How do you select the right AI and ML techniques for your data?

In my experience Since AI is still in its early adoption stages choosing the right techniques for your data would be an iterative process that involves experimentation, feedback, continuous improvement and development based on the specific characteristics of your problem and data. Firstly, you need to understand your problem in order to identify what it is that you’re looking to solve and achieve. Secondly, you have Consider your available resources and this would depend on the size of your dataset and computational resources available. It would be more efficient to choose algorithms that can handle your data efficiently.

?? Kickstart your AI and ML journey with precision! I'd stress the essence of problem definition in the AI and ML landscape. Initiating with a clear-cut understanding of the problem leads to tailored solutions. While techniques like supervised learning or deep learning might buzz with popularity, always align your choices with the nuances of your dataset. Dive deep into data analytics, but never overlook your methods' speed, accuracy, or explainability. The magic lies in aligning techniques with organizational objectives seamlessly. Could you let me know why you're doing this? Because AI's true potential emerges from meticulous technique alignment rather than trends. ????

It is difficult but important to clearly quantify your success criteria. Machine learning models being non-deterministic in nature it is difficult to predict the outcome of the solution (no pun intended) as opposed to conventional technology solutions. Defining a measurable success criteria helps to time box the model development and communicate the progress periodically.

When defining the problem for an AI or ML project, I engage with stakeholders to understand their objectives and challenges. I then craft a clear and measurable problem statement, establish relevant metrics for success, and assess the feasibility of solving the problem with AI or ML techniques. Data exploration is essential to understand the available data and its quality. Throughout the project, I maintain an iterative approach, ensuring ongoing alignment with stakeholders and validation of the problem definition as project dynamics evolve. This systematic process ensures that the problem statement is well-defined and serves as a solid foundation for successful AI and ML implementation.

Defining the problem is the linchpin in the AI and ML process. It's about distilling the essence of your data challenge into a crystal-clear objective. This clarity guides the selection of AI and ML techniques, ensuring they are not just technologically advanced but contextually apt. The key is to align these techniques with specific goals, be it enhancing efficiency, predicting outcomes, or unveiling insights. Success criteria should be tangible, measurable, and reflective of your strategic vision. In this realm, precision in problem definition isn't just beneficial, it's critical for navigating the intricate landscape of AI and ML towards impactful, data-driven solutions.

Exploring your data is a crucial step in shaping your AI and ML strategy. It involves a thorough examination of the dataset's size, source, quality, and structure. Understanding the nuances of your data - its labeling, categorization, and key features - is essential in identifying the most suitable AI and ML techniques. This exploration reveals not just the data's characteristics but also its limitations and potential biases. Engaging with end-users and adhering to industry standards can provide invaluable insights into the practical application of the data. Remember, the effectiveness of AI and ML is not just in the algorithms but in how well they resonate with the richness and diversity of your data.

I think having enough data is also important. If you don't have enough rows of data (deep experience) or enough columns (broad experience) then, using ml is questionable choice and one should try to collect additional data and varied data.

The individual analyzes how data volume, origin, quality, format, labeling, structuring, and attributes are paramount for AI and machine learning systems. AI and ML rely on substantial amounts of data obtained from diverse sources like surveys, observations, experiments, or existing datasets to effectively train algorithms. High-quality, standardized data in the proper format is essential for accurate predictions, relevant recommendations, and efficient automation. Labeling and structuring data, such as organizing it in a meaningful, application-specific way is important for supervised learning models. Features and variables depend on the application but include measurable properties, demographic info, pixels or words.

I think exploring your data also needs to include to talk to the ones using the data. There needs are the most important. Also checking for industry standards is good for this point.

normalization required ? outliers? imputation needed or just removing rows with NaN ? feature engineering or using raw data in a deep learning model ?

Another consideration is the complexity of the solution based on the data team. - If you don't have a dedicated data scientist or machine learning engineer, a no-code or low-code solution as offered by cloud providers can be the best solution that is easy and fast to deploy, but not very customizable. - If you have one data scientist or ml engineer, a pre-trained model with some fine tuning might work best. - If you have a whole data science team, then they can decide what they need, which might include training a model from scratch.

