Wherever machine learning exists so does the potential for broader artificial intelligence (AI) applications. That’s because machine learning is, in effect, a subset of artificial intelligence development and is often used for the early days of training a model through algorithmic disciplines. As a result, machine learning stocks are small yet potentially critical contributors to the future of AI development. Through their proprietary software, they may become even more valuable than they currently appear to be as large AI-driven companies search for novel technologies to incorporate into their products.

For investors, these three machine learning stocks represent potential opportunities for an AI-driven future 15 to 20 years from now. These are not stocks to invest in for short-term gains as they are relatively niche in their applications and fly under the radar for most analytical firms.

Klaviyo (KVYO)

Focused on making the automation of marketing exceptionally more intelligent, Klaviyo (NYSE:KVYO) might be one of the best-kept secrets among machine learning stocks. While marketing email automation might not seem like cutting-edge AI technology, the data derived from understanding consumer responsiveness to various types of direct marketing could be generally lucrative for the company.

Recently, Klaviyo introduced three additional features to its proprietary algorithmic model, Klaviyo AI. One such feature, Flows AI, enables clients to take advantage of personalized ad campaigns and review audience sentiment. This comes at a critical time for many of Klaviyo’s customers in the retail sector as they have already begun ramping up the preparations for November’s Black Friday deals and the broader holiday season, which is less than six months away.

Now these powerful new offerings from Klaviyo’s AI have turned the company’s product into a marketer’s best tool. That’s because the AI allows marketers to analyze, interpret and implement consumer data to deeply understand their customers. This improves marketers’ ability to generate ideas and execute them.

Ambarella (AMBA)

One of the biggest limitations to the current model of AI training and machine learning is the way these models take in novel information and data. Ambarella (NASDAQ:AMBA) recognizes this contradiction and has dedicated itself to the development of vision-processing technology for AI applications.

While it seems like science fiction, Ambarella’s products enable visual data such as video to be sequentially mapped using grid coordinates and then processed into a data format that AI can understand. This kind of technology will be critical for bridging the gap between robots and the artificial intelligence needed to operate them.

While the company has begun to attract a serious amount of short interest, some analysts remain bullish on its prospects. Some are even curious to see how the fledgling company will manage capital expenses in order to expand technological offerings.

Duolingo (DUOL)

While a green owl that teaches languages might not seem central to the improvement of machine learning stocks, Duolingo (NASDAQ:DUOL) is more than meets the eye. That’s because the average number of acceptable answers to a Duolingo translation exercise is more than 200. In fact, some longer sentences can have as many as 30,000!

That’s an impressive statistic and it’s made possible by the company’s application of machine learning in order to train its translation models on how to interpret a correct translation. With this type of proprietary software, DUOL may become instrumental to the future of machine learning and AI training for its ability to recognize linguistic patterns.

As such, the company could soon open new revenue streams by cooperating with AI companies seeking to create chatbots and large language models that can learn languages rapidly while applying proper grammatical and contextual conventions.

On the date of publication, Viktor Zarev did not hold (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.com Publishing Guidelines.

On the date of publication, the responsible editor did not hold (either directly or indirectly) any positions in the securities mentioned in this article.