Artificial intelligence (AI) is slowly becoming more mainstream, as companies amass large amounts of data and look for the right technologies to analyze and leverage it. That’s why Gartner predicted that 80% of emerging technologies will have AI foundations by 2021. 

With the trend towards predictive analytics, machine learning and other data sciences already underway, marketers need to start paying attention to how they can leverage these techniques to form a more data-driven marketing strategy. With this in mind, we’ve asked AI industry experts why marketing leaders need to start considering AI, and some of the best open-source AI frameworks to keep tabs on.

How AI Is Changing Business

Dean Abbott, chief data scientist and co-founder of SmarterHQ, believes AI should be top of mind for most business leaders. For marketers, Abbot explained, “AI means integrating more sources of data and using that data to improve the prioritization, personalization and content of marketing campaigns using machine learning algorithms.” 

While many organizations are just starting to understand how AI can fit into their digital strategies, it's quickly becoming a necessity to leverage data effectively. “But the bottom line is this,” Abott stated, “leaders that use AI effectively will supplant leaders who put obstacles in the way of implementing AI because effective AI ultimately will become an integral part of successful, profitable businesses.” 

Here are six innovative open source AI frameworks to keep an eye on in 2020 and beyond.

Related Article: 6 Issues Marketers Need to Consider for Successful AI Implementations

1. TensorFlow

Google’s open-source framework TensorFlow is an ecosystem of tools, libraries and resources that’s used by many popular companies like Airbnb, eBay, DropBox and more. TensorFlow aims to simplify and abstract away the complexity of machine learning algorithms to streamline development. Using visual models and flowgraphs, developers and data scientists can quickly create neural networks and other machine learning models to leverage data. Airbnb, for example, is using TensorFlow to categorize apartment listing photos to ensure they accurately represent a particular space.

2. Amazon SageMaker Neo

Amazon recently open sourced Amazon SageMaker Neo, a feature of its machine learning platform, as a service offering. The newly released Neo-AI project code will enable AI developers to train machine learning models and run them anywhere in the cloud. The Neo-AI project is optimized for edge computing devices and Internet of Things (IoT) sensors that need to make fast and low-latency predictions. 

Learning Opportunities

For example, Pioneer Corp — a company that specializes in digital entertainment products — uses Amazon SageMaker Neo for real-time image detection and classification from cameras within cars. Similarly, Nomura Research Institute (NRI) is using Amazon SageMaker Neo to detect objects within cameras installed in convenient stores, airports and other businesses to optimize operations.

Related Article: 7 Ways Artificial Intelligence is Reinventing Human Resources

3. Scikit-learn

Scikit-learn is a Python-based open-source machine learning library that focuses on data mining and analysis. It’s built atop NumPy, SciPy and matplotlib with a curated set of high-quality machine learning models available for the most popular use-cases. Scikit-learn is used by well-known brands such as Spotify, J.P. Morgan and Evernote for predictive analysis, personalized recommendations and other data-driven tasks.

4. Microsoft Cognitive Toolkit

The Microsoft Cognitive Toolkit (CNTK) is an open-source deep learning framework. CNTK can be included in projects as a library in various languages or used as a standalone machine learning tool through its own model description language called BrainScript. The commercial-grade toolkit is used by Skype, Bing, Cortana and other brands with massive datasets that require a scalable and highly optimized machine learning platform.

5. Theano

Theano is a deep learning Python library that is tightly integrated with NumPy. That means its primary use-case is to define and evaluate complex mathematical expressions using relatively simple Python scripts while leveraging advanced computing to optimize performance. Even so, Theano is considered a low-level framework and many brands choose to use frameworks like Keras or Blocks that are built atop it instead.

6. Keras

Keras is a high-level machine learning API that can run on top of TensorFlow, Microsoft Cognitive Toolkit and Theano. Its ease of use and focus on the developer experience makes Keras the go-to for rapidly prototyping new apps. Many brands like Netflix, Uber and Yelp, as well as smaller startups have integrated Keras into their core products and services. Netflix, for example, has leveraged deep learning to predict customer churn, which is crucial as a subscription-based business. The company has an overwhelming amount of customer data and is able to identify customers they suspect will cancel their service to offer them discounts or other incentives to renew.