Model training techniques

Instruction and application

Complete

As AI processes have been developed, the landscape of training methodologies has expanded dramatically. Traditionally, we would train models from scratch on task-specific datasets; with AI, we now have a sophisticated toolkit of approaches, each with distinct advantages for different use cases.

The training process is often iterative and cyclical, requiring continuous refinement so our models reach peak performance. Let’s explore these processes and understand when is the best time to use them.

Model training and methodology spectrum

Like any construction project, there needs to be a philosophy behind your methodology: what is it that you want to achieve? What resources do you have available? How can you best utilise them?

When training a model to achieve an organisational goal, these considerations are important as they will guide the training process. Modern training methodologies can be categorised along several dimensions:

1. Data efficiency

Refers to how well a model can learn and achieve high performance with a limited amount of training data. Highly-efficient data methods include:

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Transfer learning:

Leverages knowledge from a pre-trained model.

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Few-shot learning:

Aims to perform well with only a very small number of labeled examples.

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Data augmentation:

Artificially expands the training dataset by creating modified versions of existing points.

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Active learning:

Strategically selects the most informative unlabeled data points for annotation.

2. Computation requirements

Describes the amount of computational resources (time, processing power, memory) needed.

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Low requirements:

Simple linear models or small decision trees.

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High requirements:

Deep neural networks with billions of parameters requiring HPC infrastructure (GPU/TPU).

3. Knowledge transfer

The ability to use knowledge learned from a source task to improve learning on a related target task.

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No transfer:

Each model is trained from scratch independently.

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Extensive transfer:

Relying heavily on pre-trained models (e.g., fine-tuning a Large Language Model).

4. Specialisation vs Generalisation

Describes the intended scope of the trained model.

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Specialised:

Designed for a narrow domain (e.g., medical diagnosis from specific imaging).

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Generalised:

Aim to perform across a broader range of tasks (e.g., LLMs).

Common training techniques

Now that we have established the spectrum, let’s look at common training techniques.

Key point

Understanding these techniques enables us to select and apply the most appropriate methods to optimise learning and prevent issues like overfitting.

Advanced training techniques

Due to the rise in ML and AI capabilities, we have seen a host of new advanced training methodologies.

Transfer learning and fine-tuning

Involves leveraging knowledge from a pre-trained model on a source task to improve learning on a related target task.

Distributed and federated learning

Distributed training splits the workload across multiple machines to accelerate training.Federated learning is a collaborative approach where models are trained across decentralised devices while keeping data local.

Reinforcement Learning from Human Feedback (RLHF)

Used to align large language models with human preferences. It involves collecting human feedback on model outputs and training a reward model to optimise the language model.

Final thought

Researching these advanced techniques will expand your toolkit and enable you to tackle more complex problems, building more powerful and reliable AI systems.

A small healthcare startup has developed a novel medical imaging device but has a very limited dataset of labelled images for a rare disease. They want to build a classification model to automatically detect this disease in new scans. Considering their limited data, which of the following model training methodologies would likely be the MOST effective starting point?

A) Training a large convolutional neural network from scratch on their limited dataset.B) Using unsupervised learning techniques like clustering to identify potential disease patterns.C) Employing transfer learning by using a pre-trained model on a large, general medical image dataset and fine-tuning it on their specific, small disease dataset.D) Implementing federated learning by collaborating with other hospitals to aggregate their small datasets.

A research team is developing an AI model to generate realistic 3D models of furniture based on text descriptions. They have access to a powerful computing cluster with many GPUs, and the training process for their initial models is taking several weeks. Which of the following strategies would be the MOST appropriate to significantly reduce the training time?

A) Applying early stopping to prevent overfitting.B) Implementing hyperparameter tuning using grid search.C) Utilising distributed training to split the model training workload across the available GPUs in their computing cluster.D) Switching to a simpler model architecture with fewer parameters.

A company has built a sentiment analysis model for customer reviews. However, they are concerned that subtle changes in language or the use of slang might cause the model to misclassify reviews. To improve the model's robustness against such variations, which of the following advanced training techniques would be the MOST suitable to implement?

A) Contrastive learning using pairs of reviews with similar sentiment but different phrasing.B) Reinforcement learning from human feedback (RLHF) by having human annotators rate the model's sentiment predictions.C) Adversarial training by generating slightly modified reviews that are designed to fool the current model and training the model to correctly classify these "adversarial" examples.D) Transfer learning from a language model trained on a different domain of text data.

In progress