The generative AI wave is in full force, and many enterprises are hoping to take advantage of innovative new AI-driven technologies. In fact, 78% of enterprises plan to adopt xGPT, LLMs or generative AI as part of their AI transformation initiatives during the fiscal year of 2023, according to a study from ClearML and the AI Infrastructure Alliance (AIIA). There has also been an enormous uptick in new AI services and new machine learning (ML) models to choose from.
Now that the market is saturated with AI, how do we determine what kind of model is appropriate for the situation at hand? Well, ML models can arguably be designated into two categories: Off-the-shelf models built for general use cases and bespoke models designed from scratch for specific domains.
Both off-the-shelf and custom models will play a role in tomorrow’s AI-fueled landscape. So, when should you use one over the other? Below, we’ll consider when it’s appropriate to use generic versus custom models and examine the advantages and disadvantages of both approaches.
When To Use Off-The-Shelf AI
Off-the-shelf AI refers to pre-trained models. These models are designed for generic use cases and are optimized to do one thing and do it really well. Off-the-shelf AI can come in various forms—some ML models are fully accessible on networks like Hugging Face or open sourced on GitHub. Others are more proprietary, with access priced as software-as-a-service (SaaS).
The obvious benefit of off-the-shelf AI is that you don’t have to produce something from scratch, yet you sacrifice some customizability. Here are some other factors to consider choosing a pre-trained model:
- Time and skills gap: If you want to quickly integrate a model, using a pre-trained library can help save time and energy. This may also be a deciding factor if your organization lacks the skills to train your own AI from scratch and integrate it into your application.
- General use cases: There’s no need to reinvent the wheel if your application requirements fall into general use cases. For example, plenty of competent pre-trained models exist for natural language processing, image classification, facial detection, sentiment analysis and other areas.
- Limited dataset: If you have a limited amount of data or data that is highly unstructured, it might make sense to utilize off-the-shelf AI. For instance, OpenAI’s GPT-4, which powers Copilot X, has been trained on billions of lines of code.
- Lack of resources: Besides requiring an extensive dataset, training models can be highly computationally intensive. Therefore, if your organization lacks computing resources or funding for training, it might make sense to go with a generic model.
When to Create Custom Models
Custom models are a bit more hands-on than off-the-shelf AI. Creating a bespoke model requires a unique set of structured, labeled data and a platform for training the model. For example, this could be accomplished using TensorFlow, a popular open library for implementing deep learning.
Training a model independently is expensive but might be worth it for certain circumstances. Most obviously, if the model you require hasn’t been developed yet, it will require custom development. Here are some other reasons you might want to opt for a bespoke model as opposed to a pre-trained library:
- Large data quantity: If your corpus of data is large and unique to a task at hand, you may be well-equipped to train your own model. Suppose you have terabytes of internal data detailing a company’s inner workings, such as logs from customer interactions on a specific e-commerce website.
- Highly domain-specific: Or, perhaps your needs are unique and highly specific to a domain or vertical. In this scenario, a generic model may not cut it. Domain-specific models may be necessary to cater the AI to nuances within specific industries, like finance, healthcare, or education.
- Security and privacy: Data privacy concerns have somewhat slowed the adoption of new generative AI. Some organizations have even outright banned the use of ChatGPT. Custom ML development could help avoid security, data privacy or intellectual property gray areas.
- Greater degree of control: On that note, developing a custom model helps an organization fine-tune a library from scratch. A higher degree of control is likely necessary if you perform novel research or are a software company specializing in AI development.
Combining the Two With Transfer Learning
Although building your own AI from scratch is tedious and requires a wealth of data, it grants more control over the development process. That being said, pre-trained libraries are a viable option to help quickly jumpstart new AI endeavors. However, there is also a third middle ground to consider.
Another roadmap is to start with an off-the-shelf model and then fine-tune it over time. Reperusing a library for a second related task is known as transfer learning. This could be a helpful alternative to hit the ground running with a framework and then mold it to your needs over time, effectively bridging the benefits of both worlds.
Prepare for Future AI/ML Ops
AI is quickly enhancing various applications, and the market around ML is set to increase in the coming years. Research and Markets predicts the global automated machine learning market will reach over $5 billion by 2027, with a CAGR of 42.97% from 2022 to 2027.
Regardless of the exact implementation, the exponential growth and integration of AI/ML into the software development life cycle must be matched by an equal investment into operations, including ongoing maintenance, upkeep and security compliance, to ensure that the upfront investments pay out in the end.
