First, let's start with some definitions. Machine Learning (ML) is a type of AI which automates analytical model building by allowing software to learn from historical data, identify patterns, and make predictions and decisions with little to no human guidance. AutoML (automated machine learning) refers to the tools and processes which make it easier to build, train, deploy and serve custom ML models.
Data analytics and machine learning
ML models are the basis for most augmented analytics capabilities. They’re the source for insight recommendations and conversational, search-based analytics. They’re also the engine for cleaning and prepping the data, reducing errors and saving countless hours of manual work in curating tables, text, images and videos.
The best BI and data analytics platforms go a step further by integrating a robust AutoML capability into their solution. This allows you to build custom ML models and then explore interactive visualizations and dashboards with predictive insights based on the models you develop.
Human intuition is still required.
Most business problems can’t be solved purely by algorithms or machine learning. They require human interaction and perspective. Augmented analytics is a combination of human curiosity and artificial intelligence, making BI and data analytics accessible to more people by allowing them to ask questions and automatically generate insights from their data, through simple conversational and visual interactions. Ultimately, this brings deeper insights and data-driven decisions which improve a company’s performance.