How Nonprofits Use AI to Improve the Workforce
Artificial intelligence has a hand in just about every industry possible, including the nonprofit sector. Though some may not realize it, nonprofit organizations account for nearly $400B of revenue annually worldwide. But some nonprofits still struggle to make ends meet and expand their workforces, causing them to fold and... Read more
Problem Solving with Data for a Better Business
When working with large datasets, the smallest anomalies can throw a wrench in predictive analysis. For example, if a company manually enters data into its database, a human error like mistyping or improper timestamps in the training data of a machine learning model may give you reduced accuracy results.... Read more
Operationalization of Machine Learning Models
Lots of businesses want to use machine learning, but few are ready to integrate machine learning into a real-life context of operations. Dr. Mufajjul Ali, Data Solutions Architect for Microsoft, outlines how Microsoft is addressing these needs and offers some advice for businesses looking to operationalize ML models and... Read more
Using AI to Revolutionize Child Behavioral Diagnostics and Therapeutics
At ODSC East’s opening day, experts from around the world gave presentations on how AI is breaking boundaries in their respective fields. One area that data scientists are hoping to reshape with the help of AI is the process of child behavioral disorder diagnostics. Halim Abbas is the Chief... Read more
What are MLOps and Why Does it Matter?
During the industrial revolution, the rise of physical machines required organizations to systematize, forming factories, assembly lines, and everything we know about automated manufacturing. During the first tech boom, Agile systems helped organizations operationalize the product lifecycle, paving the way for continuous innovation by clearing waste and automating processes... Read more
9 Common Mistakes That Lead To Data Bias
Data scientists spend a lot of time with data, which by itself is neutral. It only follows that answers gleaned from the data would be neutral too. Even though data is neutral, our responses to data are sometimes filled with bias that can skew our outcomes. Let’s examine some... Read more
How to Choose Machine Learning or Deep Learning for Your Business
AI is the future, or so you’re hearing. Every day, news of another organization leveraging AI to produce business outcomes that outstrip competition hit your inbox, but your company either hasn’t started at all or is mired in the discussion. AI, machine learning, and deep learning are sometimes used... Read more
Why Value-Stream-As-A-Service Could Be Your Business’s Next Big Thing
Mapping your value stream is a huge part of running an agile operation. Value streams reveal where product or service flow excels and where it breaks down, offering insight into how your organization can provide continuous value to customers and improve existing services or products for future deployment. Traditionally,... Read more
Why You Still Need Business Intuition in the Era of Big Data
Artificial intelligence is coming for your job, right? Not so fast. Despite what doomsday enthusiasts predict, machines are a long way off from becoming dangerously sentient. Instead, machines are taking over areas where humans just aren’t that great, freeing up human labor to do what we do best, create,... Read more
5 Roadblocks to Getting an ML System in Production
We typically meet an organization’s data science team after they’ve carried out a successful proof of concept. The algorithm they built or acquired produced results that were promising enough to greenlight development of a production ML system. It’s at this point that the immaturity of ML project management often... Read more