What are the most effective ways to monitor an AI project after deployment?

Vou sugerir aqui algumas métricas básicas que considero Importantes para o monitoramento pós implantação da maioria dos projetos de IA. Tempo de resposta: o tempo que o modelo de IA leva para fornecer o resultado. Uso de recursos: a quantidade de memória, processamento e armazenamento necessários para o modelo de IA. Precisão: a porcentagem de vezes que o modelo de IA fornece um resultado correto. Recall: a porcentagem de vezes que o modelo de IA identifica corretamente um resultado positivo. Satisfação do cliente: o grau de satisfação dos clientes com o desempenho do seu modelo de IA.

Success is in the eye of the customer - so make sure your metrics reflect a business outcome. For example I'm involved in training an ML model to identify time-sensitive messages coming in to a health system from a patient portal in order to reduce the response time for these messages. Make sure your primary metric is response time. It's easy to get lost in accuracy, AUC, recall, precision, sensitivity, specificity, PPV, etc. and all the ML metrics and forget the original purpose and measurement of that.

Important metrics for traction: 1. Accuracy: This metric measures how often the AI system makes correct predictions or decisions. 2. Precision and Recall: Precision measures the correctness of positive predictions, while recall measures the system's ability to find all relevant cases. 3. Speed and Latency: These metrics assess how quickly the AI system responds or processes data. 4. User Satisfaction: Often gauged through surveys, feedback, and usage metrics, this assesses how well the AI meets user needs and expectations. 5. Business Impact: Metrics such as return on investment (ROI), cost savings, and revenue generation are essential to evaluate the AI's contribution to business goals.

Not only should the success metrics be clear when an AI model is deployed, but there should also be a process to determine when an AI model is outputting unexpected results. Unexpected results indicate that an AI model is not performing properly and that there may be a problem with the parameters in the model or the data ingested into the model.

6 years, countless lines of code, and enough pixels to fill a galaxy. That's what it took me to be writing this for you. What's your AI supposed to achieve? Is the first question to ask yourself. Know what wins look like before you start: Define success metrics for accuracy, speed, and impact. To add to it, check for scalability. We would never want to overpromise and under deliver at scale. Without them, we're flying blind, treating a cough as a cure. So, let's define these benchmarks clearly, like a roadmap to success, and keep our AI on track. Remember, a champion athlete needs well-defined goals, and our AI is no different. It needs to know what it's playing for.

Data and feedback can be gathered through various factors outlined below: - User Surveys - In-App Feedback Mechanisms - User Interviews - Social Media Monitoring - Online Analytics Tools - Customer Support Channels - A/B Testing - Feedback Forms on Website - Usage Analytics - Email Surveys - Community Forums - App Store/Platform Reviews - Partnership Feedback

Effective data and feedback collection for an AI project involves diverse strategies. User feedback surveys and usage analytics provide direct insights into user experience and behavior. Monitoring performance metrics like accuracy and response times helps identify technical improvements. In depth methods such as interviews, focus groups, and support ticket analysis offer qualitative insights. Additionally, regular stakeholder meetings and A/B testing facilitate ongoing evaluation and adaptation, ensuring the AI system remains aligned with user needs and operational goals.

From my experience, collecting feedback and data is vital for monitoring your AI project. It’s important to gather information from different sources like surveys, user reviews, ratings, comments, support tickets, and usage logs. This feedback helps understand how users perceive and interact with your AI, and how it impacts their behavior and satisfaction. It’s also crucial for identifying any problems, mistakes, or biases in your AI system, allowing you to make necessary improvements quickly.

At our AI software startup, effective monitoring post-deployment heavily relies on systematically collecting and analyzing feedback and data from users and stakeholders. Utilizing diverse channels, such as user surveys, app reviews, and direct feedback, we gain valuable insights into how our AI solutions are perceived and used in real-world scenarios. This approach is complemented by real-time analytics, providing us with immediate data on user interactions, application performance, and potential issues. Crucially, we don't just gather feedback; we integrate it into our development cycles, ensuring that user insights directly inform enhancements and updates.

