What are the best practices for ensuring robust ML models?

To make your machine learning models solid and safe, do these things: 1. Know your goal: Pick the proper math method that fits your goal. 2. Check your data: Ensure the information you use to train the model is sound and covers your needs. 3. Keep it simple: Use techniques like 'regularization' to avoid complicating the model. 4. Understand your model: Use visuals to help see what the model is doing. 5. Test and watch: Use different ways to check if your model is working correctly and monitor it.

The best practices of picking the robust ML Model depends on the problem and business use case of your organization. These are the key steps to identify the best or Robust ML model 1. Organization vision/mission 2. Identify the business use cases 3. Resources availability 4. In house expertise 5. Scalability of the model 6. Ethical and legal constraints with the model 7. Support of the model when deployed into production 8. Availability of model

Feature Engineering Matters: Crafting informative features can be as important as choosing the right algorithm. Invest time in feature engineering to extract meaningful insights from your data. This step often requires domain expertise and creativity. Effective feature engineering can significantly impact the performance of your ML models. It can lead to faster training times, better model generalization, and ultimately, more accurate predictions. However, it's important to note that feature engineering is often an iterative process. You may need to experiment with different feature engineering techniques and continuously evaluate their impact on your model's performance to find the best set of features for your specific problem.

Feature engineering is the first step in making sure your machine learning models work well. Many ML engineers focus on picking and using models, but they often overlook the importance of feature engineering. When you do good feature engineering, you've already done half the work. Also, it's helpful to understand the math behind machine learning models to make them robust. For example, in classification problems, if you follow certain math rules like independence assumptions, you can get the best results with the Bayes Classifier.

Work with cross-functional stakeholders on the problem definition. As they say "Start with the problem, not the solution/tool". Once you are clear on a problem and convinced that it is solvable through ML, define the scientific approach, data needs, evaluation criteria, success criteria, deployment strategy, monitoring, and maintenance. Work through this incrementally and not all at once as ML models do not go past the prototyping stage sometimes due to their experimental nature and it's not a worthy use of time to write exhaustive design documents in that case. Also, I have not seen many contributions about automating the ML flow once the models are in production. This ensures that models can be kept up to date with minimal supervision

Feature engineering is critical for models to perform robustly across settings. Scrutinize whether the correlations between the features and the target label are expected to be stable across training and deployment settings. Leverage domain expertise to verify whether the features used are causally related to the target label.

Prevent data leakage: Ensure point-in-time correctness for feature data so the ML models are operating in production on the same data assumptions they were built upon. Work with your data engineering teams to assess what's available and collaborate to build robust feature pipelines. Building a robust feature store is a major topic in and of itself so I will leave the details to a later post.

Data preprocessing and exploration play a crucial role in building robust and reliable ML models. Just as an artist prepares a canvas before painting, you must ensure your data is clean, consistent, and truly reflective of your problem domain. Addressing missing values, outliers, duplicates, noise, and formatting issues through preprocessing steps helps refine your dataset. It's like polishing rough diamonds to reveal their brilliance. Furthermore, exploring your data using descriptive statistics, visualizations, and feature engineering techniques empowers you to delve deep into its intricate patterns, relationships, and potential biases. Thus, you enhance the quality, relevance, and reliability of your data.

It is important to create reusable data assets or feature stores that can be reused again and again. This also ensures that when we are doing model monitoring or retraining we have a reliable source that is consistent throughout the time. Data drift can also be a cause of model performance dropping and feature stores with previous history make it easier to analyze the said problems.

Feature engineering is one of the most crucial steps in developing an effective ML model. Simple and intuitive ML models can be deployed with features meaningful to the objective. The right set of features will significantly reduce the time required to hit the market which otherwise would be spent on hyper parameter tuning.

Adding to the methods of training and testing ML models, an effective strategy I've employed involves a staged approach to Bayesian optimization. By grouping hyperparameters into categories like learning + regularization, learning + complexity, and complexity + regularization, I optimize in rounds, typically focusing on 3 to 4 parameters at a time. After these specific rounds, a final optimization is performed using the most impactful hyperparameters from all previous stages. In my experience, this process rarely requires more than four rounds, making it efficient while often leading to robust models.

Some datasets can have intrinsic structure which must be taken into account when creating training, validation and test sets. Medical data for example can have several data points belonging to the same patient - and thus the split should be done at patient level, and not at the particular exam or image. Otherwise it is a direct way to a vice and hard to detect overfitting.

