Nearly a year ago, we published our thesis on machine learning (ML) observability and announced our Series A investment in Arize*. Today, we’re thrilled to share that Arize has raised a $38M Series B that was led by TCV, and we’re excited to continue supporting Jason, Aparna and the entire Arize team as they scale their ML observability platform.

Artificial intelligence (AI) is going mainstream, and IDC projects that global spend on AI will more than double over the next four years from $85 billion in 2021 to more than $200 billion by 2025. This growth is tied to companies across a wide range of industries and end markets that are continuing to invest in building out the AI / ML systems and infrastructure needed to translate raw data into actionable intelligence.

Indeed, many enterprises that aren’t investing in the necessary AI / ML infrastructure are continuing to struggle to see concrete benefits from AI. A recent survey by Arize of data scientists, engineers and executives found that nearly 88% of teams said their business executives can’t quantify the ROI of ML initiatives “at least some of the time.” And nearly 49% of the data scientists surveyed said their jobs are more difficult in the wake of Covid-19—which triggered abrupt and significant shifts in corporate work setups and customer behaviors–due to elevated drift, data quality and performance issues. We think Arize’s technology can help close some of these gaps and help companies improve productivity and boost revenue.

Companies such as Wayfair*, Home Depot and Etsy have talked publicly about the benefits they’re seeing as a result of leveraging AI to power critical pieces of their day-to-day operations, including being able to deliver a better customer experience, boost productivity and make more intelligent, data-driven decisions. We expect AI adoption to continue to expand as ongoing growth in data volume, as well as variety, increasingly requires AI to analyze and interpret large amounts of data. In addition, access to cloud computing power and storage, coupled with advancements across the AI / ML toolchain, continues to democratize access to advanced analytics and predictive modeling.

Most ML teams have made significant investments across the pre-production phase of the ML workflow (labeling, training, deployment) but are blind when it comes to understanding, troubleshooting and improving the performance of models once they are deployed into production. Further, ~80% to ~90% of data today is unstructured, including images, video, text and voice data, giving rise to more complex deep-learning models, which add another layer of difficulty when it comes to understanding model behavior and diagnosing potential model problems.

Arize empowers ML teams to detect, troubleshoot and eliminate ML model issues faster, providing the missing link to reliably scaling AI / ML implementation and adoption. Using Arize, ML practitioners can quickly catch model and data issues, diagnose root cause and continuously improve their model’s performance. Arize’s product is already being used by teams at top ML companies, including Spotify, Instacart, P&G, Stitch Fix, Ebay, New York Life and Uber.

We are excited to continue our partnership with Arize, and we look forward to helping to fuel the company’s next chapter of growth.