Wie sorgen Sie dafür, dass Ihre Machine-Learning-Modelle relevant bleiben?

In my experience we should also look into the cause for large change in metrics of the data. We need to look into the reasons like concept drift and data drift also. Concept drift: The change in the target variable of your data. Data drift : change in distribution of the data to which the model is applied

Monitoring performance - Metrics Tracking: Track key metrics like accuracy, precision, etc. These metrics provide insights into the model's predictive and classification capabilities. - Business Outcomes: Metrics could include revenue, customer satisfaction, etc. Correlate these outcomes with model changes to gauge real-world impact. - Alerts and Dashboards: Set up automated alerts and dashboards to receive notifications on performance changes. - Retraining and Fine-tuning: When metrics drop below acceptable thresholds, consider retraining or fine-tuning the model. - Model Replacement: If performance consistently declines despite retraining, consider replacing the model with a more suitable approach.

I feel there are few things which needs to be done regularly. You need to some dashboard to track the performance using metrics and at the same time you need to keep an eye on open-source community, same use-case might be achieved by fine-tuning an open-source model which might give much better result than home-grown model.

Absolutely! Monitoring model performance is crucial to ensure they stay effective. Regularly tracking metrics like accuracy, precision, recall, and business outcomes such as revenue and customer satisfaction helps us make informed decisions. Setting up alerts and dashboards keeps us proactive in addressing anomalies and maintaining model quality. This practice ensures we keep our models up-to-date through retraining or replacement when needed. #AI #MachineLearning #ModelPerformanceMonitoring

The metrics you track can differ depending on the context and the end usage. What corresponds to a good and bad outcome? Is it for the end-user really important to have low false negatives ? In that case you should use a KPI that captures that information. Yes you want to track business outcomes, but more specifically you want to align every ml model with a certain business goal such as improved retention and track the progress towards that goal. Are the model actually helping in improving retention, dau, revenue, ...? Lastly performance can degrade over time. By analyzing the error over time you might be able to identify issues ahead of time before the problem becomes so big it becomes more apparent.

Also, imposing data validation constraints before feeding data to model training can be super helpful. It can help to ensure the quality and reliability of your machine learning model. Data validation involves checking the integrity, consistency, and reliability of the data you plan to use for training. By setting up proper validation constraints, you can identify and address issues early in the process, leading to better model performance and more accurate predictions.

Here is one of those cases where actual domain expertise and significant data science experience makes the difference. It is not only to retrain including new data, it is also deciding how much of the old data should remain in the training dataset. Metrics like accuracy will give us the model performance, but also here we need to be careful with its interpretation.

I feel we should retrain the model only when there is huge change in trend and patterns as retraining is a quite expensive process be it computationally or time wise. Therefore, if there is small changes then it better to just iterate over the new data few times so that model can learn new change in the trend.

There are certain situations where retraining is essential. This should be considered during the project, like using retrain compliant models, or including automatic retraining pipelines. A practical approach is running a few batches with controlled learning rates from the current model to make quick adjustments before a major retrain. This also gives more importance to the most recent data. But retraining isn't always necessary. As the model's performance gets higher, any improvement become significantly more demanding. Adding a small percentage of data couldn't bring any enough novelty that justifies the retraining. It's important to check the impact of new data to determine if the model needs a complete retraining or just fine-tuning.

I used a form of rapid machine learning (trainable in a second or less) to optimize classification of patterns in measured results of subjects when they are using mental effort to achieve certain specific results. The effect size (fraction of correct classifications) is relatively small, which the ML increased by a factor of 2-4. To preserve generality, a database of measurements of previous efforts from multiple subjects is screened by one or more quality factors. That data is balanced so each classification uses the same number of training examples. Every real-time measurement is used with the screened previous data and new training is performed for every new data point. This approach is general, but also fine-tuned for specific subjects.

In my work with organisations, ethical considerations are becoming central. I've found that creating an ethical framework tailored to the business and societal context not only helps mitigate risks but enhances trust in the model. Transparency about how decisions are made fosters accountability, and is key to the responsible deployment of machine learning.

Preemptively designing models within the context of responsible machine learning and AI can ensure that ethical guardrails of machine learning models are adhered to. Ensuring you have addressed principals of models being fair, transparent, inclusive, reliable, safe, private and accountable can set you up for success in this regard. These are the benchmarks within most responsible AI Frameworks which are scrutinized.

Implications of a ML model are manifold. Let's take a search ranking system for instance. Proximal systems impact: Additional latency introduced & effects on SLAs. Justifiability of improved ranking against potential latency. Business metrics' impact: Noticeable improvement in user experience and efficiency in locating items. Observable improvement in conversion rates. User behaviour and societal impact: Potential creation of filter bubbles or echo chambers. Biases needing mitigation, e.g., women being frequently recommended nursing or care jobs as compared to their male peers. Thoroughly examining these aspects provides insight for deployed system's effectiveness, fairness, and impacts on user experience & societal perceptions.

The greater the potential of any ML or AI application, the greater the possibility of unanticipated results. Apparently, no one working on the recent generative AI programs ever considered that their products could do something as unexpected as "hallucinating" completely false information (a nice way of saying, they lie). When Asimov conceived and wrote about his "three laws of robotics," he never dealt with how the laws could be made real, that is, actually unbreakable by a robot. We now face a similar, but real situation with AI: how, indeed, do we compel any AI (and ML) to not only do what its designers believe is safe and ethical, but also including what humans didn't anticipate. A rather daunting challenge, which is not yet understood.

