What are the most important things to remember when working with machine learning models?

In my experience, perfect data quality is an unrealistic goal for most real-world machine learning projects. While cleaning and preprocessing data is important, spending too much time chasing ideal data can delay deploying models and derail projects. I've found effective machine learning is often more about adaptability than perfect data. Simple models like random forests can handle noise and some anomalies without major accuracy impacts. I try to take a "good enough" approach to data quality - focusing on obvious issues and easy wins but not striving for perfection. I prefer to deploy models faster and expect to update them periodically as more data comes in.

I think considering the quality of the data is really important role in machine learning m. As we all know about the data trash in, trash out. Our machine learning data model might be misleading if we choose the wrong data that will lead to wrong results. Successful machine learning will be based on successful data modeling which will be based on the quality of data that we are using.

Machine learning (ML) is a rather sensitive bunny and it just doesn't handle low quality data well. The saying "junk in, junk out" is as true for ML as it is for everything else. This amplified with ML, as ML cannot handle Nulls either. There are exceptions, but these are rare. Build ML on top of your existing data prep pipelines. You will need to engage your business stakeholders to learn how nulls, and outliers should be dealt with, and upgrade your existing jobs accordingly. Be mindful that the approach to dealing with missing values can and will have a tremendous impact on the predictions of any model that you create produces

Everybody loves clean data; nobody loves cleaning. A great way to stand out is to do what others dread: - Build cleaning and quality checks into the pipeline. - Build alerts, visualisations and tools for colleagues. - Own the process so others come to you for help.

When working with machine learning models, prioritize data quality and choose the appropriate model for your problem. Be mindful of overfitting and underfitting, and use cross-validation for performance assessment. Effective feature engineering is crucial for accuracy. Ensure the model's decisions are interpretable, particularly in high-stakes scenarios. Address biases to maintain fairness and ethical standards. Continuously monitor and update the model to sustain its relevance and accuracy.

The most useful model is often the simplest one that gets the job done. Complex models like neural networks are powerful but can be overkill for many real-world problems. I try to start simple with linear regression or random forests, validate performance through cross-validation, then only increase complexity if the model is underfitting. Grid search helps balance model flexibility and overfitting risk by tuning hyperparameters. However, endlessly tuning models can delay deploying to production. At some point you have to ship a "good enough" model that meets the business need, even if you think you can eke out a bit more accuracy. After launch, collect more data then retrain models periodically.

Sometimes Logistic Regression can do that neural networks can do but at high cost, it means that you have to find the simplest models that can solve your problem, if not then move to neural networks but dont overkill with model complexity.

Choose relevant features that contribute meaningfully to the model's performance. Removing irrelevant or redundant features can improve model efficiency and interpretability.

When working with reinforcement learning (RL) models, there are a few key things to keep in mind. First, it is important to clearly define the reward function, which will guide the agent's behavior. Second, it is important to consider the exploration vs. exploitation tradeoff. And find the right balance between exploring new behaviors and exploiting the knowledge gained so far. Third, it is important to select the right learning algorithm, such as Q-learning or policy gradient. When working with unsupervised learning models, it is important to choose the right algorithm.

Having clearly defined the business problem, make sure to select the best metric to assess success in addressing that problem. For example precision or recall may be better for use in model selection than accuracy alone.

?????????? ???? ?????????????? ??????????????????????? It's the process of selecting, modifying, and creating features to improve the performance of ML models. Main steps include 1. Feature Collection ??- Gather varied and relevant data tailored to a specific problem 2. Feature Cleaning ?? - Tackle missing values, eliminate duplicates, and correct data errors 3. Feature Selection ??- Choose the most impactful features 4. Feature Transformation ??- Normalize or standardize features, applying necessary mathematical transformations 5. Feature Construction ???- Create new features by combining or altering existing data for deeper insights 6. Feature Iteration ??- Continuously refine features based on model feedback for optimal performance.

Including choosing a good transform. That is part of feature engineering. An example is our CoShNet which use a 2nd generation wavelet to transform the inout image into a rich and stable feature space.

Fancy feature engineering can overcomplicate models and delay deployment. I try to let algorithms handle feature transformations instead of hand-crafting. For example, neural networks with embeddings can automatically learn effective representations of categorical variables and text data. Tree-based methods like random forests and XGBoost can handle raw, messy data pretty well too. Of course scaling and imputing missing values is still important. But feature selection should focus on dropping redundant or irrelevant fields first, not engineering new ones. The simplest useful features are best to let the model learn relationships. Evaluate model accuracy then engineer more features if needed.