Gathering comprehensive feedback and data is crucial for optimizing your AI project. Utilize surveys, reviews, ratings, and comments to gauge user satisfaction and identify areas for improvement. Analyze support tickets to address issues promptly, ensuring a seamless user experience. Leverage usage logs and analytics to track user behavior, measure engagement, and identify popular features. This multifaceted approach allows you to uncover any potential biases or errors, fostering transparency and trust. Actively listen to stakeholders to align your AI project with user expectations, ultimately enhancing its overall effectiveness. Regularly collect, analyze, and adapt based on feedback to continuously refine and evolve your AI project.

Trago aqui algumas ferramentas que você pode usar para trabalhar com registros e logs para projetos de IA: ELK Stack: é uma coleção de ferramentas de código aberto para coletar, processar e analisar dados de logs. CloudWatch: é um serviço da Amazon Web Services que fornece monitoramento e análise de logs. Azure Monitor: é um serviço da Microsoft que fornece monitoramento e análise de logs. Aqui temos ferramentas pagas então você pode escolher a ferramenta que melhor atende às suas necessidades e orçamento. Lembrando que são apenas sugestões.

From my own experience, setting up logging and alerting in your AI project is a game-changer. It’s like having a vigilant watchdog that keeps an eye on everything happening within your AI system. These tools jot down every important event, catch errors, and flag anything out of the ordinary. This is super helpful for keeping your AI reliable and secure, and if something does go wrong, you’ll know right away and can jump into action. Plus, it’s great for looking back at what your AI did and why, ensuring everything’s above board and transparent.

Set up comprehensive logging systems to record the AI's decisions, actions, and any errors or anomalies. This is vital for troubleshooting and understanding how the AI is functioning in real-world scenarios. Implement alerting mechanisms to notify relevant stakeholders of critical issues or significant changes in performance metrics.

Implement logging and alerting mechanisms in your AI project to record and notify events, errors, or anomalies. This helps maintain reliability, security, and availability, allowing quick troubleshooting of problems. Logging and alerting also support auditing, tracing actions, and ensuring accountability and transparency.

Obviously a very important process for us but here are some things our engineers do: --Comprehensive Logging Systems: We integrate extensive logging mechanisms within our AI applications. These systems meticulously record various events, transactions, and anomalies.. --Proactive Alerting Mechanisms: Alongside logging, we have established proactive alerting systems. These are designed to instantly notify our team of any critical events or discrepancies, facilitating swift response and minimizing potential disruptions. --Error Tracking and Resolution: The logging data significantly aids in error tracking. It helps our engineers to quickly identify, diagnose, and resolve issues, ensuring high levels of reliability and performance.

Se os dados indicarem que o projeto não está atingindo suas metas, é importante tomar medidas corretivas. É comum que com o tempo existam ajustes nos dados de treinamento, alterações no modelo de IA ou melhorias nos processos de negócio para atender as expectativas de qualidade eu de experiência do cliente final.

From my experience, keeping your AI project up-to-date and regularly retrained is crucial. AI isn’t something you can set and forget; it needs to evolve with changing data, user expectations, and business objectives. Regular updates and retraining help maintain its performance and accuracy, ensuring it stays relevant and effective. This approach also lets you add new features or adjust to new requirements that might surface from user feedback or market trends. Essentially, it keeps your AI fresh and aligned with your goals.

--Adapt to Evolving Data: AI systems often deal with evolving data. Regular retraining helps us adapt these systems to changing patterns, ensuring they remain effective. --Enhance Performance and Accuracy: Incredibly important, continuous updates and retraining allow for the refinement of algorithms, leading to enhanced performance and accuracy. --Prevent Model Drift: Over time, AI models can drift from their initial accuracy. Regular retraining counters this drift by updating our models with current data trends. --Incorporate User Feedback: To us, user feedback is a goldmine for improvement. Incorporating insights from feedback into the AI system through updates ensures that the system evolves in line with our user expectations and needs.

AI models can become outdated as data and environments change. Regular updates and retraining with new data are essential to maintain accuracy and relevance. Develop a schedule for periodic reviews and updates, depending on how quickly your domain or data changes.

Updating and retraining an AI project involves incorporating new data, refining algorithms, or adjusting model parameters to enhance performance or adapt to changing circumstances. It includes evaluating model performance, identifying weaknesses or shifts in data distributions, and iteratively refining the model through retraining. This iterative process ensures AI systems remain accurate, relevant, and effective over time, accommodating evolving requirements or environments.

