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Essential Data-Centric AI Tools for 2022

With the rise of AI, specifically Machine Learning (ML), the number of tools and frameworks accessible to data scientists and developers is expanding and ever-changing. While ML is changing the world, the world of ML is changing, too. As the practice moves from model-centric to data-centric, data scientists require new tools to extract meaning and value from data.

The most prevalent tools in the field can assist with data labeling in AI training, synthetic data generation needed for QA testing and privacy, ML performance monitoring, and ease of deployment. We’ve gathered a list of the top data-centric AI tools gaining popularity on LinkedIn and several forums within the greater AI community. These tools enable the transition from model-centric AI toward data-centric AI development.

Data Labeling Tools Help With Data-Centric AI Training

Data Scientists use data labeling during the preprocessing stage and throughout the life of the model to improve and adapt as changes are made. Data labeling requires taking raw data, enriching it with a layer of information relevant to the predictions a model is learning to generate.

Accurate models require accurate labels. So, data labeling tools should be flexible to accommodate various types of data, facilitate quality assurance, and provide analytics that improve annotation results, just to name a few areas. Data labeling tools should incorporate security and access restrictions because most internal data is sensitive.

It’s also important to maintain consistency among labelers and batches when labeling datasets. Internal labeling teams—i.e., humans—are ideally equipped for quality since it requires domain expertise. Data labeling tools can be automated to assist this particularly tedious labeling process.

Label Studio Community

What is it: Label Studio Community Edition is the leading open source data labeling software. Label Studio supports a variety of datatypes and modeling use cases and is highly customizable to accommodate individual project requirements and workflows. More than 20,000 data scientists worldwide use Label Studio for data science and machine learning projects.

Why you should use it: Label Studio Community Edition is easy to install, customize, and use for a variety of datatypes and use cases, including NLP, audio and speech recognition, images, videos, time-series data, and more. Small annotation teams and individual data scientists use Label Studio to annotate samples for model training, to generate label predictions (pre-labels), and for continues active learning.

Label Studio has a robust template library. Label Studio templates are pre-defined labeling interfaces for certain use cases and data types, including natural language processing, audio/speech processing, and conversational AI. You can use any template to get started with a labeling project, or customize it for a more specific labeling use case.

Label Studio Enterprise by Heartex

What is it: Heartex, the company behind the open source Label Studio software offers commercial editions with expanded capabilities for larger teams looking to scale their data labeling processes. Heartex Enterprise extends the open source project by including quality management, data security, access controls, and analytics capabilities. 

Why you should use it: Label Studio Enterprise is ideal for teams that are scaling their data annotation capabilities and require role-based access controls, built-in quality assurance, and reporting and analytics. Teams using the open source project are able to easily upgrade their projects to Label Studio Enterprise to leverage the more advanced features. Label Studio Projects and Workspaces and automated task assignments keep teams organized and data labeling processes efficient. Role Based Access Controls (RBAC) enable different stakeholders to access Label Studio features and data based on their role. Quality assurance features and management reports ensure high-quality annotations and consistent labeling practices across the annotation team.

Synthetic Data Generation Tools for QA Testing and Privacy

Synthetic data generation is artificial data created to maintain privacy, test systems, or produce training data for machine learning algorithms. Unlike manual data generation tools, which generate fake data, ML-based tools allow you to generate synthetic data. Synthetic data is used to safeguard a dataset's privacy and confidentiality. On the other hand, fake data is created at random to appear to be real data, yet it might give erroneous and unverified information. Businesses need synthetic data tools to assist with these three areas: privacy, product testing, and training machine learning algorithms.

The Synthetic Data Vault

What is it: The Synthetic Data Vault (SDV) is considered a leader with numerous projects, research, and tutorials. SDV is a software ecosystem for creating synthetic data that is open source, within the academic field and open-source community.

Why you should use it: SDV offers a synthetic data generation library ecosystem that enables users to study single-table, multi-table, and timeseries datasets quickly. You can use the datasets to create synthetic data with the same structure and statistical characteristics as an original dataset. 

One thing to keep in mind is that SDV models require a large dataset to train on. As a result, the model can provide a useful dataset that accurately depicts the real process but may be pricey in terms of hardware.


