The Best Open Source Algorithms
This is part 7 of a 9 part series on Machine Learning. Our goal is to provide you with a thorough understanding of Machine Learning, different ways it can be applied to your business, and how to begin implementations of Machine Learning within your organization through the assistance of Untitled. This series is not by any means limited to only those with a technical pedigree. The objective is to provide a volume of content that will be informative and practical for a wide array of readers. We hope you enjoy and please do not hesitate to reach out with any questions. To start from part 1, please click here.
Welcome back to the Untitled Machine Learning series where we have been taking you on a journey through the world of ML, Deep Learning and AI in all of its forms. Parts 1-6 were primarily focused on educational components surrounding this subject matter, however, the remainder of the series will focus on practical applications and approaches for implementation. In this blog post, we will discuss the best open source algorithms that we utilize in our own toolbox.
We have found throughout the variety of advanced analytics projects Untitled has worked on that this library has the best flexibility for an extreme breadth of Machine Learning problems. Out of all the packages listed in this post, you’ll find that this algorithm is the most frequently updated. Tensorflow is widely considered the most popular ML solution in production today.
Very recently deployed by Uber’s open source team, Ludwig is a low-code/no-code Machine Learning package that leverages the TensorFlow library. In an effort to make ML more accessible for the masses, Uber publicly released Ludwig in February of 2019. This package is loads of fun to use and will help you rapidly develop models and focus on what matters: condensing time to insights.
Ludwig requires very little software development background to install and deploy in a workable environment. Untitled has yet to try out Ludwig for a client, but we believe later this year a package like this will allow for us to provide advanced analytics to smaller scale companies at an affordable price.
We think of Keras as the quick and dirty ML package, great for rolling up sleeves and constructing models quickly. Initially released in 2015, the goal of the Keras package is to reduce the time it takes to construct and deploy deep learning models.
Keras is a fan favorite for most of the Data Science community for its user friendliness, extensive community and rapid development capabilities.
The bedrock algorithm for the data science community, Scikit-learn is the oldest and most mature of the ML packages listed in this post. First publicly released in 2007, this open source algorithm is a darling to the Machine Learning community. Scikit-learn has the most extensive documentation and projects to reference that we’ve seen.
Whether you’d like to use the package for simple regression problems or advanced unsupervised learning clustering analysis, Scikit-learn will not disappoint data scientists in its consistency and simplicity.
We hope you enjoyed this post and will continue on to part 8 where we discuss the risk of building offline models and training on purified data sources. In that post we will dive into the struggle and tension of introducing new models into aging architecture and antiquated data science teams.
If you are interested in starting on a Machine Learning project today or would like to learn more about how Untitled can assist your company with data analytics strategies, please reach out to us through the start form.
Check out part eight of this series.