É importante coletar dados consistentes e que tenham relação com a ética do seu modelo. Os seus dados coletados devem incluir informações sobre o impacto do projeto nos indivíduos que o usam e na sociedade de um modo geral. Esses dados podem ser coletados de várias fontes, incluindo feedback dos usuários, pesquisas e análise de mídias sociais por exemplo. Sobre indicadores recomendo avaliar : Transparência, Responsabilidade e Privacidade.

With great power comes great responsibility. Just like any powerful tool, our AI needs an ethical compass. Zoom Out, See the Big Picture: Your AI isn't operating in a vacuum. It's part of a complex ecosystem, and its success must be measured by its contribution to the collective well-being. By proactively assessing its societal footprint, you can ensure your AI not only performs, but also transforms for the better, leaving a positive and lasting impact on the world around it. Our AI is dealing with real people, not lab rats. We need to ensure it's unbiased, respects privacy, and plays by the regulations. Think of it as the Hippocratic Oath for AI – Do no harm (to data, privacy, or reputations).

Continuously assess the societal and ethical impact of your AI project. Ensure that it adheres to ethical guidelines and does not unintentionally harm or disadvantage certain groups. Conduct impact assessments to understand the broader implications of your AI, including potential biases, privacy issues, and long-term effects on users and society.

Ethics is central in AI implementations. You have to constantly be considering whether the data, the model that you're using, and the impact that it has on your business or surrounding industry has ethical ramifications. In many ways, AI is a black box, even top researchers don't know how it manages to give its outputs, and remains a major area of study. For that reason, you may not know what kind of output it may give to your team or users. This consideration alone is massive. You're potentially deploying a tool that you don't know how it will work. Not to mention the larger implications of whether or not deploying the AI you're attempting to deploy is aligned with your values, principles, and mission

Despite initial ethical considerations, AI systems can evolve in unexpected ways, particularly when interacting with diverse and dynamic real-world data. Implementing mechanisms for detecting and addressing biases, especially those that might affect underrepresented or vulnerable groups, is crucial. This process involves not just technical adjustments but also engaging with a diverse range of stakeholders, including ethicists and representatives of affected communities, to gain a broader perspective on the AI's impact. Regular audits by independent third parties can also provide an objective view of the AI system. This holistic approach ensures the AI project evolves in complex social landscapes.

Once deployed, healthcare AI models are often "locked" to meet regulatory requirements, ensuring consistency but limiting their adaptation to the dynamic healthcare environment. This can lead to decreased effectiveness as patient demographics and disease patterns change. Addressing this issue requires dedicated units within healthcare facilities for regular monitoring and updating of AI models, using statistical process control tools. This ensures that AI models adapt to the changing healthcare environment while remaining safe and compliant with regulatory standards. Collaboration among various professionals is key to maintaining the efficacy and safety of AI in healthcare.

A metric that is often overlooked is "bias drift" i.e. the possibility that your model will behave in a more biased way against protected groups after launch due to a change in the data landscape. Make sure to use either open source or commercial software (e.g. Watson OpenScale) to set bias thresholds that - similarly to accuracy thresholds - will alert to unwanted drift using counterfactual analysis or similar methods.

AI projects will require re-calibration (re-training) or an entire revamp of models owing to changing data, market trends etc. Also, given the pace of research and the introduction of newer tools/packages every day, keeping pace or upgrading to optimal tools/techniques is an important practice. Also, I think one important factor is to balance the ROI for any project, be it AI or not. In the end, given the magnitude of changes that keep happening in the AI world (including workforce and skillset), it's always important to keep the processes/pipelines being developed simple and consistent enough to follow through.

Incorporate interdisciplinary insights from fields like psychology and sociology to understand and improve the interaction between AI systems and their human users.

Scalability and Performance: Monitor and optimize for scalability and performance, ensuring that the AI system can handle increased loads or changing usage patterns. Legal and Regulatory Compliance: Keep abreast of and comply with relevant legal and regulatory requirements, which can evolve rapidly in the field of AI. Security Monitoring: Protect the AI system from security threats. Regularly update security protocols and monitor for any breaches or vulnerabilities.