In my experience, this is the most important aspect of model training but it's equally challenging. ML teams need to have a close working relationship with the Business unit, a lot more than any other software development team. Earlier alignment, expectation management can narrow down the scope of an ML project and thus improve quality at a lower cost.

Feedback from end user is very important, but you have to stay objective and keep in your mind that they not always have deep understanding of how algorithms work inside, so you have to process all feedback and check if their suggestions can help you and your model become more accurate.

Implementing a feedback loop with end-users can not only provide valuable insights, but also give users the ability to influence the system, fostering a sense of ownership and trust. Acknowledging and acting on feedback not only improves the model but can also enhance user engagement and loyalty.

It's important to make sure that your code structure is of high quality from square one. Invest that extra hour into the code quality of your data loading, experiment tracking, and result analysis code. It will pay dividends in the future as you continue to experiment and iterate.

I agree this is key to succeed in an ML system, like it is to succeed with a new business idea in a Startup (see The Lean Startup by Eric Ries). Generative AI ( #GenAI ) and Automated ML (AutoML) will help too in speeding up this process, depending on the use-case

I agree, continuous experimentation and iteration are the heart of effective machine learning. Techniques like A/B testing, multivariate testing, and feature engineering allow us to compare models, measuring their impact and performance. Hyperparameter tuning and model selection further optimize outcomes. Embracing this iterative approach uncovers valuable insights, enabling us to seize new opportunities and devise innovative solutions. #MachineLearning #Experimentation

Iteration on models includes consistent performance analysis and hypothesis formulation. One could identify areas where the model lacks desired performance. For instance, does a forecasting model overestimate sales over particular weeks of a quarter? Does it perform poorly when new inventory is introduced? Upon reaching such a hypothesis, an experiment can be set up by training a new model on altered features/ architecture etc. and then this hypothesis can be tested in an experiment. Various hypothesis testing frameworks (A/B tests, shadow tests etc.) can be utilised to conduct such an experiment. Based on the results from the experiments, the initial hypothesis can be reformulated and thus iterated upon.

Exposing to different uses cases may broaden the applicability of the models. One thing that I feel that is not in the bullet point list is that multimodality is the new trend. So for a model to be relevant it must embrace different inputs.

To keep your ML models relevant, you can follow these steps: 1. **Regular Updates**: Continuously update your models with new data to adapt to changing patterns and trends. 2. **Monitoring and Maintenance**: Regularly monitor model performance and make necessary adjustments to ensure it remains accurate over time. 3. **Retraining**: Periodically retrain your models using the latest data to capture evolving patterns and behaviors. 4. **Feedback Loop**: Incorporate user feedback to identify areas of improvement and address any issues. 5. **Future-Proofing**: Design your models with flexibility in mind, so they can be easily adapted to changes in data or requirements.

While I see other comments about improving the model performance by monitoring and using further data, I believe there is much more than that. ML moves so fast these days, that if we are not up to date, our system becomes obsolete. Successful business models break completely in matter of days and new technologies like #GenerativeAI (#GenAI) speed up processes including Machine Learning cycles tremendously. I love the recommendations and I would highlight joining communities and become active instead of just consuming material. Showing and teaching others is the best way to truly learn

AbsolStaying current in the rapidly evolving machine learning landscape is paramount. Continuous learning through books, articles, webinars, and networking with fellow practitioners keeps us abreast of trends and innovations. Engaging with the community by sharing knowledge fosters growth and addresses challenges. This commitment to learning not only hones skills but also propels us to lead in this dynamic field.

Semantic Layer: Focusing on learning resources and opportunities that enhance domain expertise and business understanding. Domain-Specific Needs: Staying up to date with industry trends, regulations, and innovations. Customer Needs: Learning from customer interactions, feedback, and trends to continually align the models with their evolving needs.

Machine learning is a rapidly evolving field. To keep your skills current and models relevant, you need to continuously learn and grow. - Read books, blogs, and research papers on the latest ML advancements. - Follow ML thought leaders on social media. - Implement experimental models and techniques on test datasets. - Network with other ML practitioners. - Consider getting ML certifications to validate and advance your expertise. - Teach and mentor others to further strengthen your own skills. - Keep an open and growth mindset.

One additional aspect I'd emphasise is the importance of collaboration across disciplines. Working closely with domain experts, whether in finance, healthcare, or manufacturing, often unlocks deeper insights and ensures that the models are truly aligned with real-world complexities. Embracing interdisciplinary collaboration enriches the machine learning practice.

In many manufacturing industries, continuous evaluation of models is essential due to seasonal changes, such as the transition between winter and summer, which can alter the ground truth of the production process. Even if parts are produced in compliance, adjustments are needed. Therefore, it is vital to record production data throughout the year to enable ongoing model fine-tuning, ensuring alignment with the variable manufacturing environment.

General monitoring best practices - If it’s possible, don’t give the application’s “monitoring power” to one person. - Take a lean approach; using too many tools can be very tasking. - Monitoring doesn’t start after deployment, it starts when you begin experimentation. - Always consider what’s optimal for the productivity of your team when you encounter any crucial decision-making point. - Encourage your team to properly document their troubleshooting framework and create a framework for going from alerting to action to troubleshooting for effective model maintenance.

Ok , lets think of your machine learning model as a recipe for your favorite meal. When you first create the recipe and follow it carefully, you get a tasty dish. But as time goes on, ingredients may change, or you might want to try new things for a different flavor. To keep your dish delicious, you update your recipe. Similarly, to keep your machine learning models working well, you need to regularly check your data, make changes when needed, stay updated, and listen to feedback and goals that may change. Just like tweaking your recipe keeps your meal yummy, adjusting your models keeps them accurate and helpful..