There has been so much talk about Machine Learning and Artificial Intelligence lately that it has become obvious – they are drastically changing the world. Due to how promising these technologies are and the number of benefits they already deliver, many companies are willing to use these solutions for their business transformation.
Although Machine Learning and Artificial Intelligence are related, they are not the same thing, and it’s worth understanding the differences between the two. Artificial Intelligence (AI) focuses on computer systems capable of performing tasks that usually only humans could do – such as decision-making, visual perception, speech recognition and so on. Machine Learning (ML), on the other hand, is a subset of AI that incorporates numerous technologies. By processing large quantities of submitted data, ML recognizes various patterns, and therefore builds new analytical models.
So how much does it cost to utilize artificial intelligence? Let’s discover!
Future Is Now
Machine Learning (ML) certainly has a lot to offer. The state-of-the-art technology becomes pervasive in our lives as it starts to be widely adopted by many companies across different industries.
By automating routine tasks and offering creative insights, every sector from insurance to healthcare is reaping the benefits of ML. E-commerce platforms leverage ML algorithms to facilitate the buying process and personalize their offers based on customer behavior. It has also been seized as an opportunity by marketers – machine learning chatbots prove to be effective at generating leads and providing enhanced customer experience.
Businesses are gaining a competitive edge with AI-driven solutions, which are used for making accurate predictions and generating business insights. The advanced voice and image recognition capabilities have been implemented in easy-to-use mobile applications.
Azati’s Image Modeling Application allows you to change wallpapers on the photo taken with a smartphone immediately
As a rule, the Artificial Intelligence project cost depends on the work being done to develop a product. The development work is usually split into several phases. Having a general idea of the project phases may help you make a rough estimate of its cost. The following application roadmap is adopted by Azati when developing systems based on ML algorithms.
1. Discovery & Analysis Phase
The purpose of this phase is to conduct a feasibility study and set business and project objectives.
The work on a project starts with analyzing the customer’s business processes, data assets, and current metrics. At this stage, the project team defines success factors (expected metrics improvements), applicable technological stack, timeline and budget, and reflect them in the corresponding documentation.
The parties find out whether or not the AI concept is possible, and if it is, define the scope of work needed for the next move, namely prototype development.
If all critical data, processes, and metrics are available in the required format, the phase takes up to 5-7 working days on average. We do it for free.
2. Prototype Implementation and Evaluation Phase
A prototype is a business model created to test feasibility and proof of concept. It can be a limited, text or drawing-based mock-up, or a more sophisticated code-based prototype. Its form depends on the project complexity and tools (screen generators, application simulation programs, or design tools) used to develop it. Prototypes are shown to and discussed with the client.
Prototyping is a great technique that allows software professionals to validate requirements and design choices. Prototypes are quick and cheap to produce, and flexible to adjust. The risks and costs associated with software implementation are significantly reduced, as the requirements are well-discussed early on before development begins.
We are striving to make this step as budget-friendly as possible. Typically, prototype development costs are about $25,000.
3. Minimum Viable Product (MVP)
An MVP is a real product with a set of functional features developed on the basis of the prototype findings. The MVP relies on the client’s actual data and is exposed to a small group of real customers as a simplified version of the ultimate product solution. The feedback is very relevant, as it is way less expensive to modify the system at this stage than when it is fully developed. Average MVP costs vary between $35,000-$100,000, depending on the project size and complexity.
4. Product Release
At the last stage, the product with a complete set of predefined features is developed and then launched into the market. The preceding steps put a lot of emphasis on the requirements elicitation and validation – therefore, the end product is made with minimal risks. The cost of this phase is usually estimated during the previous stages.
ML-specific factors affecting the final ai development price
The process of developing an ML-based system has some distinctive features which determine the final cost.
The development of a reliable ML-system depends not only on excellent coding – the quality and quantity of the training data play a pivotal role.
First of all, large representative data sets are required to reasonably capture the relationship that may exist between input and output features. If there isn’t enough data, there are options like collecting more data or using external data sources. Another solution is using data augmentation methods to increase the sample size artificially.
One more requirement is that the data must be easy to work with – it must be well-organized, and stored in the proper format in a data warehouse. Since this is not the case sometimes, some preparatory activities (e.g., ETL processes) are needed.
The next cost-effective factor is whether or not the data is structured. It is easier (consequently cheaper) to work with well-structured data. In some cases, data is subject to cleaning, tidying, and conversion. Moreover, it provides for working with missing, extreme and unexpected values, dealing with outliers, obvious errors and so on.
In practice, most companies manage unstructured (e.g. free-form text notes), or semi-structured (e.g. XML, email) data. There is a whole class of ML-algorithms created to make use of this kind of data, and typically such projects cost more.
The sufficient algorithm performance is another key cost-effective factor, as often a high-quality algorithm requires a round of tuning sessions. This increases the final cost.
It’s worth noting that the performance rate varies according to the client’s business objective and the cost of wrong predictions. A broker would take advantage of the system that produces 55% of correct predictions, for it already ensures profits. However, a 90.9% accurate system aimed at diagnosing a disease, with treatment being lethal to false-positive patients, is by far not satisfactory.
Why do we need to care about data processing speed and algorithm performance to understand how much the AI project costs? The answer is simple: all modern AI applications are based on data and use it for the learning process. So, if your data is processed slowly – it will take more time to teach the neural network.
So, how much in numbers?
It’s a common misconception that leveraging the ML technology might cost a fortune, but it’s not true nowadays.
While a few years ago only the likes of Google, Microsoft and Facebook could afford to develop ML-powered software, now a large number of companies can do this as well. Thanks to the emergence of various tools, libraries, and frameworks for building ML-based software, the ML technology is becoming more available to businesses.
Prices are set for each case individually. For an insurance fraud detection tool, the price ranges between $100k-300k. However, it all depends on the project scope and complexity, customer and system requirements, as well as other factors, mentioned earlier.
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