Become an AI/ML Model Training Expert: Navigating Data, Models, and Metrics

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Are you ready to take your AI and machine learning projects to the next level?

Welcome to our newsletter on AI/ML Model Training! In today's edition, we embark on a journey through the realm of model training, unraveling the essential components and best practices that hold the key to unlocking the true potential of your AI and machine learning initiatives.

What Is Model Training?

Model training, the backbone of machine learning, empowers you to craft AI/ML models that drive success. The magic happens during the training phase, where the quality and quantity of data you feed your model directly affect its performance.

In this process, two critical elements take center stage:

1. The Training Dataset: This treasure trove of labeled data samples, comprising typical inputs and corresponding model outputs, is your secret sauce. We partition it into training, validation, and testing segments to ensure robust results.
2. The Training Algorithm: The arsenal of algorithms at your disposal comes with trade-offs—complexity, interpretability, performance, and computational needs. Each algorithm has its unique training process and data requirements.

The effectiveness of your AI model is directly linked to the quality of the training data you use.

Modern deep neural networks excel in representing an extensive number of parameters.

But-

If your data is inaccurately labeled, it will result in billions of erroneous features and countless hours of effort going to waste.

We certainly want to help you avoid that scenario :)

What You Should Do Before Training Your Model

Define Your Task

The first phase of any machine learning project is developing an understanding of the business requirements. You need to know what problem you’re trying to solve. Work with the owner of the project and make sure you understand its objectives and requirements. The goal is to convert this knowledge into a suitable problem definition for the machine learning project and devise a preliminary plan for achieving the objectives.

In order to build a successful model, you will need good data, sufficient volumes of data, and the ability to clean the data and prepare it to the model’s training requirements. Depending on the nature and size of the dataset, this can be a formidable task.

At the start of the project, identify your data needs, what data is available, and whether it is in appropriate format and shape for the machine learning project. You’ll need to go through the following stages:

• Identify the problem and candidate algorithms
• Identify data required to train the algorithm/s
• Collect initial data
• Identify its quality and suitability for the task
• Plan what is needed to make the dataset suitable for the project

Finally, it is important to identify differences between training data and real-world data and determine your approach for evaluating model performance.

Define Required Data and Annotations

Some organizations have no problem collecting data for Machine Learning and already have a large collection of information accumulated over the years. In some cases, this includes digitized information. However, if you haven’t collected data, you can use a public or commercial reference dataset to handle this task. 

When you start analyzing your dataset, you can collect data, ensuring it has the right format. You might collect structured or unstructured data—structured data could be a CSV or CLS file with columns for each data attribute, while unstructured data might include text, image, and video files. The type of data you collect depends on the business use case.

There are several ways you can find a dataset online: 

• Google Dataset Search—lets you search via keywords and filter results based on data type or if the dataset is free. 
• Github—provides a list of popular public datasets based on various categories.
• Kaggle—lets you find datasets with usability ratings based on desired categories (it is a valuable resource for independent ML projects). 
• OpenML—lets data scientists share data and collaborate to solve problems. 
• UCI Machine Learning Repository—lists various data generators and datasets.

If you don’t have enough data, you can use a data generator. Synthetic data is crucial for projects that require massive datasets. However, you must understand the dataset creation process to use it. 

Data generators can help provide supplementary training or testing data when you cannot find enough real-world data. Synthetic data is also helpful for protecting privacy and data confidentiality, especially if you process sensitive data (i.e., medical or personal data).

There are two main types of data generators:

• Distribution model-based generators—these might use distributions based on real data or a data scientist’s expertise. 
• Deep Learning-based generators—these include Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which use neural networks to create data and require special expertise.

Choose the Right Model

Once the data is available and you know the problem you need to solve, select the machine learning model that is best suited for the task. This involves the following steps:

• Choose the right algorithm based on your learning goals and data requirements.
• Identify the model variant, architecture, or options that will give you the best results.
• Within the chosen model variant, learn to configure and tune hyperparameters for optimal performance (for example, in a neural network this can be the number of layers, activation function, and learning rate).
• Benchmark models and hyperparameters to prove which works best for your use case.
• Decide if the model provides the required level of explainability.
• If relevant, consider an ensemble approach, running multiple models in parallel to enable a “best of many” results.
• Determine your model’s operational and deployment requirements.

Set an Achievable Performance Level

Establish a line of communication with project owners and stakeholders and create clear expectations about the result of the machine learning project. Talk to them about the performance level a model can realistically achieve and whether that performance will provide value or solve the business problem. Jointly define a minimum performance threshold that will be considered as success.

Choose One Model Performance Metric

An AI/ML model should have one primary performance metric you can assign prior to training, and use to evaluate the model throughout its lifecycle. For instance, for a regression task, you can use the root mean squared error (RMSE) as a performance metric. For a classification task, the performance metric could be classification accuracy against a labeled dataset.

Once you select your performance metric, use it to compare potential models for your business problem, and track each model through testing stages. Use the same metric in production to evaluate the model’s success at generating real-life predictions.

Your AI/ML expertise is about to reach new heights. Stay with us for an amazing learning journey.