Why the explainable AI market is growing rapidly

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Driven by the digital transformation, there seems to be no upper limit to the heights that companies will reach in the next few years. One of the notable technologies helping companies scale these new heights is artificial intelligence (AI). But as AI advances with numerous use cases, there is the ongoing trust issue: AI is still not fully trusted by humans. At best, it’s under intense scrutiny, and we’re still a long way from the human-AI synergy that is the dream of data science and AI professionals.

One of the underlying factors behind this disconnected reality is the complexity of AI. The other is the opaque approach that AI-led projects often take to problem-solving and decision-making. To overcome this challenge, several business leaders looking to build trust in AI have turned to explainable AI models (also known as XAI).

Explainable AI enables IT leaders—particularly data scientists and ML engineers—to query, understand, and characterize model accuracy and ensure transparency in AI-enabled decision making.

Why companies are hopping on the explainable AI train

With the estimated size of the global market for explainable AI, which is set to grow from $3.5 billion in 2020 to $21 billion in 2030, according to a report by ResearchandMarkets, it is evident that more and more companies are now embracing get on the train of explainable AI. Alon Lev, CEO of Israel-based Qwak, a fully managed platform that brings together machine learning (ML) engineering and data operations, said in an interview with VentureBeat that this trend “could be directly related to the new regulations that certain industries are deploying need more transparency about the models predictions.” The growth of explainable AI is based on the need to build trust in AI models, he said.


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He further noted that another growing trend in explainable AI is the use of SHAP values ​​(SHapley Additive exPlanations) – a game-theoretic approach to explaining the outcome of ML models.

“We see that our fintech and healthcare customers are more involved in the topic as they are sometimes legally required to explain why a model made a certain prediction, how the prediction came about and what factors were taken into account. In these specific industries, we are seeing more models with explainable AI built in by default,” he added.

A growing marketplace with tough problems to solve

There is no shortage of startups in the AI ​​and MLops space, with a long list of startups developing MLops solutions including Comet, Iterative.ai, ZenML, Landing AI, Domino Data Lab, Weights and Biases, and others. Qwak is another startup in this space, focused on automating MLops processes and allowing companies to manage models the moment they are integrated into their products.

Aspiring to accelerate MLops potential with a different approach, Domino Data Lab is focused on building on-premises systems to integrate with cloud-based GPUs as part of Nexus – its enterprise-focused initiative being developed in partnership with Nvidia was developed as a launch partner. ZenML itself provides a tooling and infrastructure framework that acts as a standardization layer, allowing data scientists to iterate on promising ideas and build production-ready ML pipelines.

Comet is proud to offer a self-hosted and cloud-based MLops solution that enables data scientists and engineers to track, compare and optimize experiments and models. The goal is to provide insights and data to build more accurate AI models while improving productivity, collaboration, and explainability across teams.

In the world of AI development, the most dangerous path is from prototyping to production. Research has shown that the majority of AI projects never make it to production, with an 87% failure rate in a crowding-out market. However, this in no way means that established companies and startups are not succeeding in riding the wave of AI innovation.

Regarding Qwak’s challenges in delivering its ML and explainable AI solutions to users, Lev said that while Qwak doesn’t create its own ML models, it does provide the tools that enable its customers to use the models they create efficiently to train, adapt, test, monitor and produce . “The challenge we pinpoint is the dependency of data scientists on engineering tasks,” he said.

By shortening the lifespan of model building by removing the underlying drudgery, Lev says Qwak helps both data scientists and engineers continuously deliver ML models and automate the process using its platform.

Qwak’s distinguishing features

In a difficult market environment with various competitors, Lev claims that Qwak is the only MLops/ML engineering platform that covers the entire ML workflow from function creation and data preparation to deploying models in production.

“Our platform is easy to use for both data scientists and engineers, and platform deployment is as easy as a single line of code. The build system standardizes the structure of your project and helps data scientists and ML engineers generate testable and retrainable models. It will also automatically version the code, data and parameters of all models and create ready-to-use artifacts. In addition, the model version tracks differences between multiple versions, preventing data and concept drift.”

Founded in 2021 by Alon Lev (former VP of Data Operations at Payoneer), Yuval Fernbach (former ML Specialist at Amazon), Ran Romano (former Head of Data and ML Engineering at Wix.com) and Lior Penso (former Business Development Manager at IronSource), the team at Qwak claims to have turned the race and approach to preparing the market for explainable AI on its head.

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