Which AI software development tools, frameworks, libraries, and other technologies should you add to your toolbox?
And the number of emerging AI tech these days sure is... overwhelming!
Which one(s) the perfect fit for your own machine learning project/model/problem? Which one's equipped with precisely those features that you need for a fully functioning AI algorithm?
To lend you a hand, we've made a “drastic” sorting out and narrowed the high amount of AI software available to a shortlist of... 8.
The 8 best AI technologies to consider “turbocharging” your ML project with:
1. Infosys Nia
A knowledge-based AI platform to go with if your AI-powered project's goal is to:
gain in-depth insights into customer behavior
reduce financial transaction frauds
optimize asset efficiency
streamline how your team manages customer inquiries
"And how does it work?"
"What does it do, more precisely?"
It collects organizational data on the legacy systems, the people and the processes involved and “piles it up” into a self-learning knowledge base.
One that developers and data analysts in your team can leverage to create high-performing, scalable ML models.
And all that even if they don't have data science expertise, thanks to the platform's easy-to-use ML workbench.
extensibility: for data preparation, machine learning methods, visualizations
self-service provisioning: elastic cloud deployments
GUI-based features: enabling your AI software development team to build accurate ML models
integrated enterprise framework: for data preparation, reports, deployment, and modeling
streaming fast predictions: Infosys Nia Prediction Server
The second — yet not “the second best” — AI software development tool in our list is an:
customizable at scale
… deep-learning library written for Scala and Java.
One that Clojure programmers, too, using Hadoop and other file systems can use for building their deep neural networks.
A library designed as a plug-and-play AI solution for fast prototyping.
it can be used in business environments on distributed CPUs and GPUs
tailored to perfectly fit a micro-service architecture
GPU support for scaling on AWS
Python, Java, and ScalaAPIs
it scales on Hadoop
it can import neural net models from other frameworks — Caffe, TensorFlow, Theano — via Keras
it comes with a cross-team toolkit for DevOps, data scientists, data engineers
An open source machine learning library & a Lua-based script language & a scientific computing framework.
Why/how has it “earned” its place on our shortlist here?
first of all, it provides a “heavy load” of algorithms of deep machine learning
the Facebook AI Research Group, the Idiap Research Institute, IBM and Yandex are just some of the heavy-weighting names using it
it's built to “fuel” machine learning projects with both speed and flexibility, without adding an unnecessary overhead
linear algebra routines; and it supports plenty of them: for indexing, type-casting, cloning, slicing, sharing storage etc.
efficient GPU support
numeric optimization routines
it's embeddable, with ports for Android and iOS backends
great interface to C (via LuaJIT)
4. Tensorflow, One of the Most Popular AI Software Development Tools
A Google-powered open-source software library for machine learning projects. One that's conveniently easy to use across a wide range of platforms.
You get to use it with:
As a new user, you'd be joining the high league of all those big names that are currently using this AI software development technology in their ML-enabled projects: Uber, Intel, Twitter, eBay...
“And how does it work?”
Basically, what it does is that it provides you with a library storing numerical computation that uses data flowgraphs.
In short: you'd be building your neural networks using flowgraphs:
the nodes in the graphs stand for the math operations
the graph edges represent the tensors (multidimensional arrays of data) communicating between them
It's this flowgraphs-based structure that enables developers to deploy deep learning frameworks over several central processing units (CPUs) on tablet devices, mobile, and desktop.
But probably one of TensorFlow's biggest strengths and the reason for its wide adoption is its documentation:
It provides plenty of support for newcomers (those new to Python here included: from tutorials to detailed documentation, to online resources...
Another interesting feature is given by its multiple APIs:
the lowest level API: gives your complete programming control
the higher level API: makes repetitive tasks more consistent and easier to carry out for different users
Top TensorFlow-powered Apps:
RankBrain: deployment of deep neural nets on a large-scale basis for search ranking on Google.com
Massively Multitask for Drug Discovery: a deep neural network model for detecting favorable drug candidates
On-Device Computer Vision for OCR: computer vision model that performs optical character recognition for real-time translations
The library that you should go with if your AI software development team is made of devs with rich experience in implementing neural networks.
OpenNN (Open Neural Networks Library) is a C++ programming library designed to learn from both:
Note: Neural Designer, a predictive analytics software that creates visual content enhancing the interpretation of data entries — e.g. tables and graphs — is OpenNN-powered.
it provides plenty of support — documentation, tutorials — for helping users get into neural networks, even if it's built for developers with a solid AI background
it implements data mining methods by bundling multiple functions
bundles of functions that can get embedded into other software tools via API (thus enabling and streamlining the interaction between these software tools and the predictive analytics tasks)
it's a high performant neural network library: high processing speed, great memory management (since it's built in C++) and CPU parallelization
time series prediction
optimal shape design
Datasets and Mathematical Models:
6. Apache SystemML
An IBM-powered machine learning technology.
Or, if we are to detail this short definition a bit:
It's a scalable, flexible in-memory data processing framework providing a huge database of algotihms focused on: clustering, classification, regression, collaborative filtering.
automatic optimization based on both cluster and data characteristics (scalability & efficiency)
algorithm customization via Python-like and R-like languages
it can be run on top of Apache Spark, due to its great scalability capabilities
multiple execution modes: Standalone, Spark MLContext, Hadoop Batch, JMLC (Java Machine Learning Connector)
A deep learning framework written in C++, with a Python interface built around 3 main features:
Speaking of the latter, this is an AI software development tool that provides developers with an automatic inspection tool based on imaging.
If your machine learning project includes computer vision-related tasks, Caffe (Convolutional Architecture for Fast Feature Embedding) makes a great, robust choice.
extensible code, that enables active development
an active community constantly improving it
8. Apache Mahout
How important is scalability for your machine learning app project?
If “critical” is the word you'd use, then Apache Mahout is the AI software development tool for your project.
It's designed with scalability in mind and as a tool empowering data scientists, mathematicians, statisticians to implement their own algorithms quick and easy.
provides pre-built algorithms for Apache Flink, Apache Spark, H20
support for various distributed back-ends (Apache Spark here included)
comes packed with modular native solvers for GPU, CPU, CUDA acceleration
Matrix and vector libraries
These are the top 8 AI software development tools to narrow down your options to.
To evaluate first, putting them against:
your project's goals
your team's experience with machine learning algorithms
... and to determine whether they're the perfect fit.