We live in a time of huge opportunities; where workers across an organization can leverage Artificial Intelligence (AI) for improving business outcomes. But data scientists, who are crucial to deploying AI solutions, don’t have the bandwidth to provide custom solutions to all users at an organization. Businesses need ways to integrate the valuable work of data scientists and empower more users to leverage AI in easy and intuitive ways.
Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to confusion.
Machine Learning is a subset of Artificial Intelligence.
Microsoft has started integrating Power BI with Azure ML and we will talk about their offering later, but to help with my knowledge of AI, I took a bootcamp by Data Science Dojo
Data Science Bootcamp by Data Science Dojo
Data Science Dojo is a paradigm shift in data science bootcamps. They offer short-term, in-person, hands-on data science training that will get you started with practical data science in just one week. Their tag line is “data science for everyone” and after attending the training I would have to agree. Raja and Rebecca did a great job of teaching the techniques needed to apply data science to your business. Of all the training that I have taken, this was by far the best! The amount of knowledge that I gained was beyond comparison. The bootcamp was structured in three phases:
- Getting Ready
- 5 Day Immersive Bootcamp
- After the Bootcamp
In the Getting Ready phase, you will learn the fundamentals of data science and data mining, R programming, and Amazon and Azure tool for data science and engineering. Now for the hard part, the 5 Day Immersive Bootcamp, this was an intense 10 hours a day for 5 days. This was the right mix of theory, hands-on labs, and science and engineering from experienced instructors that have practical real-world experience. Some of the things that we learned were: Big Data engineering, Text analytics, Regression analysis, Classifications, Parameter tuning and Predictive models.
For the After the Bootcamp phase, graduates receive invites to alumni-exclusive events, tutorials and networking groups (because data science can’t be learned in 5 days).
I can’t say enough about this great bootcamp and if you get a chance to go, do it! So why did I do all of this? Well, if you’ve been reading the news coming out of Microsoft, a lot of it has been related to bringing Azure ML to the business user. Here is some of that news.
Integrate Your Azure Machine Learning Models with Power BI
Advanced machine learning requires specialized data science tools. Azure Machine Learning is a platform where data scientists develop machine learning models to take on complex business challenges. Azure ML models built by data scientists can now be easily shared with business analysts. Power BI works behind the scenes to discover the models to which each user has access and automatically creates a point and click user interface to invoke them. This makes collaboration among business analysts and data scientists easier and faster than ever before.
Link to the original Power BI article on Azure Machine Learning Model.
New Key Driver Analysis feature in Power BI
Key driver analysis helps you understand what drives an outcome. It reasons over your data, ranks those things that matter, and surfaces those key drivers. For example, consider a student’s plans to attend college as a KPI. There are different factors that impact whether kids plan to enroll in college. Key driver analysis automatically surfaces those things that matter the most. Below, you see that parental encouragement has significant positive impact on a student’s plans.
Link to the original Power BI article on key driver analysis.
Build Your Own Machine Learning Models in Power BI
In Power BI, business analysts will now be able to build their own machine learning models without writing a single line of code. We’re using the automated machine learning feature in Azure Machine Learning, but instead of targeting developers or data scientists, we’ve simplified it and made it broadly accessible for common use cases. This means that when an analyst builds a machine learning model in Power BI, it does all the heavy lifting by selecting the best algorithm and features with just a few clicks.
Link to the original Power BI article on building Machine Learning Models.
Ready to Do Even More with Your Data?
Start organizing, knowing and executing on your data today with dataflows and Power BI to provide a self-service data lake in the future. KTL Solutions works with business leaders every day in helping them lead their organization into becoming a data-driven organization.
Need help executing on your data? Contact us today.