Artificial intelligence brings automation to industries, streamlining workflow, improving operations, and increasing revenue. However, determining how your business can benefit from AI can be challenging.
The first step is understanding a little about how AI works so that you can identify what operations it can enhance in your business.
Next, you need to determine whether off-the-shelf products work for you or if it is more desirable to build a custom solution.
The third decision you will have to make is to decide if you want to build in-house or outsource the development.
This article breaks down the various shades of grey from off-the-shelf products to fully custom solutions.
1. Readymade AI-powered products
AI solutions can be obtained in many ways, each offering varying degrees of customization. Sometimes you’ll come across a readymade product, a plug-and-play solution, but without the flexibility of modifications.
Out-of-the-box solutions are easier to implement since they don't require training and are ready for use. These solutions target a wider audience and solve generic problems related to the industry. One example is Rossum.ai which offers an AI-based invoice processing solution (Intelligent document processing or IDP) that handles text data from receipts and other documents.
2. API-based AI-as-a-service solutions
You may also find API-based AI-as-a-service solutions. These REST APIs can be easily integrated into working systems, but they require technical expertise to set up cloud services (such as AWS and Azure).
As per the IDP example above, butlerlabs.ai provides a suite of API products for developers to automate data capture and processing from documents. These ready-to-use products seem quite appealing but accompany some limitations. Users are bound to the functionality provided by the product and have little or no control over the inner workings of the systems.
3. Open-source AI models on GitHub or Kaggle
Open-source AI models on GitHub or Kaggle display excellent results and can be modified to benefit business use cases further. Developing custom solutions on pre-built models can be a bit of a hassle, but the end product has far-reaching potential and many benefits.
4. Models built from scratch
The next option is to build AI models from scratch. This approach takes developers through the entire AI development life cycle, which we will discuss later.
Users can define the project functionality by narrowing AI to specific use cases following their business needs. Taking OCR as an example, after extracting details from the text, a fully customized solution can be trained to capture handwriting and identify text in movies or video clips or similar variations. On top of that, it has complete freedom over inputs and outputs, not limited to predefined limitations of any APIs. The possibilities are limitless. Additionally, customized models might offer better scalability potential as they are purpose-built for your business.
Building a custom AI model is a gradual process accompanying several steps. Let's look at those below.
Machine Learning Development Cycle
A machine learning development cycle is a step-by-step procedure. Each step is carefully planned to optimize development. These steps are as follows.
- Identify AI/ML opportunities: Companies must first identify areas of their business where AI could be put to work. It then needs to prioritize those areas based on its ability to solve the problem (availability of data), the potential economic value of the outcome, and potential ethical implications.
- Data Gathering and Exploration: Every AI project needs data for building models. Publicly available datasets are available for certain training in some models, such as people detection. However, when targeting niche problems, generic open-source datasets may not be suitable. Often, data engineers first develop and implement data mining procedures to collect sufficient and appropriate data for model training. Besides that, companies sometimes must undergo lengthy data modification and cleansing procedures.
- Feature extraction: Data scientists also need to define, what are the important attributes of the data that will be used within the AI model and are assumed to have a significant impact on its results.
- Model selection and customization: There are multiple ways to solve similar problems with AI, each with its pros and cons. Often choosing the right model is a decision about making tradeoffs between accuracy, versatility, or computing power requirements.
- Model Training: Training AI models requires cutting-edge computing resources. While businesses can install in-house machines, one-time investments are often prohibitive of this path. Instead, many opt for using online services in the form of PaaS or IaaS to rent computing power. Either way, training models require companies to employ AI engineers and data scientists with the relevant knowledge.
- Evaluation of model performance and deployment: When the outputs of the model meet the business requirements, it then often needs to be integrated into existing systems.
- Monitoring: Engineers must continuously monitor AI models once deployed. The model's performance dictates how useful it is against the targeted problem. If the performance is unsatisfactory, the training needs re-evaluation.
- Fixing mistakes / Re-training: AI models are rarely 100% accurate, which is why re-training is essential to the development pipeline. Every re-training process includes newer data and targets issues faced by previous model versions.
Organizations, that decide to build their own models also have to weight the pros and cons of on-premise vs. cloud security of the solution.
5. Use Low-Code/No-Code platforms
The entire ML development cycle can be overwhelming, especially considering the time and cost involved. Now, organizations can also build no-code/low-code custom AI solutions with the help of platforms such as botx.cloud. Low-code solutions require little to no programming expertise and often allow customization in selecting appropriate models, custom training, and rich integration possibilities. They also have a fully developed backend which includes all the functionality to carry out AI operations. Developers can link the solution with their existing system via REST APIs, which makes integrations simple and fast. Similarly like API-based AI-as-a-service solutions, these platforms are cloud based and are secured by the providers.
Custom AI solutions allow businesses to automate entire workflows. They demonstrate top-notch performance with the added flexibility of customized development and deployment.
An experienced AI partner will know what the most efficient way to build a custom AI solution for a particular use case is and will balance the need for customization with the economic viability of the model.
Low-code or No-code AI platforms allow professionals without AI backgrounds to build AI solutions without worrying about AI infrastructure or problems with scaling the solution.
Interested but need help figuring out where to start? Reach out to us. We will help you understand your business needs and their appropriate solutions. Contact us today.