What is AI Training Data?
The term Artificial Intelligence is the process of a machine imitating human intelligence factors such as; learning, self-correction and reasoning. Where companies typically find it useful are fields of object identification, pattern finding and efficient processing of patient data, to name a few. Companies using artificial intelligence in their operations need data to fuel their algorithms. The fuel, AI/ML training data, refers to datasets which are used to help a program understand how it should use technologies such as neural networks to educate itself further.
The end goal to training your AI is to give your business more automated efficiency with fewer errors. Training data is often associated with validation and testing sets. Using the mentioned datasets in combination will make your programs more reliable, smarter and efficient.
How can I use AI & ML Training Data?
Any company working with AI can gain from training data. Before coming effective, systems based on artificial intelligence need to be optimized and taught to perform their tasks. AI/ML Training data does exactly this.
The popularity of artificial intelligence as a topic hasn’t appeared without a reason. As the technologies develop, more and more companies aim to utilize machine learning and AI in their future operations. Any company who needs to attract potential buyers, make better business decisions, or learn more about their customer needs, can use AI to perform data analysis and find patterns in big data. The typical users would include people from the following industries/departments:
- HR departments can use AI to create automated candidate selection and filtering pipelines, enabling them to find people who fit their company quicker and more efficiently.
- The healthcare industry can and is currently using machine learning for daily tasks such as: personalized X-ray readings, medicines and personal health care assistants.
- One specific use case is using such data for the medical hardware. Nowadays some hospitals use robotic powered devices to perform surgeries, and these devices are created using artificial Intelligence and robotics.
- Another use case for the medical industry is building automated medical records. While avoiding the use of paper, makes it easier to access and keep track of the records, as well as avoids human error.
- Some Retail companies use AI to create virtual shopping experiences for customers in by providing personalized recommendations.
- Another use case for this industry would be applications within stock, inventory and supply chain management. Machine learning technology can easily automate all these processes, allowing retail companies to hand their inventory management systems/decision to AI-based applications.
- AI/ML based data is used by many banks as it cuts out a lot of manual labor, and some use cases include: the identification of fraudulent transactions, more accurate credit scoring, automating manual management tasks.
- Technology is constantly advancing, and new ways of committing fraudulent transactions have formed, putting cardholders in more risk. AI-based applications can help in pattern recognition, making it easier for the banking industry to automate their fraud detection processes.
- The Marketing industry can benefit vastly from AI data, as it can be used to analyze people’s social presence, and extract the necessary information for this industry to attract the right people.
- AI-based applications can handle day to day tasks and customize marketing and sales information for consumers. An example of such an application are AI-based Chatbots which enable you to improve customer satisfaction by creating recommendations for upselling. Over time, the application can learn more about the customers and provide a more accurate recommendations to your customers.
- Machine learning can automate the creation of pricing models for individual market segments.
- A specific use case for this field is cutting out A/B testing and instead creating responsive search ads, which learn different combinations over time and increase the chances of discovering what works for your company in a shorter timespan.
- The Education industry is currently benefiting from AI learning. A particular use case is the creation of a scheduling system which organizes parent teacher meetings, as well as other school activities.
Common use cases are as follows:
- Object tracking - tracking moving objects across frames within a video
- Human verification (KYC) - Captchas, differentiating between human and machine
- Logo / Object recognition - recognizing patterns and identifying aspects from images and videos
- Automated support - text based learning can be used in applications such as chat bots
What are typical AI/ML training Data attributes?
The data attributes could vary depending on your use case, and the API’s available for a specific scenario.
How is AI/ML training Data typically collected?
Due to its wide variety of use cases, this data type is often collected from a multitude of different sources. However, a typical approach involves collecting a large amount of data from the web by scraping techniques. The raw data is then stored on a server. AI/ML Data providers offer API’s to their servers where the data can be licensed directly from.
As an example, take a look at this specific use case:
You are in need for a system which automates the detection of fraudulent transactions and you would need to collect a massive amount of data about consumer purchase behavior. This is typically done by an automated system which triggers an insert into a database each time a transaction is made by a cardholder. However, in order to paint the bigger picture you would need to identify patterns that warn about fraudulent behavior.
Finding such patterns in datasets which may have millions of data points will become painful and is not scalable. Given their capabilities, AI and ML applications can do this job for you. In order to get the AI working for you, it first needs to be “trained” with training data. The data collected for training purposes must be consistent, it should contain all the possible scenarios that could occur. Without a sufficient dataset the machine learning algorithms will have a hard time improving themselves, leading to results which can be skewed and inaccurate.
How to assess the quality of AI/ML training Data?
You may be inclined to go for any data you get your hands on, however taking the following steps to ensure good quality of the data would be beneficial.
Watch out for:
- Number of empty fields (missing information will leave gaps in learning)
- Data coverage (better coverage will give your program a more holistic view on the problems it should solve)
- The ratio of data to errors (errors will lead to skewed machine behaviour)
- Outdated data (depending on the industry and use case, timeliness of data might be highly relevant for reaching efficient results)
How is AI/ML training Data typically priced?
We see many providers offering access to their databases via licensing fees. However, given the variety of use cases for this data type, custom quotes and one time transactions are not seen unfamiliar in the AI training data acquisition space.
What are the common challenges when buying AI/ML training Data?
AI and ML are still relatively new topics in today’s business. While the technologies revolving around the area have evolved quite far, many companies are still struggling to find datasets which serve their purposes well.
Some common challenges you may face are:
- Not finding the right data provider which can provide you with your specific use case
- Skewed data
- Poor data quality
- Data which doesn’t hit all your requirements, preventing you from getting the relevant outcome.
What to ask AI/ML training Data providers?
When entering a discussion with your data provider it is important to gain an understanding of their offerings, how is their data collected and by what means are they able to integrate to your daily business processes.
Here are some questions to get you started:
- Ask them for details of how they collected the data
- Clarify what the key identifiers for this data type
- Make sure that they specify the ratio of data to errors
- Ask them how the data can be integrated into your system and what their procedure is for this