Exploring your data is a crucial step in shaping your AI and ML strategy. It involves a thorough examination of the dataset's size, source, quality, and structure. Understanding the nuances of your data - its labeling, categorization, and key features - is essential in identifying the most suitable AI and ML techniques. This exploration reveals not just the data's characteristics but also its limitations and potential biases. Engaging with end-users and adhering to industry standards can provide invaluable insights into the practical application of the data. Remember, the effectiveness of AI and ML is not just in the algorithms but in how well they resonate with the richness and diversity of your data.

The type of data available will determine what ML approach can be used. For instance, if you have a labelled dataset or a dataset that can be labeled, which is most commonly the case in industry applications, a supervised technique will be appropriate. If however your dataset has no labels then an unsupervised technique such as clustering would be a good approach to use. Again, if you’re working with image data, for instance, computer vision and deep neural network algorithms would be an ideal choice. So, the combination of a clearly defined problem and the availability of relevant data is required for choosing an appropriate ML solution.

if this is a computer vision task you should probably start with a pretrained NN such as VG* and just add a few layers for your task, e.g. classification, softmax, etc. If you have a high dimensional dataset and want to make sense using unsupervised learning use PCA to reduce the dimensionality then work with clustering algorithms, all in scikit learn

Natural Language Processing (NLP) techniques are essential for analyzing various types of data involving human language. NLP enables the interpretation of unstructured text, extraction of information, sentiment analysis, text classification, information retrieval, processing of audio data, and recognition of handwritten text and text in images. Overall, NLP plays a crucial role in effectively processing and understanding human language data in various forms.

While I'm focusing on classification problems such as fraud detection and default estimation, the outlined steps are versatile and can be applied across various machine learning methodologies, albeit with slight adaptations. When devising a set of modeling approaches, it's crucial to fine-tune your set of hyper-parameters using your training and validation data, utilizing hyperparameter tuning techniques like Bayesian optimization. Subsequently, the finalized trained models should undergo testing on blind test sets, utilizing the complete test set without downsampling to prevent biased or inaccurate outcomes.

Employing standard quantitative metrics, such as AUC-ROC and AUC-PR, across the full model score spectrum is beneficial. Additionally, it's essential to construct an operating table that includes model thresholds as rows, encompassing precision, recall, false positive rate (FPR), true positive rate (TPR), total number of alerts, and the total number of unique alerts, especially if other models are already in production for the same problem, as columns. Understanding the business's daily alert budget provides valuable insights for threshold selection, aiding in aligning the model with the allocated budget. In practical scenarios, there is often a constraint on generating false positives, underscoring the significance of this consideration.

Training and testing an AI or ML model is a critical phase where theoretical design meets practical application. This process involves not just feeding data into the algorithm but fine-tuning parameters for optimal performance. Testing, using metrics like AUC-ROC or AUC-PR, is crucial to evaluate the model's effectiveness on unseen data, ensuring accuracy and reliability. Additionally, consider creating an operating table with various thresholds and performance metrics, especially important in scenarios with existing models for the same problem. This step is vital in aligning the model with real-world constraints and operational needs, particularly in minimizing false positives.

Training and testing an AI model are crucial for assessing its accuracy and reliability. During training, the model learns from a curated dataset and adjusts its parameters to minimize errors. Testing is done using separate data to evaluate the model's performance on unseen data. Accuracy, reliability, and evaluation metrics like precision and recall provide insights into the model's performance. Balancing complexity and generalization are key. Continuous monitoring and evaluation are necessary to maintain performance over time.

Modern AI toolboxes like MLflow and Kubeflow orchestrate your entire training pipeline, automating data preprocessing, model selection, and hyperparameter tuning. Ditch spreadsheets for interactive notebooks in Jupyter or Google Colab. Get over overfitting with techniques like early stopping and regularization, validated by rigorous testing suites across diverse subsets of your data. Experiment with transfer learning, pre-trained models like BERT and ResNet waiting to be fine-tuned to your specific task.

