Cloud Stocks: Cloudera Focusing On Machine Learning For Verticals

Sramana Mitra
4 min readApr 8, 2021

I’m publishing this series to discuss a topic that I follow closely — cloud stocks, trends, strategy, acquisitions, and more. I like fundamentals-focused business building, and outline the principles of fundamentals-focused business building in my free Bootstrapping course.

According to a recent report, the global Hadoop-as-a-Service Market was estimated to grow from $7.4 billion in 2019 to $74.8 billion by 2026 driven by a demand for cost-efficient solutions for management of big data. Hadoop’s ability to handle large volumes of data efficiently and in a cost-effective manner is accelerating the growth of the service. Cloudera (NYSE: CLDR), a leading player in the market, reported strong results for the fourth quarter.

Cloudera’s Financials

Cloudera’s fourth quarter revenues grew 7% to $226.6 million, significantly ahead of the market’s forecast of $221.42 million. GAAP net loss was $0.18 per share compared with a net loss of $0.22 per share a year ago. Non GAAP net income was $0.15 per share, which was better than the market’s forecast of an income of $0.11 per share.

During the quarter, subscription revenues grew 13.7% to $206.8 million and services revenues fell 26.2% to $19.8 million.

For the fiscal year, Cloudera reported revenues of $869.3 million, up 9% and an EPS of $0.45.

For the first quarter, Cloudera forecast revenues of $216-$218 million with an earnings forecast ranging from an income of $0.07-$0.09. It expects to end the current year with revenues of $907-$927 million and a net income of $0.35-$0.39 per share. The market was looking for revenues of $229.05 million for the quarter with a net income of $0.10 per share and revenues of $948.07 million for the year with a net income of $0.44 per share.

Cloudera’s ML Focus

During the quarter, Cloudera announced the availability of the Cloudera Data Platform (CDP) on Google Cloud. The platform is a hybrid and multi-cloud data and analytics platform, that provides security and governance for large enterprises. The addition onto Google Cloud provides Cloudera the ability to deliver an enterprise data platform on a global scale. CDP on Google Cloud takes Cloudera’s Shared Data Experience and creates secure data lakes in the company’s Google Cloud account in minutes. Using the new solution, companies will be able to take in data from several new or existing data sources.

Earlier this quarter, Cloudera released Applied ML Prototypes (AMPs), which are a new way of developing and shipping enterprise machine learning (ML) use cases. AMPs provide complete ML projects that can be deployed with a single click from Cloudera Machine Learning. By offering an end-to-end framework for building, deploying, and monitoring business-ready ML applications, data scientists will be able to accelerate the development of a fully working ML use case. Cloudera has currently released 10 AMPs so far. These AMPs will be industry-specific, use-case-specific, and application-specific to help accelerate the development of ML tools.

Cloudera is trying to make it big in the ML space. In a recent Gartner magic quadrant for the Data Science and Machine Learning segment, Cloudera was ranked as a niche player for its ability to support complex data workloads and its offering of metadata support for DataOps and MLOps. However, Cloudera still lacks GUI for development and domain specific solutions. The new AMPs are expected to help it plug that gap. Gartner believes that the market is still nascent with plenty of revenue and funding opportunities available for companies that differentiate themselves on the product side. Cloudera can cash in on the market with its ML-focused efforts.

Its stock is trading at $12.54 with a market capitalization of $3.7 billion. The stock had fallen to a year low of $7.19 in April last year. The stock had climbed to a year high of $19.35 in February.

Disclosure: All investors should make their own assessments based on their own research, informed interpretations, and risk appetite. This article expresses my own opinions based on my own research of product-market fit, channel execution, and other factors. My primary interest is in product strategy. While this may have bearing on stock movements, my writings tend to focus on long-term implications. The information presented is illustrative and educational, but should not be regarded as a complete analysis nor recommendation to buy or sell the securities mentioned herein. I am not a registered investment adviser and I am not receiving compensation for this article.

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