Key Components of a Data Science Factory: Model Development
The field of artificial intelligence (AI) continues to expand rapidly. According to Statista, global revenues from enterprise AI applications are expected to reach $31.2 billion by 2025. This trend is set to achieve a 52.59% compounded annual growth rate (CAGR) within the forecast period starting in 2018.
For those operating in the industry, this robust expansion continues to drive innovation. As incumbent workflows evolve, many enterprises are beginning to leverage the power of AI. From image recognition to the use of algorithms and machine learning to protect against security threats, enterprise use cases are showing ongoing diversification.
Source: McKinsey
Enterprises are now building machine learning models that have the potential to revolutionize their businesses. However, many of these models fail to generate real returns in the absence of necessary tools and expertise. As such, many companies are turning to solutions that streamline model development, reduce friction, and improve outcomes.
In this article, we’ll explore the model development process, the emerging alternatives available to enterprises, and the future of the AI industry.
The AI Model Development Process
The process of building an AI model is complex and daunting. As such, many enterprises struggle to maintain momentum when developing and implementing AI solutions. But what exactly does this process entail? Let’s take a closer look at each component of the AI model development process.
Problem Definition
As with any business endeavor, identifying the problem that must be solved is crucial from the start. Business objectives must be specific enough to guide the model development process; generic goals like "reducing costs" won't generate optimal results. It's also crucial for companies to quantify the amount of improvement they're hoping to achieve, setting the stage for meaningful ROI assessments.
Data Acquisition
Acquiring the right quantity and quality of data is vital to the success of any AI model. Cutting corners here can dramatically reduce the accuracy of outputs. This step of the process results in a representation of data that is necessary for training the model.
Data Preparation
Because algorithms aren't intelligent enough to extract meaningful insights from raw data, it must first be prepared using various methodologies. These methods derive meaningful insights from raw data following pre-processing.
Feature Engineering
Feature engineering is a crucial component of building any AI system. Features are the parameters in the data that influence the model. The process of identifying these features is known as feature engineering, and it's hugely time-consuming. According to Forbes, data scientists spend 80% of their time in the data preparation phase before modeling.
Model Development
The first step toward model creation involves selecting the appropriate algorithm(s). These algorithms rely on prepared data to create and train the model. There are hundreds of machine learning algorithms that data scientists can access, and new ones emerge every day. In producing a functional business tool, the correct algorithm and machine learning problem must be in alignment.
Source: Cleaning Big Data
Model Validation
Following the completion of a model, companies must review the outcome. This process might involve assessing the impact of changes, evaluating risks, and making deployment decisions. After an exhaustive development and experimentation phase of model development, companies must ensure they maintain focus on original business objectives.
Multi-Modal Development
Using the right tool to build AI models depends on the problem being solved, and the skillset of those working to solve it. Luckily, today's market offers enterprises several options. We can breakdown these options into select categories providing unique features.
Code-Based Model Development
Code-based model development refers to those platforms that use coding languages such as R, Python, Spark, and TensorFlow in Notebooks or IDEs to build intelligent models. These solutions are well-suited to tech-savvy teams that have plenty of experience working with open source development.
Low-Code GUI Model Development
As is stands, AI algorithms exist under layers of complex code. However, this dynamic is changing with the advent of graphical user interfaces (GUI) for data preperation, feature engineering, model development, and validation. These visual environments have made constructing machine learning models more accessible.
And because a more diverse range of users can use them, they facilitate the democratization of AI, also helping to ensure the technology is used responsibly and ethically.
AutoML Solutions
Industry heavyweights like Google and Amazon have access to full-stack AI teams, but most companies don’t - that’s where AutoML comes in. AutoML refers to the techniques and tools that enable the automation of machine learning processes. For instance, AutoML might automate data collection and cleaning, model development and testing, or production deployment and scaling.
Because producing successful enterprise-level AI models remains hugely inhibitive, AutoML continues to gain traction as a shortage of specialized skills persists. However, it’s important to note that while AutoML can make model production and validation easier, it does have limitations.
The Evolution of AI Modeling
Although developing AI models remains an intensive process, companies like Quickpath are working to streamline the experience. Through the use of GUI and AutoML platforms, enterprises can better deploy resources to achieve viable project outcomes and scale productivity across a wider audience of mixed skills sets.
Although this movement is in the early stages, market disruption has already started. As the ecosystem continues to evolve, the democratization of AI appears poised to continue. And with accessibility further bolstering growth, the AI industry and resulting models point to immense potential.