In today's rapidly advancing world of artificial intelligence, the demand for highly sophisticated deep learning models has surged. These neural networks are incredibly capable, but they come with a significant caveat – they're insatiably hungry for data. The sheer amount of data needed to train these models has become a major obstacle for many practical applications. The traditional approach of manually curating massive datasets is not only labor-intensive but also often unfeasible.
However, there's a new approach in the AI field that's gaining momentum and shifting the focus away from the models themselves to the data they rely on. It's called data-centric AI, and it's poised to play a pivotal role in shaping the next wave of AI advancements. In this blog post, we'll explore what data-centric AI is, why it's important, and how it's transforming the AI landscape. Welcome to the exciting world of AI where data takes center stage.
What is data-centric AI?
For years, AI development revolved around models – iterating on model architectures, algorithms, and other parameters to optimize performance. Data was treated more as a fixed input to feed into models. This model-centric approach led to many breakthroughs, but also limitations as models struggled with data hunger and brittleness.
Within a data centric paradigm, instead of treating data as a fixed input, you collaborate with experts to:
- deeply understand it,
- inject knowledge into it, and
- rapidly iterate on it to solve problems.
Data becomes the central focus of iterative development, the key interface and asset rather than models. More time is spent on tasks like labeling, curating, managing, and augmenting data. With smart data programming, weak supervision, and other techniques, small datasets can be radically expanded to train even the most data-hungry models. And direct collaboration with subject matter experts allows their domain knowledge to be injected into models.
The rise of deep learning has accelerated the shift to data centric AI. Today's models have hundreds of millions or even billions of parameters to train – requiring massive datasets that are impractical to collect and label manually. A data centric approach provides a path forward.
Data-centric AI vs model-centric AI
To understand data centric AI, it helps to contrast it with the previous model-centric paradigm:
- Model-centric AI – in the model-centric approach most development time is spent on experimental research to improve the machine learning model performance. The model itself is developed via tasks like feature engineering, architecture design, improving training process, and tweaking algorithm selection. Data is more of a fixed input to feed into whatever model is developed,
- Data-centric AI – more time and attention goes into systematic work on datasets, i.e. managing, labeling, cleansing, and augmenting data to increase the accuracy and performance of machine learning applications. The model architecture may be more standardized or even commoditized. Data tasks become the primary driver of progress.
Put simply, data centric AI puts data at the heart of the development lifecycle rather than treating it as an afterthought. Instead of just building models and feeding them data, you collaborate with experts to deeply understand the data, inject knowledge into it, and rapidly iterate on it to solve problems.
Benefits of data-centric AI
Adopting data centric methods unlocks many advantages, including:
- Better model accuracy – rather than endlessly tweak model architectures, data centricity focuses on the root source of model intelligence – the training data itself. Higher quality data with less noise inherently improves model learning and generalization. Specific techniques like data programming inject subject matter expertise directly into data, outperforming models reliant on purely statistical patterns.
An application in autonomous driving systems illustrates this in detail; by refining the training data to better represent real-world driving conditions, these systems can achieve higher accuracy in decision-making and situational awareness.
- Rapid retraining – real-world systems see constantly shifting conditions over time. In a data centric approach, updating the training data allows rapid retraining of models to handle new scenarios. This agility would be difficult in traditional model-centric paradigms requiring extensive re-architecting of models. Programmatic labeling functions provide an efficient way to generate new training labels rather than manually adding them.
For instance, in fraud detection, as fraudulent techniques evolve, a data-centric artificial intelligence can be quickly retrained with updated data, maintaining its effectiveness in detecting new types of fraudulent activities.
- Lower costs – manually generating training data simply does not scale for modern data-hungry models needing millions or billions of labels. Data programming provides a customizable software platform to generate labels programmatically, replacing armies of human labelers with far lower computational costs. Weak supervision techniques further mix and denoise these programmatic labels to minimize costs of data centric AI.
- Increased scalability – this approach significantly eases the scalability challenges often faced in AI development. By emphasizing data quality and efficiency, organizations can scale their artificial intelligence solutions without the exponential increase in complexity and cost typically associated with scaling model-centric AI.
In healthcare, for example, a data centric AI system can be scaled to accommodate larger patient datasets and diverse medical conditions, enhancing the system's utility across different healthcare settings.
- Enhanced adaptability to emerging trends – data centric AI facilitates a more dynamic response to evolving market trends and consumer behaviors. By centering its focus on data, it enables AI models to quickly align with new patterns and environmental changes, thereby ensuring they remain relevant and effective in a rapidly changing business landscape.
Take inventory management as a practical example. Here, data centric AI proves instrumental in dissecting and understanding intricate patterns of customer purchases, seasonal variances, and the dependability of suppliers. This analysis is key to fine-tuning inventory levels, creating a system that's agile and responsive. Such a system adeptly adjusts inventory in real-time, adeptly navigating challenges like sudden shifts in market demand or disruptions in the supply chain. The goal is to maintain an optimal balance, avoiding both excess stock and shortages, ensuring the smooth operation of the supply chain even under unexpected circumstances.
Organizations from various industries have achieved dramatic benefits from data centric AI, including 45x faster development cycles and 25% accuracy gains on business-critical applications. As models become increasingly commoditized, high-quality data is often the key differentiator.
Challenges of data-centric AI
While promising, scaling data centric AI has hurdles. The most serious challenges include:
- Data collection – obtaining real-world data at the volume required for modern models presents practical hurdles around access, privacy, and more.
