If you want to use artificial intelligence consulting services, you must first understand these six artificial intelligence terms in order to make good use of the consulting content to a large extent.
1. Data Wrangling
Data organization refers to the process of obtaining metadata and converting it into a form and structure that can be recognized by machine learning and artificial intelligence. In order to obtain the data collected by customers and use these data to build any models required for software solutions, data collation is one of the first steps that any artificial intelligence consultant will take.
This process involves many steps, including data entry, data structuring, cleaning up bad data, and processing the data to create more valid fields. This part looks simple, but it may be the most important part, during which data input by the customer is required to guide the new consultant to organize the data.
2. Data interpolation of artificial intelligence models
Most data sets have missing value fields, which makes the data set seem sparse. The quickest solution is to simply remove this field or attribute from the data set, but usually this solution is very low-level, after all, any data that the consultant can obtain at the beginning is very precious.
In this case, most artificial intelligence consulting companies will use data processing technology to assign the most reasonable value to the missing value based on the remaining data. The most common technique is the mean value interpolation method, which takes the mean value of the known data in the field and fills in the vacancies. Many data science consultants use this technique, which is a good way to fill gaps without affecting the current data architecture.
3. Data partition
Many models that use artificial intelligence and machine learning will group data into groups for use in model training and testing. Many artificial intelligence consulting companies will require the provided data, whether it is file size or number of lines, to meet a certain number of requirements, so as to ensure that there is enough data for grouping.
Sometimes they will work with customers to collect future data as a test set and add it to the established data set. At Scalr.ai, we will try to combine the two, especially when the future data can be easily obtained through an easy-to-control data stream.
4. Supervised learning
Many artificial intelligence consulting services use machine learning or data science, and use algorithms to find the connection between the two based on attributes (also called fields) and the final known target. Most artificial intelligence consultants use at least one of these methods in AI software solutions.
A typical example of this method is a model that uses the square footage, the number of floors, and the number of doors of a house as fields. The target variable is the known house value. Using this model, the future house price can be predicted.
5. Unsupervised learning
You guessed it. The above set of input data was used in this process, but the target variable was not used, and a different conclusion was reached. Generally speaking, this is done because the target variable is unknown, and the overall information about the data is unknown, but some target variables need to be constructed.
Most artificial intelligence consulting companies use these algorithms to find outliers in the data, such as out-of-range data points in the security system, which may be red flags.
6. Evaluation indicators of the model
Finally, hire people to build effective models and algorithms to get the desired results. The artificial intelligence consultant can grasp the actual progress of the completed work through evaluation indicators, and make decisions on how to adjust and solve the problems that arise.
Most of the time, the terms you can hear for evaluating models are accuracy, AUC, and precision, but in fact, there are many ways to evaluate models in software.