Organizations are constantly looking to incorporate artificial intelligence (AI) in their daily operations.
However, with the large amounts of time and money required for extensive AI integration, organizations must find smarter ways to implement AI, such as using pre-trained AI models.
As you probably know, transforming an organization with AI and machine learning can be time-consuming. The efforts and finances required to complete the process depend on the level of automation and digitization being introduced in the various departments of the organization. Deep learning and other components of AI need thousands upon thousands of datasets to improve the competency of automated operations over a given period. As a result, the process of AI deployment may be arduous for organizations if they are starting the implementation process from scratch. To resolve this, the concept of transfer learning can be useful for organizations. Transfer learning, in simple words, is a technique involving the usage of pre-trained AI models to hasten the deployment process for your organization. What is transfer learning? What are pre-trained AI models? Read on to know more.
Normally, machine learning involves the use of datasets secured from a wide range of sources to train AI's neural networks regarding visual, audio, and other types of information. A common issue with this method is the sheer data crunching that goes into finding historical information for machine learning. To carry out deployment through the standard process can be huge time and money-consuming for organizations. Also, it is practically difficult to find massive datasets tailor-made for machine learning. Business owners seek solutions to make AI implementation in their operations more profitable and sustainable. Transfer learning can grow in significance in organizations looking to automate their operations with advanced AI models.
As a concept, transfer learning includes many aspects of machine learning. In transfer learning, the knowledge gained during the training of one type of problem is used for another, similar problem. In this way, instead of developing a neural network from scratch, organizations can use existing networks of algorithms that can achieve the results that they are seeking. In this method, data analysts have to determine which layers or strands of data can be useful for pattern recognition. For example, if an AI model is being trained to identify a pigeon from a large gathering of various species of birds, analysts can use neural networks which contain past knowledge about identifying, say, sparrows for the purpose. With minor modifications, the model can be ‘taught’ to uniquely identify pigeons and, in the future, all the species of birds on earth.
Organizations need to have clear ideas about the type of AI model training they want to implement (information-wise). They need to have clear questions to be answered regarding the objective of training a neural network. By clarifying these requirements right off the bat, data scientists and analysts working on automation operations can identify the ideal types of pre-used flexible networks, which include several forward and reverse iterations. By employing pre-trained AI models which have been trained earlier on big datasets, they can use their existing protocols to co-relate and use for their existing AI models. In a way, this is essentially a machine-to-machine exchange of information. By using this process, organizations 'transfer the learning' of the pre-existing model for the purpose of solving newer problem statements.
Organizations need to be particularly cautious while choosing the pre-trained AI models for their specific problems. The process of finding similarities in datasets is key for data analysts working on the process. If the problem statement for a given machine learning operation is completely different from the one on which the old training model was applied, then the deployment output will show wildly inaccurate results in the later stages. For example, a model trained to identify grammatical errors would be a complete mismatch if it is used to train a brand-new model for the purpose of identifying human facial features.
As specified earlier, pre-trained AI models will need basic modifications to adapt them for similar problem-solving purposes. These pre-trained AI models generally tend to generalize the data outside its area of 'expertise.' To iron such niggles out, data experts need to fine-tune the existing model in various ways. Data experts involved in the process generally assume that the pre-trained network has little to no 'neural' problems. Therefore, they do not make sweeping changes in the weights (signal regulators between two neurons in a network). Organizations generally use reduced learning rates (compared to the learning rates used for the older model training) when they commence the process of training their new AI models.
Data specialists make slight alterations to the pre-trained AI models before using them in the transfer learning procedure. The extent of modification depends on the type of project as well as the degree of variance (from the original project) that is needed in the new AI models.
Replicating the Pre-Trained Model Architecture: Organizations can simply redesign a pre-trained model whilst keeping its basic structure and composition the same. While the architecture is identical, the weights are initialized randomly, and the AI model is trained for new datasets.
Making Attribute Extraction Models: A pre-trained AI model can be used for the purpose of identifying specific attributes in given data. To attain this, engineers can remove the output layer and then use the pre-trained network to extract features for newer datasets.
Variable layer configuration of AI Models: Organizations can partially train new AI models using the pre-trained neural network. In such cases, the weights of the initial layers of the AI model are frozen while only the higher layers are trained. Testers generally mix it up with the number of frozen and active layers to observe the differences in the output of the AI models. Certain scenarios and rules must be considered and complied with while fine-tuning pre-trained AI models. There are several positives of using pre-trained models instead of 'reinventing the wheel' when it comes to training AI models.
a) Simplicity: As implied earlier, organizations can save loads of time, money and effort by adopting transfer learning for making themselves AI-ready. The different steps involved in finding and using pre-trained neural networks can be incorporated easily by accomplished IT experts.
b) Performance Stability: AI systems trained via pre-used neural networks can quickly achieve similar (or even better) levels of model performances compared to their train-from-scratch counterparts.
c) No Data Labeling and Versatile Use Cases: Organizations deploy transfer learning because it does not necessitate large quantities of labeled, curated data for training the models. Additionally, versatile use cases can be generated from predictive analysis, attribute extraction and transfer learning.
Transfer learning is a fairly straightforward process. However, data experts involved take special care to avoid making glaring blunders in the procedure. Some of the precautions are:
Firstly, organizations need to be aware of the similarities between their datasets and the ones on which the pre-existing model was trained. Secondly, they need to know exactly how many attributes belonging to the original model can be correlated with the requirements of the new one. These considerations are vital as the new model's performance patterns will closely replicate the older network's working.
Organizations must ensure that the pre-processing of the new AI model should be identical to the original model's training. Data pre-processing can be defined as the processing performed on raw collected data to repurpose it for further rounds of processing. In the case of new AI models, the pre-processing values must be close to that of the pre-trained AI models.
Hardware selection is key when opting for transfer learning. Here are some options which could come in handy for machine learning tasks involving pre-trained AI models: NVIDIA systems, Apache MXNet and Stanford DAWNbench are some of the well-known applications and platforms for the same.
Generally, organizations are advised by data analysts to keep the pre-trained parameters constant (Or, in other words, use the pre-trained AI models as feature/attribute extractors). If the parameters change, then the organizations should modify them with an imperceptible learning rate increment or decrement so that the networks do not erase the learnings of the original model.
Quite simply, transfer learning can accelerate the process of AI deployment in an organization. As we have seen, there are a few factors that hold the machine learning process back if it is carried out from scratch by organizations. The main factor in favor of using pre-trained models for AI training is familiarity. Pre-trained AI models are well-versed with the requirements needed to train a new AI model feeding on similar types of datasets.
Usually, massive datasets are needed to train a neural network from scratch. Such sets are not always available -- this is where transfer learning enters the fray. Transfer learning empowers the basic machine learning patterns. New models can be trained with comparatively little training data since the model is pre-trained. Transfer learning is useful in the field of Natural Language Processing (NLP) because it requires expertly curated data for its operations. Most importantly, the model training time is reduced from a few weeks or months to a few hours or days.
Naveen is the Founder and CEO of Allerin, a software solutions provider that delivers innovative and agile solutions that enable to automate, inspire and impress. He is a seasoned professional with more than 20 years of experience, with extensive experience in customizing open source products for cost optimizations of large scale IT deployment. He is currently working on Internet of Things solutions with Big Data Analytics. Naveen completed his programming qualifications in various Indian institutes.