While deploying your model to real-world applications, do not expose your machine learning model directly to consumer applications, rather have a few layers in-between like some micro-service that actually parses the data from the machine learning model and does all the transformation and decoration needed for an application. It will reduce the dependency on the ML engine for any change, and will be easier and your ML model will remain more secure. Also, when you plan to monitor your model and its performance, also plan to analyze all the data and based on that, plan a basic feedback loop in the beginning and once you have more data, you can make this loop more complex.

Deploying and monitoring an AI or ML solution is as crucial as its development. Deployment should seamlessly integrate the model into existing systems, emphasizing scalability and security, often using intermediary services for data parsing and transformation. Monitoring is a continuous process, involving user feedback and performance analysis to assess both the model's accuracy and its real-world impact. It's essential to establish a feedback loop, initially basic and evolving into a more complex system with increasing data. This approach ensures that the model not only maintains its predictive accuracy but also remains relevant and effective in its practical application, aligning with the dynamic nature of real-world environments.

While monitoring the model's accuracy or other relevant metrics is crucial, I believe that assessing the model's impact is even more significant. Monitoring the model's performance informs us about its predictive abilities in relation to the target variable. On the other hand, impact monitoring provides insights into the model's practical utility and effectiveness.

Deploy your AI model on platforms like Google AI Platform or AWS SageMaker for seamless scaling. Serverless inference with Lambda or Cloud Functions keeps costs low, while explainable AI tools like Captum or SHAP ensure responsible predictions. Monitor model drift with Seldon and trigger automated retraining.

Deploying and monitoring an AI solution are crucial steps in improving and updating an AI project. Deployment involves moving the model to a production environment, while monitoring ensures continuous performance tracking and resource optimization. These processes enable performance optimization, feedback loop implementation, predictive maintenance, resource optimization, adaptation to change, and quality control. Overall, deploying and monitoring an AI solution ensures effectiveness, efficiency, and relevance in a changing environment.

Reviews and ratings are a staple of e-commerce, and are even more important for AI models. Misbehaving AI models can be caught and corrected by having a workflow that brings reviews and ratings to the project's AI engineers, data scientists, machine learning operations engineers, and stakeholders. People working in these internal roles should not only be able to read reviews and ratings, but also be able to review and rate the AI models themselves.

Active learning is a good idea. Monai label is a good example: start with a rough model and add more labels to take into account current usage

To collect effective feedback in an AI project, utilize tailored methods such as surveys, interviews, focus groups, usability tests, analytics, experiments, reviews, feedback platforms, AI-powered tools, stakeholder engagement, feedback mechanisms, analysis and action, continuous monitoring, and adaptation. By customizing the feedback collection approach and acting on the insights gained, project success and user satisfaction can be enhanced.

The choice of algorithms for your data depends on the task and data characteristics. For classification, Naive Bayes and CNNs are suitable for text and image classification, respectively. Linear Regression and Gradient Boosting work for regression tasks. Clustering benefits from K-Means and DBSCAN. Isolation Forest is for anomaly detection. In NLP, RNNs handle sentiment analysis, while CRFs are for NER. Recommendation systems use Collaborative Filtering and Content-Based Filtering. Time series forecasting employs ARIMA and LSTM, while deep learning tasks often use CNNs and Transformer models like BERT. Experimentation is key to finding the right fit for your data and objectives.

We need a simple solution for a baseline. For example, for a binary classification, with a yes or no label, the baseline could be to answer yes all the time. Then we can continue by first trying out the simplest ML/AI solution one can think of. Gradually experiment with more sophisticated techniques. If these solutions are better than the baseline, set a threshold that we want to achieve and go ahead with one of the solutions exceeding it. Keep in mind that it shouldn't be something overkill. Use the simplest solution that gets the job done well enough.

An old adage "A captain is as good as his team".I have found that in order for the project to be smooth and successful these are the roles that should be part of the team - + Data Engineer - A person who knows in and out of the data + SME - A domain expert that understands the business process and helps evaluate the business impact of the AI solution + Data Science - Has strong understanding of the machine learning concepts + Engineer - Understands infrastructure and proposes the most optimum way of realising ML products + Story teller - Is able to marry data to business to build a strong business case People with overlapping skills can help reduce the team size. However, it is important to all the above skills are represented adequately.