Who are you, citizen data scientist?

ZDNet

Ugh. Everyone is talking about the citizen data scientist, but no one can define it (perhaps they know one when they see one). Here goes -- the simplest definition of a citizen data scientist is: non-data scientist. That's not a pejorative; it just means that citizen data scientists nobly desire to do data science but are not formally schooled in all the ins and outs of the data science life cycle. For example, a citizen data scientist may be quite savvy about what enterprise data is likely to be important to create a model but may not know the difference between GBM, random forester, and SVM. Those algorithms are data scientist geek-speak to many of them.


The Rise Of Automated Machine Learning (AutoML): Coming To Your Industry

#artificialintelligence

Machine learning (ML) has a rapidly increasing presence across industries. While top technology companies like Amazon, Google and Microsoft certainly talked a lot about ML's big impact on powering applications and services in 2017, its usefulness continues to emerge in businesses of all sizes: automatically targeting segments of the market at marketing agencies, offering e-commerce product recommendations and personalization from retailers and creating fraud prevention customer service chatbots at banks. Certainly, ML is a hot topic, but there's another related trend that's gaining speed: automated machine learning (AutoML). What Is Automated Machine Learning? The field of AutoML is evolving so quickly there's no universally agreed upon definition.


How to find the best machine learning frameworks for you

#artificialintelligence

Several machine learning frameworks have emerged to streamline the development and deployment of AI applications. These frameworks help abstract away the grunt work of testing and configuring AI workloads for experimentation, optimization and production. However, developers need to make some hard choices when it comes to picking the right framework. Some may want to focus on ease of use when training a new AI algorithm, while others may prioritize parameter optimization and production deployment. Different frameworks have different strengths and weaknesses in these diverse areas.


The Rise of Automated Machine Learning Transforming Data with Intelligence

#artificialintelligence

No matter what industry you're in, autoML can help you use machine learning successfully and extract and leverage business insights hidden in places where only machine learning can reach. Machine learning (ML) has a rapidly increasing presence across industries. Top technology companies such as Amazon, Google, and Microsoft certainly talked a lot about ML's big impact on powering applications and services in 2017. Its usefulness continues to emerge in businesses of all sizes: automatically targeting segments of the market at marketing agencies, e-commerce product recommendations and personalization by retailers, and fraud prevention customer service chatbots at banks are examples. Certainly ML is a hot topic, but there's another related trend that's gaining speed: automated machine learning (autoML).


Who Are You, Citizen Data Scientist?

#artificialintelligence

Ugh. Everyone is talking about the citizen data scientist, but no one can define it (perhaps they know one when they Here goes -- the simplest definition of a citizen data scientist is: non-data scientist. That's not a pejorative; it just means that citizen data scientists nobly desire to do data science but are not formally schooled in all the ins and outs of the data science life cycle. For example, a citizen data scientist may be quite savvy about what enterprise data is likely to be important to create a model but may not know the difference between GBM, random forester, and SVM. Those algorithms are data scientist geek-speak to many of them. The citizen data scientist's job is not data science; rather, they use it as a tool to get their job done.