How to Accelerate ML Development with Pre-Trained Data Models
Recent advancements in artificial intelligence (AI) like autonomous systems, computer vision, natural language processing (NLP), and predictive analytics are all powered by machine learning (ML). In those scenarios, ML helps to move data in the value chain from the informational level to the knowledge level.
Most smart systems you’ve interacted with today were probably developed leveraging supervised learning. Supervised learning is all about building ML models from scratch. However, this approach isn’t always the best. Many AI and ML projects fail because of a lack of resources and, of course, a lack of useful AI training datasets.
Supervised learning demands time, money, and significant human effort to make it work. That’s why it’s vital for enterprises to find viable alternatives to supervised learning. While for many years there has been no way around this problem, ML engineers have recently identified new ways to optimize ML models.
What is Transfer Learning?
Transfer learning describes the process of using knowledge from a learned task to improve the performance of another (but somewhat related) task. For example, using a soccer player as a placekicker in American football.
This approach helps to reduce the amount of required training data. It also allows ML models to make predictions in a new target domain by leveraging the knowledge learned from the source domain (or existing ML models) or from another dataset.
The ML model was trained to do a specific job, so it won’t be 100% accurate when completing a new task. So, you must prune the model and fine-tune it based on your particular use case. For example, suppose you have an ML model trained to identity dogs. When you add transfer learning into the mix, you can reuse that model and tweak it to identify wolves.
Who Should You Use Pre-Trained Data Models?
The key benefit of using pre-trained datasets in transfer learning is the fact that it’s cost-effective. It also helps accelerate project development and time to market. However, you should only utilize transfer learning techniques when you lack target training data. The source and target domain should also share many similarities even though they aren’t identical.
In general, it’s always better to use small training datasets and simple ML algorithms. This is because small data needs models that aren’t complex or highly biased. In that way, scenarios such as overfitting the model to the data can be avoided.
The resources required to build an ML model from scratch are significant, so it’s not an option for everyone. This is because you’ll need to hire a highly specialized team of data scientists, ML engineers, and data annotators with significant domain expertise.
You also need an enormous amount of data that will cost a lot of money and will take months (or even years) to collect. Then you’ll have to expend time and resources to label your data accurately, program the algorithm, train the model, test the model, deploy it, and continuously monitor it. This will probably be out of reach if you’re a startup or a small to medium-sized company.
Transfer learning evens the playing field and allows smaller businesses to compete with industry giants. You also accelerate time to market because you don’t have to label the data (although you might have to tweak and label some of it based on your use case), and you aren’t depending on a team of experts because you’re not building a new model from scratch.
With the ongoing tech talent shortage, transfer learning could be a lifesaver for many companies looking to maintain a competitive advantage or business relevance. So, when data is missing, it is always best to use the knowledge gained by solving a related task.
What Are the Different Types of Pre-Trained ML Models?
Unsupervised transfer learning using pre-trained models usually follows the process listed below:
Select a Pre-Trained Data Model
Selecting the model is critical to transfer learning. It’s important to get this first step right to accelerate project development and successfully meet your pre-defined objectives. In this case, you must choose a model that’s as close as possible to the use case or problem you’re trying to solve.
What attributes of the source model do you want to leverage for a new task? This could be some or all of them, but it must be established before you kickstart the ML project.
However, if you’re planning on using only parts of a model, you may want to consider using just the model architecture. If it’s a neural network, you can choose specific layers in the network to enable transfer learning.
The correct choice will depend on the type of problem you want to solve and the pre-trained model you’re working with. You must keep “pruning” and tweaking it until it’s perfect for the ML training exercise.
Train the Model to Maximize Performance
To optimize performance, you must keep tweaking your model. By fine-tuning and confirming its precision with additional training data, you stand a better chance of achieving your project goals.
This is because transfer learning works best when you have different datasets to infer knowledge. If finding the necessary data is a challenge, you can leverage data aggregation. In this case, you can modify a small set of data to create different variations to train the model.
If you have your own training datasets, it might be helpful to include third-party data to enable instant access to annotators for efficient labeling. Although AI and ML have come a long way in recent years, finding high-quality labeled data remains a major challenge.
It’s crucial to gather additional data with labeled datasets to build a robust data training pipeline. This approach can accelerate the learning process and is scalable as needed over time.
As more businesses adopt transfer learning protocols with pre-trained data, you can expect to see more quality AI-powered products released regularly. It also enables greater innovation and experimentation without bre
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