Machine learning is a nightmare without some kind of structure. You can’t build everything from scratch, especially if you’re in a business setting. Even if you want to (and if you do, comment here and tell us about it!), you don’t have time in most cases. You need a framework to help bring your vision to life. Here are a few machine learning frameworks designed to help get those projects off the ground. And, as always, let us know if the one you love isn’t on the list.
[Related Article: Deep Learning Frameworks You Need to Know in 2020]
Scikit-Learn is a feat of the Python community. It handles both supervised and unsupervised learning and has robust documentation for every possible question you’d have. If you know Python, you’re covered with this framework.
It’s capable of working on multiple tasks without sacrificing speed. Spotify uses it, as does Evernote. It comes with clean API and is highly efficient for data mining. If you’re building models, Scikit-Learn is a fantastic option.
You saw that coming, of course. TensorFlow remains a giant in the industry with open source libraries and support for classifications, regressions, and neural networks. It’s both CPU and GPU compatible and runs on mobile devices for those of you who need flexibility.
Google’s DeepMind uses TensorFlow for research, and the scalable production and deployment options make it a standard across industries. Although it’s a little dense for machine learning, if you’re using a hybrid of machine and deep learning and willing to put in the time for training, TensorFlow is a robust option.
Facebook’s alternative to Google’s TensorFlow is a flexible, more lightweight machine learning framework built for high-end efficiency. PyTorch is accessible to anyone who knows Python, and for ML research purposes, it’s more popular than TensorFlow.
It has excellent community documentation and offers easy and quick editing capabilities. It’s a bit slower on the production side, but that’s easily fixed with an API server. It provides dynamic graphing (opposed to TensorFlow’s static graphing), and it’s open-source.
CAFFE may be most popular with deep learning, but if you’re concentrated in platforms like mobile, it could provide a flexible alternative to TensorFlow. Facebook uses CAFFE 2 for applications on its mobile app, for example.
Computational restraints could make some frameworks too bulky, but CAFFE fast and excellent with visual recognition. You’ll have to be familiar with C++, so software developers and engineers out there, this could be your ticket to machine learning models.
Microsoft Cognitive toolkit
Microsoft built this one to handle deep learning, but it can be formulated to process huge amounts of unstructured data for machine learning models. It’s useful for recurrent neural networks, for instance. If you’re putting baby steps into the field of deep learning, this could be a good bridge.
It’s highly customizable and supports multi-machine back ends. With rapid training time and a relatively simple to use architecture, it’s a deep learning framework that’s backwards compatible with machine learning.
Another one that bridges a gap between machine and deep learning, Keras is a simple machine learning framework with a simplistic interface. It provides rapid prototyping and works with TensorFlow. It’s straightforward to learn and gives Python-based developers a good foothold into deep learning.
Its primary usage is classification and summarization. Other language functionalities like translation, speech recognition, and tagging are also part of its usage.
Firebase ML Kit
A relatively new offering, the ML Kit offers developers a multi-platform option capable of handling text recognition, image labeling, and object classification. It has pre-trained models and allows you to build minimally coded projects.
Google introduced it to help developers integrate machine learning models into mobile apps without a lot of technical expertise. Since this is a function full of issues and difficulties, Google’s design helps cut down on the backward flips inexperienced developers have to do.
Deciding on Your Machine Learning Framework
The data science community has made your machine learning projects so much more comfortable with rich frameworks to help you get to your end goals. You don’t have to start from scratch unless you want to, and the time you save goes directly into your innovation time. Make sure you begin compiling your toolkits so that each project goes more smoothly.
[Related Article: Machine Learning Guide: 20 Free ODSC Resources to Learn Machine Learning]
The list isn’t exhaustive, but if you’re working with machine learning for the first time in 2020, these are good places to get started. If you’re already a machine learning master, help us out in the comments by outlining the frameworks you use to help ensure your projects get off the ground smoothly. We can all learn by example and from such a richly collaborative community.
Ready to learn about all of these machine learning frameworks in-person? Attend ODSC East 2020 this April 13-17 and learn to use them in-person from some of the people who made them!