What is it:Tonic mimics safe, realistic, and de-identified data for QA, testing, and analysis by simulating production data.

Why you should use it: Tonic's synthetic data platform provides developers with the information they need to create high-quality solutions while maintaining compliance and security. It allows teams to cut development time, minimize costly data pipeline overhead, and mathematically ensure data privacy.

For example: In eCommerce, a shopper’s transaction history could reveal identifying details you may not want to share with data engineers or analysts within the organization. Tonic would take the original payment data and turn it into a new, smaller dataset with the same statistical qualities as the original data, but without the original consumer’s private data. While protecting user information, Tonic is able to provide concise data to an engineer for application testing or an analyst for testing a marketing campaign.

Tools That Provide Actionable Insights to Improve ML Performance Monitoring

ML performance monitoring tools track and assess data performance and provide information for debugging if something goes wrong. The need for performance monitoring has increased as ML infrastructure has grown because the model's performance against the data degrades over time. Model performance can be affected by the static model or the training data being too old.

One of the key aspects of ML performance monitoring is observability. Observability is vital because it delivers the raw, granular data needed to comprehend and see how the model performs.

Fiddler AI

What is it: Fiddler AI is a next-generation Explainable AI Engine that enables data science, product, and business users to understand, evaluate, validate, and manage their AI solutions. Their AI engine provides actionable insights for ML monitoring, observability, and explainability. The types of insights Fiddler AI offers are data drift, data integrity, outliers, performance, and bias. These insights ensure users have a transparent and dependable experience.

Why you should use it: Through automated monitoring and observability, Fiddler AI manages model performance more effectively by receiving alerts for data drift, biases, and performance declines, allowing for the creation of innovative AI-powered consumer interactions. 

With their Explainable AI Engine, you can track and reduce customer churn by showcasing insights as to why a customer may be likely to churn.


What is it: Arthur is an AI performance startup that helps businesses realize the full potential of AI. They achieve this by providing ML monitoring and performance optimization through the combination of explainability, observability, and bias mitigation. 

Why you should use it: Arthur lets you track a model's performance using any metrics to proactively uncover opportunities to improve model performance. It can also help teams spot data drift before it causes havoc with predictions. 

One of the areas where it's well suited is the medical field, providing explainability and bias monitoring for healthcare logistic models for capacity planning or facility management.

ML Deployment Tools That Connect Operations and Development

Data engineers and data scientists are always on the lookout for new tools and ways to deploy their ML models to production. ML deployment tools enhance security by setting controls and ensuring that all systems are protected. Automation has the extra benefit of making deployment more efficient, quicker, and with fewer errors, to mention a few benefits.

Amazon SageMaker

What it is: Amazon SageMaker lets developers or data scientists quickly construct, train, and deploy ML models. It’s a part of the Amazon Web Services (AWS) ecosystem built on the two decades of experience Amazon has in developing real-world ML applications. 

Why you should use it: Through automation and established MLOps techniques, Amazon SageMaker streamlines the ML lifecycle. After models have been built and trained, Amazon SageMaker provides three options for deploying them and generating predictions.


What is it: Algorithmia automates ML deployment and improves cooperation between operations and development. 

Why you should use it: It extends the capabilities of existing Software Development Life Cycle (SDLC) and Continuous Integration/Continuous Delivery (CI/CD) systems and provides superior security and governance. All stages of the machine learning lifecycle coordinate inside of current operational procedures.

Putting the Right Data-Centric AI Tools in Your Toolbox

Choosing the right tools today can help address your current and future needs as your machine learning model use adapts and changes in your organization. Embracing a data-centric AI model strategy and utilizing tools that emphasize flexibility and obtaining high-quality data is imperative. Your data should reflect the data you anticipate seeing during deployment, accounting for any possible changes. You may want to use synthetic data to account for rare edge cases that aren't represented in your training data but may want to consider.

Heartex is open source and community-minded. We work to create the best experience in your data-centric AI modeling to power up your data labeling team. With Label Studio, you can access a wide range of features for setup, managing, running, and monitoring your collaborative data labeling projects. Schedule a product demo today to learn more about how Heartex can enhance your projects.