If you’re looking to hire someone for artificial intelligence consulting services, you need to understand these 6 AI terms before you get started to get the most out of your consultations. Understanding these basic concepts will allow you to assist your developers in providing you the solution you are looking for. At the end of the day, working alongside your consultants is the best way to see the results you desire.
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1. Data Wrangling
Data wrangling is the process of taking raw data and converting it into a different form or schema for use in machine learning or AI. This will be one of the early steps you do with any artificial intelligence consultant to take data you’ve collected in the past and use it to form any models needed for your software solutions. This gives the ai consultant both the chance to get familiar with the data as well as begin to clarify the use case for different models in your solution.
Many of the processes used are things like data importing, data structuring, cleaning bad data, and processes data to create more useful fields.
While this may feel like a novel part of your journey to implement ai software for your business, it is probably the most important and your input will be needed to guide the new consultant through your data. It’s generally agreed that data wrangling takes as much time if not more time than researching and building models themselves!
2. Data Imputation For AI Models
Like we discussed above, most datasets contain some fields that are missing values and create a sparse feel to the data as a whole. While the quick solution to this is to just drop the field or feature completely from the dataset, normally this is a horrible solution, as any data a consultant can get to start with is important.
In this case, most artificial intelligence consulting companies will use data manipulation techniques to add data to the missing values that make the most sense, given the rest of the data.
The most common technique is mean imputation, where you take the mean of the existing data in the field and fill in the blanks with this. You should see lots of data science consultants use this as it’s a great way to fill in blanks while not affecting the current schema of data. Here’s a list of the top ways to input values into a dataset for data filling.
3. Data Partitioning
In many models that use ai or machine learning, data is processed into groups to train and test models. Many ai consulting companies will ask you to provide a certain amount of data, whether that be file size or rows, to make sure they have enough data to split into these groups.
If the developers feel a need to gather more information, sometimes they will work with you to collect data in the future to use as a testing set instead, allowing them to add to an already established database. At Scalr.ai, we try to mix both in, especially if future data is easily obtainable through streams that are easy to handle. This upfront work can allow you to reap benefits down the road by already having data collection pipelines set up.
One of the most common places you will see this used for enterprise business is image recognition tools. Most models built for images will need to be trained on images of the objects you are trying to recognize. Setting aside a test folder of images insures you can test your new model on images the model has not seen before. These tools integrate nicely into larger software solutions built for automation and manual task elimination. Check out this case study to see how powerful image recognition can be.
Many artificial intelligence consulting services implement machine learning or data science models and algorithms that use features (also known to you maybe as fields) and final known targets to find a relationship between the two. You will probably see your ai consultant use at least one of these in your ai software solution.
An example of this would be a model that takes in these fields of a house: square footage, # of floors, and doors. With the target variable being the value of the house that is already known. You would use one of these models to predict the value of future houses.
As you can probably guess, this is the process of using that same input data from above, but reaching some conclusion without the use of a target variable.
Usually, this is done because the target variable is unknown, or general information about the data is unknown and we want to start to build some target variables.
Most artificial intelligence consulting companies will use these algorithms to find outliers in data such as data points that might be a red flag in a security system because they are out of a range.
6. Evaluation Metrics For Models
At the end of the day, you hired someone to build models and algorithms that work and get you results. Evaluation metrics will allow your AI consultant to see real progress in the work they have done, and make decisions about how to adjust to fix any issues.
While the results of these models might be cleaned up a little before showing you the results,
Most of the time you’ll hear terms like accuracy, AUC, and precision to evaluate these models. This is a good place to learn a little more about how the models in your software will be evaluated.
This feature is originally appeared in hackernoon.
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