Data Science for the Enterprise

A Video Guide to Getting Started with AI/ML

Learn the what, why, and how of data science in under 30 minutes!

Enterprise data science focuses on utilizing data-driven approaches and advanced analytics to solve complex business problems, drive innovation, and gain a competitive edge in the market. It involves combining technical expertise, domain knowledge, and business acumen to extract actionable insights and make informed decisions.

Discover how artificial intelligence and machine learning can be applied to the enterprise to assist your organization in your data science journey.

In this video, we’ll cover:

  • What are AI and machine learning

  • What enterprise data science is, why you should use it, and how it works

  • 2 real-world data science use cases

Watch the video now!

data science video

Don’t have time for a video, but want to hit the highlights? Here’s a brief overview of what is discussed:

This session explores the role of artificial intelligence (AI), machine learning (ML), and data science in transforming enterprise operations. The discussion highlights the key elements of AI, defined as the simulation of human intelligence in machines, and ML, a subset of AI that uses historical data to predict outcomes and inform decisions.

AI and ML go beyond traditional descriptive analytics by enabling predictive models and prescriptive insights, driving better decision-making across organizations. There’s a growing adoption of AI, with approximately 50% of companies implementing some form of AI by 2022.

A core aspect of enterprise AI involves structuring and analyzing complex data sets, often using advanced analytics and ML techniques. The integration of these technologies helps automate insights and streamline business processes, reducing friction and enhancing scalability.

In the video, Rajeev further explains the machine learning lifecycle, which includes model training, validation, deployment, and monitoring. In enterprise settings, the ML lifecycle is part of a broader MLOps framework that incorporates tools like GitHub and Azure DevOps to ensure consistency, governance, and collaboration between data scientists and developers.

Two use cases we highlight:

  • A quick-service restaurant chain using AI to identify at-risk stores based on financial data, customer feedback, and future potential, allowing leadership to prioritize attention to struggling locations.

  • A construction company leveraging machine learning to predict the likelihood of winning bids, optimizing their focus on high-probability opportunities.