In the realm of AI, mastering feature engineering enhances model performance, while comprehending use cases ensures practical application. Additionally, assessing the significance of result loss functions is crucial for refining model outcomes. It's equally important to carefully choose and feed relevant features into machine learning models, especially in the context of computer vision, to achieve optimal results.

I Think Understanding the information content of the data with respect to the problem you are trying to solve is the most important part of the work

In general, all IA model will be consuming from API for all data product, so you need to understand how handle deploy and available new AI model in API. Some deployment strategy are: ? Canary: Version B is released to a subset of user, then proceed tp a full rollout. ? Blue/Green: Version B is side by side with version A and traffic switch to version B. ? Ramped: Version B is slowly rolled out and replace version A. ? A/B testing: Version B is released to a subset of user under specific conditions.

Evaluation is not a one-time event. We must continuously monitor model performance in real-world deployment, tracking its impact on business goals and user behavior. Some ways to get started: - Utilize Automated Monitoring tools like Prometheus, Grafana, and TensorBoard to track key metrics related to model performance, data quality, resource utilization, and user feedback. - Schedule routine audits to analyze model performance, user feedback, and business impact data in detail. - Run A/B tests to compare different model versions or configurations to see which performs best.

The following list can be kept it in mind when it comes to model evaluation: - Employ metrics such as accuracy, precision, recall, and F1 score to gauge performance comprehensively. - Implement methods like stratified sampling when splitting data into training and testing sets to maintain class distribution. - Leverage cross-validation for robustness assessment. - Keep an eye on overfitting through techniques like regularization and dropout. - Utilize confusion matrices for an analysis of classification results.

- Always interpret your model: Make sure you understand how the model works and what it is predicting. - Be aware of overfitting: Overfitting occurs when a model is too closely tailored to the training data, which can lead to inaccurate predictions. - Be aware of biases: Machine learning models can have biases that lead to inaccurate or unfair predictions.

The need and importance of model interpretation varies from one domain to other. In health care domain where one bad step could result in loss of life, the model interpretability and explainability has great importance. And so does in finance. Interpretability simply means interpreting the result that the model outputs. Explainability means understanding the mechanism of how the model made that decision. There is a trade off between interpretability and accuracy of the models. Deep learning models are black box models( since often we can’t interpret or explain their output) but provide high accuracy. Simple linear or logistic models provide better understanding but low accuracy.

Model Interpretation illuminates the inner workings of our ML models, enabling us to improve them, build trust, and avoid ethical pitfalls. Interpretation scopes are the different ways to peek inside the complex machinery of machine learning models for better understanding and control. Examples include: - Global holistic: Grasping the entire model and its behavior like a simple linear regression visualized in a graph. - Global modular: Analyzing individual components impacting the model, like feature importance revealing key influencers . - Local single-prediction: Unveiling the reasoning behind a specific prediction. - Local group-prediction: Interpreting the rationale behind a cluster of predictions.

Read up about model drift. A model is only as accurate as the data it was trained on, and the hyper parameters it was configured with. Data, and more importantly, the sentiment of the people creating it will change over time. Your model will naturally become less accurate as time goes by. This action is called Drift. You need to test for drift frequently, compare the performance of your exisiting model with a newly created one each week. Comparing the two will help you to test for drift. When the latest model out performs the existing one, this is where the likes of MLFlow can help switch the old model for the new

Ability to incrementally add new knowledge into the model is important to consider. In real-world applications, circumstances surrounding a machine learning model could change. For example, an autonomous car could run into unexpected whether conditions. In such scenarios, re-training the model from scratch is not realistic. Training the model on new data alone could lead to catastrophic forgetting of old knowledge. Smarter continual learning techniques are required.

Gone are the days of scrolling between cells in a Jupyter notebook, if you want to take it to the next level, consider a machine learning lifecycle management tool like MLFlow, CometML, Weights & Biases (W&B), Neptune.ai, etc. Store your configurations, track your experiments, and package for deployment. This is relevant for lab projects as well as professional use cases. These streamlining tools help you to keep track of relevant metrics, reducing the time it takes to iterate on a model, and ultimately deliver better more consistent results.

How handle the updating process in AI model from ingested data in data system: ? What time interval is assigned to update the features in model? ? What range of data will use to update? ? How versioning data between different models?

Of primary importance is clearly defining the problem. When building a model this helps prioritize tasks like data gathering and metric selection. If you are examining someone else's model, this can help you understand their motivations.