Google researchers found that data cascades “triggered by traditional Artificial Intelligence (AI) / Machine Learning (ML) practices that keep data quality low – combining events with negative, downstream impacts from data issues – are common (92% prevalence), invisible, delayed, but often avoidable” found it.
The term Artificial Intelligence (AI) here to be referred to as an umbrella covering.
The two core components of all Artificial Intelligence (AI) systems are Data and Model, and both go hand in hand in producing desired results. However, we know that the Artificial Intelligence (AI) community is biased towards putting more effort into model building. A plausible reason is that the Artificial Intelligence (AI) industry closely follows academic research in Artificial Intelligence (AI). Furthermore, thanks to the open–source culture in Artificial Intelligence (AI), the latest developments in this field are available to almost anyone who can use GitHub. As a result, many engineers choose to work on models instead. The equation suggests that we can refine our code or refine our data to improve a solution, or of course, both.
Machine Learning (ML) is an iterative process that involves designing experimental tests around the model to improve performance. It consists of finding the exemplary model architecture and training procedure among many possibilities to arrive at a better solution.
The established process requires keeping the data constant and improving the model until the desired results are achieved.
This consists of systematically modifying/improving datasets to increase the accuracy of your Artificial Intelligence (AI) system. Unfortunately, this is often overlooked, and data collection is a one–time task.
In the emerging data-centric approach to Artificial Intelligence (AI), “data consistency is paramount.” To get accurate results, you keep the model or code constant and iteratively improve the data quality.
These data systems do not “make Artificial Intelligence (AI),” and these Artificial Intelligence (AI) technologies do not “make Data,” it is tough for businesses to succeed with Artificial Intelligence (AI), and ultimately both components must be successful. Emerging from a model–centric approach, data science tools offer advanced model management capabilities in software that leaves critical data pipelines and production environments.
This discrete architecture relies on other services to process data, the most crucial component of the infrastructure.
The Need for a Data–Centric ML Platform
Before I go any further, I repeat that more Data is not always equivalent to better data. A data–centric Machine Learning (ML) platform delivers data and models and features for business metrics, monitoring, and compliance. It combines them, and doing so is more spartan.
Data is often stored in various business applications and is complex and slow to access. Similarly, organizations no longer have the luxury of waiting for data warehouses to be loaded into data stores like a data warehouse with a predefined schema. On the one hand, aggregate data becomes more valuable as you collect more and more over time. Aggregated data provides the ability to look back in time and see the entire history of an aspect of your business, and discover trends. On the other hand, real–time Data is most valuable the moment it’s captured. Conversely, a newly created or incoming data event allows you to make decisions – at the moment – that can positively impact your ability to reduce risk, serve your customers better, or lower your operating costs.
- From the infrastructure invested in collecting data to the number of dedicated human resources and how rare it might be in ideal cases to collect this Data, it makes Data one of the most expensive assets today. Move to “just in time” and “pay–as–you–go” operating expenses.
- Improve how your entire organization interacts with data. Data should be easily discoverable with default access to users based on their role, prioritizing use cases with similar or adjacent data. For example, if your engineering teams need to make data available for a single–use chance, look for opportunities for engineers to do incremental work to uncover data for adjacent use cases.
- MLOps (Machine Learning (ML) Operations) actively manages a generated model and its task, including its stability and effectiveness. The functionality of the Machine Learning (ML) application through better data, model, and developer operations.
Simply put, MLOps = ModelOps + DataOps + DevOps
- Unified Analytics brings together the disparate worlds of data science and engineering with a common platform, enabling data scientists to explore, visualize and create data while making it easy for data engineers to build data pipelines between silo systems and prepare labeled datasets for model building. Unified Analytics provides a single–engine to prepare high–quality data at scale and iteratively train Machine Learning (ML) models on the same data.
We have identified the issues/distinguished why a Data–Centric platform is needed between the two approaches. Let’s look at the capabilities required to support any organization’s transition to the Data–Centric Approach.
- Data Processing and Management
Because much of the innovation in Machine Learning (ML) happens in open source, support for structured and unstructured data types with available formats and APIs is a prerequisite. The system should also process and manage pipelines for KPIs, model training/inference, goal shifting, testing, and logging. For example, not all pipelines process data might require GPUs, a monitoring pipeline might require streaming, and an inference pipeline might require a low–latency online service.
- Secure Cooperation
Real–world Machine Learning (ML) engineering is a cross–functional effort – comprehensive project management. Access controls play a significant role here, allowing the right groups to work together on data, code, and models in the same place while limiting the risk of human error or abuse.
We know Artificial Intelligence (AI) modes are not deterministic, so it is essential to validate a model’s output by reconstructing its definition (code), inputs (Data), and system environment (dependencies).
For example, suppose a new model is unexpectedly underperforming or biased against a population segment. In that case, organizations should audit the code and data used for feature engineering and training rebuild and redistribute an alternate version.
Documenting a Machine Learning (ML) application scales operational knowledge, reduces the risk of technical debt, and acts as a bulwark against compliance violations. In addition, documentation is an essential feature that brings human judgment and feedback to an Artificial Intelligence (AI) system.
Regular monitoring of the system helps identify and respond to events that threaten its stability and effectiveness. For example, when can it be discovered when a critical pipeline fails, a model becomes obsolete, or a new version causes a memory leak in production?
Ideally, automated testing reduces the possibility of human error and aids compliance. Validation thresholds for training/service skewness and feature and target drift. Models should be tested for baseline accuracy, feature importance, bias, input schema overlaps, and computational efficiency across demographic and geographic segments.