Are MLOps, Data-Centric AI, and Synthetic Data the Future of AI?

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What Is MLOps?

MLOps stands for Machine Learning Operations. It is a central part of machine learning engineering, and aims to simplify and automate the process of building machine learning models, deploying them to production, monitoring, and maintaining them over their lifecycle. MLOps is a collaborative function jointly performed by data scientists, data analysts, DevOps engineers, machine learning engineers, and software developers.

MLOps is an organizational pattern that enables organizations to create high quality machine learning and AI solutions. It helps data scientists and ML engineers implement a continuous integration / continuous deployment (CI/CD) approach to ML models, with proper monitoring, validation, and governance. It helps accelerate time to market and enable rapid iteration, development, and deployment of ML models to end users.


What Is Data-Centric AI?

Data-centric AI prioritizes data quality over quantity. This is in contrast to traditional model-driven AI, which took the approach that with enough data, a sufficiently smart algorithm can solve any problem. A data-centric approach can help alleviate many of the challenges that arise when deploying AI infrastructure.

In model-driven AI, the main focus is on developing and improving models and algorithms to achieve better performance for specific tasks. Model-driven AI treats data as a static artifact, and focuses on improving AI models.

Data-driven AI treats data as a dynamic element in an AI project and aims to improve data quality to achieve better results with the same model architecture. Data scientists who take this approach spend a large amount of their time tagging, scaling, managing, and organizing data to enable superior model performance.


What Is Synthetic Data and How Is It Used in Machine Learning?

Synthetic data is information generated artificially by a man-made process, not by real events. Synthetic data is generated algorithmically and can be used to train and validate machine learning (ML) models. It is common knowledge that ML systems need a large amount of data to train, and collecting this data is difficult. Synthetic data is a readily available, cost-effective alternative to production, operational, or observational data.

The benefits of using synthetic data include reducing legal or compliance issues when using sensitive or personal data, and tailoring data requirements to specific conditions where real data is not available.


Their Importance for the Modern AI Organization

Here is an outline of the potential roles of MLOps, synthetic data, and data-centric AI for advanced model development.


MLOps provides a framework to help organizations leverage machine learning to tap new revenue sources, lower operating costs, and save time with efficient data analytics workflows. It enables the automation of AI model building and deployment processes, reducing the time to market and supporting more strategic, agile decision-making.

MLOps helps guide individual developers, managers, and teams when creating an AI model, considering constraints like sensitive data and resource or budget limitations. 

Data-centric AI

AI models depend on data to perform well. Building an AI model should focus on sorting and refining data rather than the algorithm. Data consistency is essential for data processing—businesses can improve it using the following practices:

  • Sorting the labels—various data sections can have inconsistent labeling styles.
  • Familiarizing models with new data—noisy and unfamiliar data not encountered during training can impact data processing capabilities.
  • Refining data sources—it’s important to eliminate unrelated and excessive data sources and create a unified logical data structure.
  • Engineering features—it is possible to introduce new features to the data to improve the processing of input data along with the labels/targets.

Data-centric AI development is an ongoing process that requires the AI model to analyze input data to investigate errors and improve the data rather than the model.

Synthetic data

Computer-generated, synthetic data provides an alternative to real data that helps fill gaps in the input data—especially useful for data-centric AI approaches. Using neural network technologies can provide faster, more cost-effective, and more comprehensive data for model training. Data scientists leverage synthetic data to provide large-scale, realistic datasets.

The artificial nature of this data eliminates privacy issues associated with real-world datasets and ensures clear labeling and pixel information. Teams can build and test systems virtually and iterate quickly with the training data generated on-demand. Synthetic data thus helps data engineers obtain fast insights and maintain a competitive advantage.

Synthetic data is a disruptive introduction to the AI industry, allowing AI developers to test large numbers of iterations early on to help address issues sooner. It also helps ensure compliance with data privacy regulations.


How These 3 Trends are Shaping the Future of AI

Data centric AI is transforming the industry

The data centric AI trend shows that the industry is awakening to the importance of data for AI success. Building and cleaning datasets is hard work, and was previously considered less important than building sophisticated neural network architectures. But in reality, this hard work is the best way to improve model performance. This is why data scientists spend a majority of their time on data, not model optimization.

Because datasets are so important and so time consuming to deal with, they represent a major opportunity for advancement in the AI industry. The two other trends we defined will help turn data refinement from a burden to a blessing:

MLOps will standardize datasets and make them shareable

As organizations build MLOps pipelines, they will also standardize how datasets are built and shared. Today, every dataset uses a different system for labeling data. Enforcing consistent labeling across all machine learning datasets would improve data quality and promote reuse of data across projects where applicable.

Standardization will also enable better organization of data and naturally improve data quality. A small dataset of organized, relevant data will always perform better than a large, low-quality dataset.

MLOps will be instrumental in creating and enforcing these data labeling standards, and ensuring that high quality data is used at every stage of an ML model’s lifecycle. This could mean new skills will develop and new roles will join MLOps teams in the years to come.

Synthetic data will make high quality data more easily available

Synthetic data can have a huge contribution to the availability of high quality data and its standardization. When an organization generates its own data, it can easily specify what data labels are applied and ensure there are no labeling errors. Synthetic data is also a way to scale data without compromising on quality.

Today, generating synthetic data is a complex task that requires specialized expertise, but multiple vendors are joining the space promising to make the process easier. Tools and techniques are becoming available that can allow teams to generate any type of synthetic data—whether tabular data, textual data, and even unstructured data like images and video.


In this article, I introduced three key trends in the AI industry and explained how they will shape the future of AI development:

  • Data centric AI—shifts the focus to the hidden goldmine of AI projects: datasets.
  • MLOps—provides automation and consistent strategies to ease collection and management of data.
  • Synthetic data—provides a potentially unlimited source of data for tomorrow’s ML models.

I hope this will be useful as you take your data science team one step closer to the future AI organization.