In one of my projects to obtain more email signups for my role at ODSC, I create a downloadable PDF, the “ODSC Guide to Machine Learning,” which contained highlighted blogs and recorded ODSC talks related to machine learning, among other pieces of content. At the end, I wrote a letter from the editor with my thoughts on machine learning.
Graphic design was done by Paxtyn Merten.
Content compilation and descriptions by me.
One thing common thread I’ve seen at every stop in my career is the ever-increasing focus on using data to make informed decisions.
Now, perched in a place to see day-to-day developments in the field, I’ve learned so much about various frameworks and languages, the unique ways that different industries are using AI, and of course, how strong the open data science community is in sharing its knowledge.
There are definitely a few topics in particular that I’m paying extra attention to heading into 2019. Apache Spark’s MLlib is a frequent topic of conversation experts when I speak with data science experts, largely thanks to MLib’s remarkable scaling ability in implementing multiple ML algorithms. At this rate, I can see the scikit-learn library for Python becoming a common job requirement for ML experts – it’s appearing in social feeds, featured blogs, and job listings that cross my desk more and more. The TensorFlow framework is popping up in countless ML tutorials, so I’m hoping people stay active with it, even with newer libraries releasing frequently. This should be a no-brainer, but start looking into automated machine learning if you haven’t already, as it will help to increase the pace in which you can create more complex ML processes and algorithms. Let the machines work for you!
Data scientists need to make 2019 a strong year to address the common “black box” problem. As Daniel Gutierrez, a data science consultant and frequent author for OpenDataScience.com put it, “I hope the trend of ‘explainability’ or ‘interpretability’ of AI will continue to be seen as critical to the continued acceptance of the technology.” Since machine learning may open up more chances for vulnerabilities to appear, developers need to be careful with their applications and to avoid leaving entryways for malicious attacks, and to be well-prepared to defend against potential adversarial attacks. It’s better to spend the time building up your defenses rather than going through the headache of resolving issues from hackers and malware.
Whether you use the information provided in this anthology to learn a new tool, framework, or you’re now more invested in security and defense, I hope that all of the videos, blogs, and insights from the experts in this anthology prove useful for you. Whether you’re new to data science and machine learning or a seasoned vet, already working in applied data science or academia, or you’re just a fan of new technology, the open data science community is a good place to share knowledge and expand understanding of the most exciting topics in applied data science