Startups

WhyLabs raises $10M for its AI observability platform that helps data teams monitor the health of their AI models  | Tech News | Startups News

Startup News:

Up until two decades ago, machine learning (ML) was a topic of discussion for a few members of academia. At the time, machine learning applications were mostly confined to large corporations or governmental organizations. Today, ML is everywhere. However, building machine learning applications is complex with so many moving parts, ranging from requirements, data gathering and data preparation, exploratory analysis, training, development, deployment, monitoring.

To address the complexity and stream streamline ML application deployment, a new practice has recently emerged, known as MLOps, to bring the best practices of software development to data science and provide an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software.

MLOps, which stands for Machine Learning Model Operationalization Management, is defined as a “practice for collaboration and communication between data scientists and operations professionals to help manage production ML (or deep learning) lifecycle. It’s a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.

MLOps is still an emerging field that started a few years ago as a set of best practices, but it has slowly evolved into an independent approach to ML lifecycle management. Today, a growing number of tech startups and big players are trying to get an early foothold in this space.

Ads

One of the startups in the field is WhyLabs, a Seattle, Washington-based machine learning startup that was spun out of the Allen Institute for Artificial Intelligence (AI2) in 2020. WhyLabs helps data scientists and data teams monitor the health of their AI models and the data pipelines that fuel them. WhyLabs has also built an open-source library called “whylogs” to enable a lightweight data monitoring layer throughout the MLOps pipeline at scale.

RELATED:  Here are the 197 tech startups from Y Combinator's Summer 2020 Batch | Tech News | Startups News

To further bring its AI observability to every AI practitioner, WhyLabs announced today it has closed $10 million in Series A funding co-led by Defy Partners and Andrew Ng’s AI Fund, with participation from existing investors including Madrona Venture Group and Bezos Expeditions. WhyLabs will use the funding proceeds to meet growing demand, expand platform capabilities, scale operations and continue building its world-class team.

Founded in 2019 by Amazon Machine Learning alums Alessya Visnjic, Andy Dang, Maria Karaivanova, and Sam Gracie, WhyLabs AI Observatory is the first observability SaaS solution that enables continuous monitoring of both data and model health and their mutual interactions. Teams rely on WhyLabs to monitor, understand, and improve AI applications without spending precious time on manual tasks. WhyLabs AI Observatory is also the first monitoring solution that AI builders can self-onboard and start using for free.

“AI observability is mission-critical for production ML applications. At WhyLabs we are removing barriers for the adoption of this essential technology,” said Alessya Visnjic, CEO at WhyLabs. “Furthermore, we are defining Observability not just as a set of tools, but as a process and a culture that ML organizations can adopt going forward. And the industry is responding. In just the first week since opening AI Observatory, we saw AI builders from over a dozen organizations onboard the platform. There is a need for these tools and for a community-based approach for building best practices. We are thrilled to be partnering with industry experts and leading the movement that will make AI Observability a ubiquitous part of every production ML stack.”

RELATED:  Ethereum infrastructure startup Polygon joined forces with Wanchain to usher in a new era of blockchain interoperability | Tech News | Startups News

In talking with over 200 data science teams, WhyLabs discovered that the most forward-thinking teams take a two-pronged approach to operating and maintaining their models – they focus on tracking both data health and model health. Today, the universally accepted truth is that enterprises in possession of ML applications must be in want of an observability platform to keep their models from failing catastrophically.

By using WhyLabs to automate the monitoring of ML and data pipelines, teams can dramatically reduce manual operations, accelerate the time-to-resolution of model failures, and focus on shipping reliable AI-powered solutions faster. Organizations, ranging from AI-first startups to Fortune 500 companies, rely on WhyLabs to establish observability across data and model pipelines. Customers come from industries representing fintech, logistics, manufacturing, healthcare, martech, retail, e-commerce, and real estate.

“ML engineers need better tools to ensure high-quality data through all stages of an ML project’s lifecycle,” said Andrew Ng, Managing General Partner at the AI Fund. “AI Fund is excited to support WhyLabs, whose open-source logging library and AI observability platform makes it easy for developers to maintain real-time logs and monitor ML deployments.”

“WhyLabs is in a unique position to transform how AI is governed and MLOps is managed by any enterprise with the rapid adoption of its observability platform and data logging library,” said Neil Sequeira, founder and partner at Defy Partners who joined the WhyLabs Board of Directors. “They have built what is effectively a control center for operating AI applications. In turn, their technology has a meaningful positive impact on intelligent application builders and the hundreds of millions of people touched by AI every day.”

RELATED:  Crypto: A New Asset Class? Goldman Sachs sees Ethereum overtaking Bitcoin | Tech News | Startups News

Going forward, WhyLabs said its team continues to execute an ambitious product roadmap with many new features launching in the coming months. From purpose-build image and NLP observability, to proactive identification of model deficiencies, to integrations with real-time and on-device ML applications.


Tech News Today Latest Technology Headlines & Trends Link Below

News Post || Technology News || News Headlines || World News || US Headlines || Health || Education News

Source

Tags
Show More

Related Articles

Back to top button
Close