for detecting fraud and improving customer trust.

Use methods like cross-validation, split testing, and user testing to evaluate performance in various scenarios. Different evaluation criteria, such as error rates and confidence intervals, provide comprehensive performance insights. Retraining models with varied data and conducting A/B testing are critical for validation.

Test and validate thoroughly pre-project, during UAT, and with the MVP to confirm alignment with goals and user expectations, ensuring a correct path.

Craft a visually engaging dashboard, featuring key performance metrics, to effectively communicate project progress and outcomes to diverse stakeholders and audiences. Navigate metric trade-offs by prioritizing key objectives, understanding interdependencies, and considering broader project goals.

Report and communicate. Performance metrics can help AI professionals to report and communicate the results and value of their AI solutions to various stakeholders and customers, such as managers, investors, regulators, or end-users. They can use performance metrics to demonstrate the benefits and risks of their AI solutions, such as how they improve efficiency, accuracy, or customer satisfaction, or how they address ethical or legal issues. They can also use performance metrics to solicit feedback and suggestions from stakeholders and customers, and to incorporate them into their AI solutions.

Present performance metrics to stakeholders clearly and concisely. Use visualizations and emphasize key findings, limitations, and recommendations. Tailor reports to stakeholder needs, focusing on the most relevant information.

Entscheidend ist hier, dass wie mit jedem Bericht, die für die jeweiligen Stakeholder relevanten Zahlen in der für sie wirksamen Sichtweise bereit gestellt werden. Weniger ist hier bekanntlich mehr. nur weil ich auf einmal mit KI Zahlen auswerten und diese aufbereiten kann, heisst es nicht, dass ich diese Flut an Informationen ungefiltert bereit stellen muss.

Don’t make AI metrics complex or opaque. Effective reporting and communication are crucial for building trust and engagement. 1. Know your audience: Tailor your message to their level. Avoid jargon and focus on the "why". 2. Choose the right metrics:Don't overwhelm with data. Select only a few key metrics and explain why. 3. Visualize data effectively: Create clear charts and graphs to make your point understandable at a glance. 4. Tell a story: Don't just present numbers. Use data to tell a compelling story about the impact of AI on the business. 5. Be transparent: Acknowledge limitations and biases in AI models. Bonus: Use interactive dashboards and reports to allow deeper exploration and engagement with the data.

Kontinuierliche Verbesserung ist hier der Schritt ... und zwar in beiderlei Richtung. Die KI darf auch lernen die richtigen Zahlen zu interpretieren und die Rückschlüsse auch zu belegen. Denn die Haluzination und das Erfinden von Fakten bei Unsicherheit ist noch ein bekanntes übel. Daher ist die überprüfung und Nachjustierung entscheidend und aktuell noch unabdingbar.

Use performance metrics as feedback for continuous improvement. Identify and prioritize improvement areas and opportunities for enhancing performance. Implement changes and monitor their impact, aiming for constant progression.

To get better, use your performance feedback. Figure out what needs improvement, focus on those areas, and look for chances to do better. Try making changes, keep an eye on how they affect your performance, and see the results.

Learning and improving on AI metrics requires a multi-pronged approach: 1. Open feedback channels and incentivize feedback. 2. Go to outside stakeholders for feedback not just your internal team. Otherwise you will get group think and have blind spots. 3. Ask a lot of people a lot of questions about your AI metrics - ask them what you don’t know and should be thinking about. Ask “tell me something you think I don’t want to hear.” 4. Aim Fire Aim Fire. Don’t have paralysis by analysis or go for perfect. Aim in the direction you want to land and take the shot - then learn from feedback - re aim and fire again. Do this 20 times - trust me. This is simple and impactful approach to learning how to improve your AI initiatives and metrics.

Performance metrics guide AI professionals, develop model strengths/weaknesses. Metrics like accuracy, precision, recall enable fine-tuning, identifying areas needing improvement. Continuous monitoring helps in maintenance, ensuring sustained model effectiveness. Clear communication of metrics drives understanding with stakeholders, helping derive informed decisions. Metrics serve as important tools, facilitating iterative enhancement and optimization of AI models across diverse applications.

?? The key word is *consistency*. By actively | using and interpreting | performance metrics, AI professionals can not only assess the current state of their models but also drive continuous improvement, leading to more robust and effective artificial intelligence solutions. A framework to help get organized: 1. Model Evaluation 2. Identify Model Weaknesses 2. Optimize Hyperparameters 2. Feature Engineering 3. Address Class Imbalance 4. Threshold Tuning 5. Benchmarking 6. Cross-Validation 7. Monitor Model Performance Over Time 8. Iterative Improvement 9. Understand Business Objectives 10. Communicate Results Effectively For more, please feel free to follow me-glad to connect ????

With the right data, ai can support professionals in identifying patterns and trends in their work habits using performance metrics. It can also uncover areas of weaknesses which are more difficult to notice.

From a different perspective, in addition to performance metrics, companies should also actively encourage and prioritise the adoption of a basic ethical and regulatory framework. It’s very easy to deviate from these principles when striving for higher performance and target achievement. ? Despite the unprecedented acceleration of tech advancements, we still consider this to be an early stage. Establishing fundamental frameworks at this level ensures a smoother absorption of rules and regulations as the industry inevitably matures.