The power of Artificial Intelligence (AI) is vast and evolving with every passing year. Data being the powerhouse of AI holds equal value for businesses worldwide. In this edition, we shall throw light on one such AI-driven data visualization framework, Power BI that facilitates significant data analytics. We, at Oodles, as a Machine Learning Development Company, shall discuss in detail, the features of Power BI and its workings.

Five AI Features of Power BI

1. AI Builder
2. Azure Cognitive Services
3. Influencers Visual
4. Building Your ML Models With Azure Machine Learning
5. Power BI Chatbots

The ease of integrating Power BI with existing business models adds up to the listed features. More so, components of Power BI enables providers of predictive analytics services to extract actionable insights from large datasets.

Components of Power BI

1. Data visualization
2. Data preparation
3. Data modeling

Data visualization

Using Power BI we can create visuals in an easy way with interpreting and analyze our data. Now Power BI has three explicit AI visuals to create, which looks like a light bulb icon. We have lots of visuals features in Power BI like:

1. Decomposition Tree

      2. Key Influencers

      3. Q&A Visual

       4. Insights

Explain Increase/Decrease

Data Preparation

It refers to the extract transform load processes that combine, transform, and improve your dataset. Of a Power BI view, I am expressing regarding Power Query inside Power BI Desktop, also Power Query Online inside Dataflows.

1. Automated Machine Learning (AutoML) in Power BI

2. Cognitive Services

Data Modeling

DM in Power BI means the manner of designing into the memory, enterprise-grade, semantic models. Meanwhile, layman's terms, allows y'all to build an experience that allows analysts to build their reports at top of the model. A distinct data model also provides Power BI to efficiently auto-generate insights for you, through points like Q&A and Quick Insights.