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Keys to Successful AI Solutions

Faisal Akkawi, faculty director of Northwestern University’s Master of Science in Information Systems program, highlights the school’s recently announced AI specializations within its Master of Science in Data Science and the Master of Science in Information Systems programs.

Artificial intelligence (AI) has many important business applications, including computer vision, natural language processing for translation and transcribing of text-to-speech and speech-to-text, as well as software robotics enabling autonomous processes. These applications and AI solutions are seen across all industries.

AI solutions
To be successful in implementing AI solutions and to take leadership roles within various industries, students need to develop technical skills in machine learning, data management, and cloud computing. Image: Shutterstock/ by metamorworks)

Recent breakthroughs in AI are largely a result of an exponential growth in available data, as well as increased processing power of computers—both of which are prerequisites to building neural networks for deep learning. To be successful in implementing AI solutions and to take leadership roles within various industries, students need to develop technical skills in machine learning, data management, and cloud computing. 

To address the growing need for AI expertise in industry, Northwestern University School of Professional Studies recently announced AI specializations within its Master of Science in Data Science and the Master of Science in Information Systems programs, as well as an AI certificate program. The AI specializations at Northwestern cover the three pillars of AI: large data sets for training models (big data), computing power (cloud-based computing), and efficient deep learning algorithms. 

  • Big Data. Data science projects can succeed or fail based on the quality of data. Data scientists often spend 80 percent or more of their time gathering and preparing data for input to models. What originally started out as small data sets with dozens of columns and thousands rows of structured data, perhaps analyzed in an Excel file on a local machine, has evolved to become databases with thousands of columns and millions of rows. As part of their normal business operations, many organizations also rely on petabytes or exabytes of unstructured data, including images, text, and video.
  • Cloud–Based Computing.Cloud computing gives organizations an ability to grow their computing infrastructure as their needs for data storage grow. Cloud-based resources accommodate the processing demands of batch, interactive, and streaming data.  It has become ever more important to leverage technologies available in the cloud to store, analyze, and engineer data for machine learning. Compared with local data centers, cloud-based resources have lower costs and allow for auto-scaling. Clouds can also provide prebuilt frameworks for machine learning and artificial intelligence, solutions applicable to a wide-variety of business applications. 
  • Algorithms.As the volume and variety of data increase, it becomes ever more important to implement efficient data science methods and algorithms, Students need to choose the right set of tools for each research project and application. They need to match data with methods and algorithms. They need to match methods and algorithms with business applications and needs. Northwestern University offers advanced courses in data science and AI for students interested in learning about methods and algorithms, as well as application development, programming, and systems engineering skills.  

For more information about the Master of Science in Data Science and the Master of Science in Information Systems programs, visit the Northwestern University School of Professional Studies website.

Faisal Akkawi is the faculty director of Northwestern University’s Master of Science in Information Systems program. 

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