Consider a bank wanting to analyze transaction text data to improve fraud detection. While immense data exists internally, access may be restricted due to privacy policies and internal silos. Even with access, transferring raw data from operational databases into formats usable for AI presents an engineering challenge.
- Labeling effort – manually labeling enough data to train deep learning models is infeasible in most real-world settings. Moreover, involving subject matter experts in the data process is crucial but challenging due to their other pressing commitments.
Imagine a hospital wants to leverage radiology scans to diagnose rare cancers early. Expert radiologists needed to label the scans have immense domain knowledge, but little time for manual labeling amidst patient responsibilities. Forcing them to leave their workflow creates huge friction.
- Changing distributions – real-world data distributions often shift dynamically, requiring models to be continually monitored and retrained.
For example, an insurance firm develops AI for auto claims handling using past claims data. But as more self-driving cars reach roads, the data distribution will shift seismically. Without monitoring these shifts, model accuracy may silently decay over time.
Key principles of data-centric AI development
Data centric approaches aim to solve these challenges through the core principles instantiated in various techniques and platforms. However, any data centric solution will likely embody elements of all the ideals in some form.
Consider data as the central development object
In contrast to model-centric AI, where data is a static input, data centric AI positions data at the center of the development lifecycle. This paradigm shift involves keeping the model architecture relatively more fixed, while spending more time on:
- augmenting, and
- curating data.
In a medical AI application, instead of iterating on the AI model to improve diagnosis accuracy, more effort is devoted to refining and expanding the medical data set, ensuring it is comprehensive and accurately labeled.
Prioritize data quality and diversity
The essence of a data-centric approach lies in its steadfast commitment to ensuring both the quality and diversity of training data. This focus is crucial as it significantly influences the performance of AI models. A prime example of this can be seen in facial recognition technology. Here, the range and quality of facial data are vital as they directly affect the technology’s capability to accurately identify faces from various demographic groups.
Moreover, integrating a variety of data sources is a fundamental principle of this approach. This integration encompasses a wide array of sources, such as:
- existing models,
- knowledge bases, and
This approach serves to deepen and enhance the training dataset. An example of this is evident in financial fraud detection systems, where the combination of transactional data with behavioral patterns and historical fraud data significantly bolsters the system's effectiveness in identifying fraudulent activities.
Focus on data iteration
Data-centric AI is defined by its agile and iterative approach to refining data, reflecting the ever-changing nature of real-world scenarios. For instance, in the context of an online retail AI system, there is a continuous process of refining customer behavior data. This ongoing refinement is key to improving the personalization of product recommendations, making them more relevant and tailored to individual preferences.
Manage data efficiently through automation
The vastness and complexity of data in contemporary AI necessitate an automated, programmatic approach. This efficiency is critical for managing large-scale, sophisticated machine learning models. Programmatic labeling helps to avoid slow and expensive manual labor. Take a software engineering approach to programmatically generate labels at scale, such as via:
- domain expert heuristics,
- knowledge bases, and
A practical example is seen in language translation AI, where automated data labeling and augmentation significantly expedite the processing of extensive linguistic datasets.
Integrate expert insights
In the context of a climate modeling AI, involving climatologists in the data preparation process ensures that the dataset accurately reflects complex environmental variables.
Working closely with subject matter experts throughout the process rather than only soliciting data labels is essential for data centric artificial intelligence. Their extensive and nuanced knowledge in specific areas plays a critical role in accurately labeling and curating data. This precision in handling data is key to maintaining its relevance and ensuring its accuracy. Let’s now turn our attention to determining the appropriate tools that will most efficiently enable us to implement the leading practices of data centric AI development.
AI development with Snorkel AI and Snowflake
Snorkel AI provides a leading data centric AI platform achieving dramatic results across industries like banking, manufacturing, and medicine.
Spun out of pioneering research at Stanford AI Lab, Snorkel AI's core breakthrough was tackling the bottleneck of manual labeling. Their Snorkel Flow platform allows subject matter experts to create flexible labeling functions just by writing code or rules. These programmatic labeling functions directly inject domain expertise into data without intensive manual labeling.
Snorkel Flow’s modeling backend then combines potentially hundreds of noisy labeling functions into training datasets of remarkable quality. Rather than brigades of labelers, development accelerates by orders of magnitude.
Integrations with partner platforms like Snowflake provide data management capabilities to harness raw data at scale. Snowflake, launched in 2014, stands out with its unique architecture that separates storage and compute into three layers –
- efficient data storage,
- scalable virtual warehouses for computing, and
- cloud services for platform synchronization.
Operating on major cloud infrastructures like AWS, Azure, and Google Cloud, Snowflake's design allows for independent scaling to manage user activities and data volumes efficiently. Its self-managing features handle hardware needs, enabling users to focus on data analysis.
Ideal for diverse data types and workloads, Snowflake supports standard SQL, offers a marketplace through its Data Exchange, and ensures enterprise-grade security. Its user-friendly web interface, unique data time travel feature, and automated resource optimization, coupled with cost-effective billing, make it a strong partner for Snorkel AI in data-centric AI development.
Need help with all-things Snowflake?
Interested in leveraging Snowflake's data cloud for implementing data centric AI in your organization? Get in touch with RST Software to see how we can help accelerate your analytics and data science initiatives. Our team of experts can help you set up and optimize Snowflake, develop applications like Snorkel AI on top of it, and provide ongoing management and support. We tailor solutions to your unique needs, allowing you to focus on core business objectives while we handle technical complexities.