In the realm of medical data, ensuring distinct entities within training and testing sets is paramount. By segregating individuals, models thwart superficial shortcuts, prioritizing diagnostic accuracy over individual familiarity. For time-series analysis, particularly in financial domains, it's prudent to earmark recent temporal segments as test sets, thus mirroring prospective real-world applications. These delineated partitions offer a more authentic performance appraisal, sidestepping the pitfalls of over-optimistic backtests and ensuring true model generalizability.

When working with time series data, make sure to use TimeSeries splitting instead of k-fold cross validation. K-fold cross validation can result in data leakage in time series. Also, when taking averages, counts, or totals, it's important to be mindful of doing these operations keeping the potential of data leakage these operations could cause

In a machine learning project, it's essential to split and validate your data effectively: Data Splitting: Divide your dataset into training, validation, and test sets for different purposes: model parameter fitting, hyperparameter tuning, and generalization assessment. Sampling Method: Use random and stratified sampling to ensure each subset accurately represents your data, preventing bias and ensuring diversity. Cross-Validation: Implement techniques like k-fold cross-validation to avoid overfitting and underfitting, providing a robust estimate of your model's performance on different data subsets. These steps are crucial for reliable model development and evaluation.

Insights on best practices - Algorithm Selection: Choose an ML algorithm that aligns with your problem's characteristics. - Hyperparameter Tuning: Optimize hyperparameters (e.g., learning rate, regularization) to enhance model performance. Techniques like grid search or random search can help. - Optimization Method: Select an efficient optimization method (e.g., SGD) to find optimal model parameters while avoiding convergence issues. - Monitoring: Continuously monitor the training process using accuracy metrics to identify issues like overfitting or underfitting. - Regularization: Apply regularization techniques (e.g., L1 or L2 regularization) to prevent overfitting and improve model robustness.

To make a robust ML Model depends on the problem and the use case but trying out the following methods can be of help: By using a robust error metric i.e Switching from mean squared error to mean absolute difference reduces the influence of outliers. Try to cap your data at some threshold artificially You can use a model that's resistant to outliers when performing a statistical test, try a non-parametric test instead of a parametric one.

To train and optimize your machine learning model: Algorithm Selection: Choose the most suitable ML algorithm considering factors like complexity, scalability, interpretability, and robustness, aligning it with your specific problem and goals. Optimization Method: Utilize an efficient optimization technique to fine-tune model parameters and hyperparameters while avoiding local minima and saddle points. Monitoring: Continuously monitor the training process, assessing performance through learning curves, loss functions, and validation metrics. Adjust factors like learning rates, regularization, and early stopping as needed to achieve optimal results for your problem.

When starting to train your ML model, it's best to keep it simple. You can begin with a sample data to gain a basic understanding. For structured problems, AutoML tools can speed up the process, while established architectures can be helpful for complex models like those in deep learning. Sometimes, simple algorithms like tree-based models can provide valuable insights, e.g., feature importance or decision boundaries. Keeping track of the training process by checkpointing and monitoring progress is essential. At first, a default setup is a reliable foundation. Once familiar with the model's behaviour, you can optimize it by adjusting parameters for better performance. Step by step approach promotes both clarity and efficiency.

In my experience, the best thing is to start with a simple configuration. Then, based on tools such as learning curves, you can diagnose any problems and act accordingly. It is also important to keep track of the tests you run so that you understand their effects. It can also be useful to build on similar work and start with that configuration of parameters.

Quality Data: Begin with clean, structured data, addressing missing values and outliers. Data Splitting: Divide data into train, validation, and test sets. Use validation data for tuning. Metrics: Choose relevant evaluation metrics (e.g., accuracy, precision) tailored to your problem. Cross-Validation: Employ it for model stability and to prevent overfitting. Feature Engineering: Create informative features and leverage domain knowledge. Hyperparameter Tuning: Optimize hyperparameters with methods like grid search, avoiding overfitting to the validation set. Regularization: Employ techniques (e.g., L1, L2) to prevent overfitting. Interpretability: Use tools (e.g., SHAP values) to comprehend predictions.

One thing I found invaluable is the nuanced evaluation using specific data slices/segments, for example, accent, age, ethnicity, region and so on. This approach offers a more detailed insight compared to the conventional train, validation, and test evaluations. Traditional methods often overlook subtle biases or disparities, potentially leading to models that don't generalize well across diverse populations or inadvertently perpetuate systemic biases. By assessing these granular slices, we can ensure broader model fairness and understand potential biases more effectively. If you don't currently have such a robust benchmark in place, it's worth considering creating one to elevate the quality and fairness of your assessments.

