From machine learning models that help predict the climate impacts from different forms of energy, to Meta’s latest technology that holistically solves networking issues, artificial intelligence (AI) has taken the world by storm and with it, we’ve also seen machine learning (ML) technology emerge into the mainstream. As such, there’s a good chance you’ve heard terms such as “machine learning models” and “machine learning training” when consulting with AI development companies, leaving you scratching your head, uncertain as to what it all means.
At its most basic level, machine learning training is a process that involves inputting data to develop an algorithm or machine learning model that’s used to drive artificial intelligence deployments. But how are machine learning models trained? And how much data do you need in order to train the machine learning model that will serve as the basis for your organization’s AI technology?
Data Preparation and the Machine Learning Model Training Process
Machine learning model development is one of the first stages in the AI development process. The more data that’s used to train a machine learning model, the more effective it will be. But before any data is fed into a machine learning model, there’s the matter of preparing the data.
“The first step — and often, the most significant challenge in AI development — is the initial data preparation. It does not have to be in a specific format, but we do need to perform the following three steps before we begin the ML model training process,” explained 7T President and COO, Shane Long. The following is an overview of the three phases of data preparation.
Identify and capture the entire data ecosystem –What data will you include and exclude? Where is that data located? What third-party data sets will need to be obtained and do they have APIs? These are some of the areas that are addressed in this first step of the data preparation process.
Data pre-processing – The data must be cleansed, transformed and normalized so it can be inputted to create the machine learning model. This process can be rather intensive for unstructured data; less so for structured and semi-structured data sets.
Splitting the data – As the development team prepares to train a machine learning model, the data must be split into three distinct sets. These three ML data sets will include a training set, a testing set and a validation set. The training set represents the data that’s fed into the ML model. The testing set is just that – a set of data that’s used to evaluate the model’s overall accuracy and performance. The validation set is used to evaluate the ML model’s performance relative to existing models.
As mentioned above, there are three forms of data that can be used to train a machine learning model: Structured, semi-structured and unstructured data.
Structured Data – Structured data makes for a relatively easy and straightforward ML model training process. This data is well-organized, with defined data fields. Think of the data in an excel spreadsheet as an example of structured data.
Semi-structured Data – Semi-structured data has some characteristics of structured data and unstructured data. An example of semi-structured data is a survey form with multiple choice questions (structured data) and open-ended questions (unstructured data.)
Unstructured Data – Unstructured data cannot be mapped into predefined fields such as the columns and rows of a spreadsheet, and there is typically no associated data model. This data is inherently difficult to query and analyze. Think of the data contained within the open-ended questions on a survey or end-user text from a company’s help desk system; are examples of unstructured data.
Notably, recent years have brought about a major shift when it comes to leveraging unstructured data, leading to better and more efficient ML models, and, in turn, improved AI deployments.
How Much Data Do You Need for Machine Learning Model Training?
We’ve seen significant advances in machine learning and AI technology, whereby developers can use smaller amounts of data for artificial intelligence implementations. This has opened the door for small- and mid-sized companies (SMBs), which can now leverage these technologies, whereas previously, ML and AI technology was only available to the largest companies with tremendous volumes of data.
“The more data, the better. That’s always the ideal. But with a modest data set of approximately ~50,000 full-featured records, you can make a great deal of progress with the available AI foundation models and tools,” Long explained of the data requirements for developing a machine learning model.
Today, developers can use multiple data streams to train a machine learning model. In the early days of this emerging technology, single-modal machine learning was the norm, with a single data source. But today, we have multimodal machine learning, with the ability to use multiple data sources to develop ML algorithms. The end result is greater versatility, improved efficacy and a greater ROI.
As the machine learning model training process progresses, the development team will make modifications to improve accuracy and overall efficacy. Developers examine the output and this is used to guide algorithm modifications. To this end, machine learning training is typically an ongoing process with continual improvements. It’s even possible for machine learning and AI technology to self-improve over time.
Hiring a Dallas AI Development Company
Finding the right AI and machine learning development partner is essential for a project’s success. At 7T, machine learning and AI development services are our specialty. It’s estimated that 8 out of 10 of 7T’s 2024 development projects will include a machine learning-driven AI component.
Our world-class team of artificial intelligence and machine learning developers work to identify challenges within an organization; then, we create a value-generating solution with machine learning and AI technology. This problem → solution approach to AI development has the potential to bring about exceptional results, generating new, profitable opportunities using the most innovative emerging technologies.
Connecting with the right artificial intelligence developer can be challenging as you need a partner who understands the organization’s pain points, objectives and business strategy — both today and in the future. At 7T, we take the time to get to know your business, providing clients with a Business Requirements Document (BRD). The completed BRD leads to a comprehensive understanding of an organization’s needs, pain points and future objectives.
Also, check out our latest eBook, A Guide to Prepare Your Business for Artificial Intelligence (AI) Development.
7T’s Digital Transformation development team is guided by the approach of “Digital Transformation Driven by Business Strategy.” As such, the 7T development team works with company leaders who are seeking to solve problems and drive ROI through Digital Transformation with innovative business solutions such as multimodal machine learning-powered AI implementations. 7T has offices in Dallas, Houston, and Charlotte, NC, but our clientele spans the globe. If you’re ready to learn more about Digital Transformation development solutions, contact 7T today.