Fine-Tuning In A Nutshell

Fine tuning is the process of taking a model that has been trained for one task and refining it so that it can perform another task.

Understanding fine tuning

Deep learning is an effective way for models to learn from unstructured or unlabeled data without human intervention. But since the algorithms that underpin deep learning require vast amounts of data, the process can be extremely resource-intensive.

To make deep learning more efficient, small adjustments are made to a process to achieve the desired performance or output. This involves unfreezing some of the top layers of the model library for feature extraction and then training the newly added part of the model with these layers in tandem. 

If the initial task and new task are similar, fine-tuning an existing neural network enables the practitioner to take advantage of what the model knows and can avoid having to create one from scratch.

How fine-tuning works in practice

Suppose we want to fine-tune a model used in autonomous vehicles. At the moment, the model only recognizes cars, but we want to train it to also recognize trucks. 

For the sake of simplicity, we’ll remove the first layer of the model whose task is to classify whether an image is a car or not. Once this layer has been removed, we need to add a new layer to perform the same classification task for trucks.

Fine-tuning may require that multiple layers be removed or added, but it depends on how similar the task is for each of the models. Layers near the end of the model may have features specific to the original task. Layers at the start of the model, on the other hand, usually learn more basic features such as shape and texture.

Freezing weights

Once the structure of the existing model has been modified, we then have to freeze the layers in the new model. Freezing ensures the weights for each layer in the neural network do not update whenever the model is trained on new data.

In more simple terms, we want to ensure the weights are kept the same as they were once trained to classify cars. To enable the model to learn how to classify trucks, we only want the weights in the new or modified layer to update.

Then, it’s a matter of training the model with the new data.

Limitations of fine-tuning

While fine-tuning is an effective way to refine a model, it is not a panacea. The most obvious limitation is that it cannot be used for models with vastly different tasks and datasets.

It is also important to note that fine-tuning will not be able to alter a single layer of the architecture – especially if the existing weights need to be preserved. By the same token, the fine-tuning approach is unsuitable if a practitioner wants to use their own architecture.

If the practitioner chooses the wrong layer to freeze or an inappropriate learning rate, fine-tuning may produce a low-quality model that never acquires the ability to learn. 

Key takeaways:

  • Fine-tuning is the process of taking a model that has been trained for one task and refining it so that it can perform another task.
  • If the initial task and new task are similar, fine-tuning a neural network that has already been designed and trained enables the practitioner to take advantage of what the model knows and avoids having to create one from scratch.
  • Fine-tuning is an effective way to refine a model but should never be viewed as a panacea. It cannot be used for models with vastly different tasks or datasets, and a poor choice of learning rate or which layers to freeze can result in a low-quality model.

Connected AI Concepts


Generalized AI consists of devices or systems that can handle all sorts of tasks on their own. The extension of generalized AI eventually led to the development of Machine learning. As an extension to AI, Machine Learning (ML) analyzes a series of computer algorithms to create a program that automates actions. Without explicitly programming actions, systems can learn and improve the overall experience. It explores large sets of data to find common patterns and formulate analytical models through learning.

Deep Learning vs. Machine Learning

Machine learning is a subset of artificial intelligence where algorithms parse data, learn from experience, and make better decisions in the future. Deep learning is a subset of machine learning where numerous algorithms are structured into layers to create artificial neural networks (ANNs). These networks can solve complex problems and allow the machine to train itself to perform a task.


DevOps refers to a series of practices performed to perform automated software development processes. It is a conjugation of the term “development” and “operations” to emphasize how functions integrate across IT teams. DevOps strategies promote seamless building, testing, and deployment of products. It aims to bridge a gap between development and operations teams to streamline the development altogether.


AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.

Machine Learning Ops

Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.

OpenAI Organizational Structure

OpenAI is an artificial intelligence research laboratory that transitioned into a for-profit organization in 2019. The corporate structure is organized around two entities: OpenAI, Inc., which is a single-member Delaware LLC controlled by OpenAI non-profit, And OpenAI LP, which is a capped, for-profit organization. The OpenAI LP is governed by the board of OpenAI, Inc (the foundation), which acts as a General Partner. At the same time, Limited Partners comprise employees of the LP, some of the board members, and other investors like Reid Hoffman’s charitable foundation, Khosla Ventures, and Microsoft, the leading investor in the LP.

OpenAI Business Model

OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemium model. Microsoft also commercializes opener products through its commercial partnership.


OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).

Stability AI Business Model

Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.

Stability AI Ecosystem


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