14 open source tools to get the most out of machine learning

Harness the predictive power of machine learning with these diverse, easy-to-implement libraries and frameworks

Spam filtering tools, facial recognition, suggestions – when you have a large data set that you want to perform predictive analysis or pattern recognition, machine learning is the way to do it. The rise of free open source software has made machine learning easier to do on both a single and large-scale machine and in most popular programming languages. These open-source tools include libraries for Python, R, C++, Java, Scala, Clojure, JavaScript, and Go.

Apache Mahout
Apache Mahout provides a way to build storage environments where machine learning applications can be expanded quickly and efficiently to meet demand. Mahout primarily operates with another well-known Apache project, Spark, and was originally designed to work with Hadoop to run distributed applications, but has been expanded to operate with other distributed back ends such as Flink and H2O.

Mahout uses a specific language of the domain in Scala. Version 0.14 is a major internal restructuring of the project, based on Apache Spark 2.4.3 as its default.

Compiled and compiled
The author, of Innovation Labs, aims for a common problem with machine learning modeling: labeling raw data, which can be a slow and tedious process, but without machine learning models can not bring useful results. Compose allows you to write in Python a set of functions labeled for your data, so labeling can be done as a program-by-program as possible. Different variations and thresholds can be placed on your data to make labeling easier, such as placing data in bins based on discrete values or quantities.

Core ML tools
Apple’s Core ML framework allows you to integrate machine learning models into the app but uses its own separate learning model format. The good news is that you don’t need to pre-train models in Core ML format to use them; you can convert models from every commonly used machine learning framework to Core ML with Core ML Tools.

Core ML Tools runs as a Python package, so it integrates with countless Python libraries and machine learning tools. All models from TensorFlow, PyTorch, Keras, Caffe, ONNX, Scikit-learning, LibSVM, and XGBoost can be converted. Neural network models can also be optimized for size by using post-training dosing (e.g., to small bit depths that remain accurate).

Cortex provides a convenient way to serve predictions from machine learning models using Python and TensorFlow, PyTorch, Scikit-learning, and other models. Most Cortex packages include only a few files – your core Python logic, cortex.YAML files describing which models to use, and what kind of computing resources to allocate, and request.txt files to install any necessary Python requirements. The entire package is deployed as a Docker container to AWS or another Docker-compatible storage system. Computer resources are allocated in a way that repeats the definitions used in Kubernetes for the same and you can use GPU or Amazon Inferential ASIC to speed up delivery.

Feature tools
Feature engineering or feature creation, including data collection used to train machine learning and manufacturing models, are usually manual, data versions that are synthesized and transformed more usefully for model training. Feature tools provide you with functions to do this by using high-level Python objects built by a combination of data in data frames and can do this for data extracted from one or more data frames. Feature tools also provide general guidelines for angst operations (e.g. time_since_previous, to provide elapsed time between time-marked data cases), so you don’t need to scroll them yourself.

Going to school
GoLearn, a machine learning library for Google’s Go language, was created with two goals: simplicity and customization, according to developer Stephen Whitworth. Simplicity lies in how data is loaded and processed in the library, which is modeled after SciPy and R. Customizability lies in how some data structures can be easily expanded in an application. Whitworth also created a Go wrapper for the Vowpal Wabbit library, one of the libraries found in the Shogun toolbox.

A common challenge when building machine learning applications is to build a powerful and easy-to-customize user interface for mechanisms that serve model prediction and training. Grade provides tools to create a web-based user interface that allows you to interact with your models in real-time. Some sample projects include, such as the input interface for the Inception of V3 image sorter or the MNIST handwriting recognition model, which gives you an idea of how you can use Gradio with your own projects.

H2O, currently in its third major revision, provides the entire platform for machine learning in memory, from training to predictive service. H2O’s algorithms are directed at business processes — such as trend prediction or fraud — instead of image analysis. H2O can interact independently of HDFS stores, on YARN, in MapReduce or directly in the Amazon EC2 version.

Hadoop maven can use Java to interact with H2O, but this framework also provides constraints for Python, R, and Scala, allowing you to interact with all the libraries available on those platforms. You can also go back to REST calls as a way to integrate H2O into most pipelines.

Oryx, with the permission of the creators of the Cloudera Hadoop distribution, uses Apache Spark and Apache Kafka to run machine learning models on real-time data. Oryx provides a way to build projects that require decisions at the moment, such as direct family suggesting or detecting tools, provided by both new and historical data. Version 2.0 is an almost complete redesign of the project, with its components loosely paired in lambda architecture. New algorithms and new summary sections for those algorithms (e.g. for hypersymmmmm parameter selection), can be added at any time.

PyTorch Lightning
When a powerful project becomes popular, it is often complemented by third-party projects for easier use. PyTorch Lightning provides an organizational wrapper for PyTorch so you can focus on important code instead of writing pre-written for each project.

Lightning projects use a layer-based structure, so each general step for a PyTorch project is encapsulated in a layered method. The training and authentication rounds are semi-automatic, so you just need to provide your logic for each step. Setting up training results in a variety of GPUs or hardware mixtures is also easier because the instructions and object references to do so are central.

Python has become a popular programming language for mathematics, science, and statistics due to the ease of application and the variety of libraries available for almost any application. Scikit-learning promotes this breadth by building on several existing Python packages — NumPy, SciPy, and Matplotlib — for mathematical and scientific work. The results library can be used for interactive “desktop” applications or embedded in other software and re-used. This set is available under the BSD license, so it is fully open and reusable.

The Shogun is one of the longest-standing projects in this collection. It was created in 1999 and written in C++, but can be used with Java, Python, C #, Ruby, R, Lua, Octave, and Matlab. The latest major version, 6.0.0, adds native support for Microsoft Windows and the Scala language.

Although popular and widespread, the Shogun has competition. Another C++-based machine learning library, Mlpack, has only appeared since 2011 but is said to be faster and easier to work with (with a more integrated API set) than competing libraries.

Spark MLlib
The machine learning library for Apache Spark and Apache Hadoop, MLlib boasts many popular algorithms and useful data types, designed to run at speed and scale. Although Java is the primary language for working in MLlib, Python users can connect MLlib to the NumPy library, Scala users can write code based on MLlib, and R users can plug in Spark as of version 1.5. Version 3 of MLlib focuses on the use of spark’s DataFrame API (as opposed to the older RDD API) and offers many new classification and evaluation functions.

Weka, created by the Machine Learning Team at the University of Waikato, is advertised as “machine learning without programming”. It’s a GUI desk that allows data wrappers to assemble machine learning pipelines, train models, and run predictions without having to write code. Weka works directly with R, Apache Spark, and Python, the following by wrapping directly or through interfaces for popular digital libraries such as NumPy, Pandas, SciPy, and Scikit-learning. Weka’s great advantage is that it offers user-friendly, browseable interfaces for every aspect of your work including package management, pre-processing, classification, and visualizations.