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Open source is enabling developers to create new AI-based solutions.

As we look across the most rapidly transforming industries like financial services, healthcare, retail – and now advertising, developers are putting open source technologies to work to deliver next-generation features. Our enterprise clients are looking for AI solutions that will scale with trust and transparency to solve business problems. At IBM®, I have the pleasure of focusing on equipping you, the developers, with the capabilities you need to meet the heightened expectations you face at work each day.

We’re empowering open source developers to drive the critical transformation to AI in advertising. For instance, at the IBM Center for Open source Data and AI Technologies (CODAIT), enterprise developers can find open source starting points to tackle some of your thorniest challenges. We’re making it easy for developers to use and create open source AI models that can ultimately help brand marketers go deeper with AI to reach consumers more effectively. Here are a few examples:

  • For developers who want to use free, open source, state-of-the-art deep learning models for common application domains, such as text, image, audio, and video processing, the Model Asset eXchange (MAX) on IBM Developer is a great place to start.
  • The IBM Data Asset eXchange (DAX) is an online hub for developers and data scientists to find free and open data sets under open data licenses.
  • For data scientists who are looking to be more effective in the Jupyter Ecosystem, in 2020, IBM released Elyra, a set of open source AI-centric extensions to Jupyter Notebooks to help evolve the Jupyter Ecosystem with new tools for AI model development.

As marketing and media are undergoing a major transformation, developers can help advertisers shift from reacting to predicting using AI. IBM recently added new capabilities to our growing IBM Watson® Advertising suite, which leverages AI to help clients predict the optimal combination of visual elements to drive the highest engagement for a given audience, and also helps advertisers better understand the composition and preferences of their audience to inform future strategy. For developers, IBM Watson Advertising’s capabilities connect to our CODAIT projects in several ways:

  • IBM Watson Advertising Predictive Audiences: This solution enables brand marketers and advertisers to go deeper with AI to reach consumers who might look nothing alike but exhibit similar behavior. Performing audience prediction requires an end-to-end data science workflow, likely involving extensive use of Jupyter Notebooks. The Elyra JupyterLab extension recently introduced the notebook pipelines visual editor, which you can use to create and run pipelines without any coding.
  • IBM Watson Advertising Social Targeting with Influential: Natural language processing capabilities are heavily used to generate a clear understanding of social media posts and text. To see an example of a natural language processing workflow, check out this set of Jupyter Notebooks analyzing the latest COVID data, created by CODAIT earlier this year. IBM CODAIT is also looking to help data scientists improve natural language processing workflows through a project called Text Extensions for Pandas.
  • IBM Watson Advertising Weather Targeting: For developers who want to make weather predictions using open source, the Model Asset Exchange provides ready-trained models that you can quickly deploy on IBM Cloud/OpenShift and integrate with your application:

Developers can use these tools and more to build the next generation of AI advertising technology. At CODAIT, we’re making open source AI models dramatically easier to create, deploy, and manage in the enterprise. CODAIT also helps maintain projects created by IBM Research that can increase fairness, explainability, robustness, and transparency in machine learning. This includes open source packages like the AI Fairness 360 Toolkit (AIF360), which can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle. IBM Research working with CODAIT has donated three Trusted AI projects to Linux Foundation AI to help grow the ecosystem and drive innovation.

I invite you to check out our open source offerings, and build within our ecosystem.