When evaluating and interpreting your machine learning model: Use relevant quantitative metrics and qualitative methods to assess its performance and behavior. Compare results with benchmarks to gauge effectiveness. Analyze errors and biases to identify areas for improvement. This comprehensive evaluation process ensures a thorough understanding of your model's strengths and weaknesses, enabling informed decision-making and continuous performance enhancement.

While ML models often emphasize accuracy metrics, they can miss industry-specific objectives. In finance, a high-accuracy model might overlook key factors like returns or volatility. It's like a weather prediction being right about rain but missing its intensity. Beyond standard metrics like precision or recall, it's crucial to consider domain-specific evaluations. For instance, in finance, understanding a model's impact on investment returns or its behavior under market volatility is vital. Tailoring evaluations ensures models align with overarching business goals.

When evaluating the performance of an ML model, it's important to align metrics with business objectives. For example, suppose an e-commerce site wants to reduce cart abandonment rates. In that case, the "prediction accuracy of at-risk customers" is a critical metric rather than just accuracy overall. Start by comparing your model's output to a baseline to measure the incremental value it provides. Tools like feature importance can help identify variables that may introduce bias. It's also important to consider operational factors such as prediction latency, hardware requirements, and ease of integration to ensure the model is accurate and feasible. These comprehensive evaluations lay the groundwork for successful deployment.

Invest in robust MLFlow/ MLOps pipelines that can automate the tasks of monitoring, training data curation, feedback loop, training, and deployment of updated models. Ensure feedback loops are not introducing an unwanted bias (Ex: Interaction bias of not shown recommendations) into the system and correct/balance them as needed.

Deploying and monitoring an ML model requires converting it into a production-ready format and documenting its specifications and usage instructions. Compliance with security and privacy standards is crucial. Monitoring performance, quality, and reliability using metrics, logs, and feedback mechanisms is necessary. Regular updates to account for environmental and user changes are essential for maintaining relevance and reliability. This process ensures the creation of ML models that deliver exceptional performance while upholding ethical considerations.

Deploying an ML model goes beyond mere activation; it integrates within a broader system. Define the model's role: does it give raw predictions or need post-processing? Decide if it should operate in real-time or in batches. Choose between cloud or on-premise solutions as per your requirements. Pinpoint critical metrics for monitoring performance and establish protocols for error logging. Set maxima, highlighting latency expectations and max request capacities. Plan with adaptability in mind, facilitating effortless model updates. Recognizing the overarching system ensures your model performs seamlessly and aligns with adjacent components.

If the deployment environment allows, input and output monitors can be applied, especially for safety critical scenarios. The input monitors can be Out Of Distribution (OOD) detectors that make sure that the input to the ML model has close features to the input in the training set. This ensures that the ML model is making prediction within its ability. The output monitors can keep track of the confidence of the model prediction. If the confidence is low, refuse to predict or send a reminder to the model user.

Monitoring models and data is super important, especially in industries where drift can have dire consequences. In healthcare, outdated models could miss vital diagnoses. The financial sector risks substantial losses with even slight inaccuracies. Manufacturing might face overproduction or shortages, while in transportation, especially aviation and autonomous vehicles, safety can be compromised. Constantly updating and recalibrating models isn't just about efficiency—it's often about safeguarding assets, reputation, and lives.

One crucial feature is data augmentation, and it's arguably one of the most fun parts of the job! To make your model robust, think of every crazy way in which your data could be transformed, corrupted, truncated or mixed while still preserving the same label. For images, look at rotations, flips, histogram balance, perspective, noise, JPEG compression, occlusion, hue shift, contrast, motion blur, perlin noise shadows, etc. Find all these transformations that are destructive in terms of input data but not in terms of label: don't change the color of a lemon ?? because it could then look like an orange ??! Then take these transformations and randomly apply them throughout training, this will help you model be robust to them!

Know your Audience. All clients/stakeholders are at the different level of maturity when it comes to understanding the output/findings. Make sure this is accounted for to keep the discussion engaging for all participants.

Feedback Loop: Establish a feedback loop with end-users and stakeholders to collect input and continuously improve the model.

Watch out for the data distributions! They keep changing. Keep updating your model. Model performance monitoring and retraining. Reinforcement Learning is helpful in such trainings. At the end, model building is not an one time affair!

Deploying an ML model isn't the final destination; it marks the start of an iterative process. ML models exist in a dynamic environment, warranting consistent monitoring, validation, and adaptability to ever-changing conditions, be it data shifts, hardware upgrades, or updates to essential components. Always remember that the first launch is merely the beginning. A continuous commitment to refinement follows, ensuring the model remains relevant and efficient in an evolving landscape. Embrace this journey, as it's where true innovation and adaptability shine.