5 artificial intelligence tools data scientists might not know

With Artificial Intelligence going mainstream, it is not at all surprising to see the number of tools and platforms for AI development go up as well. Open source libraries such as Tensorflow, Keras and PyTorch are very popular today. Not just those - enterprise platforms such as Azure AI Platform, Google Cloud AI and Amazon Sagemaker are commonly used to build scalable production-grade AI applications.

While you might be already familiar with these tools and frameworks, there are quite a few relatively unknown AI tools and services which can make your life as a data scientist much, much easier! In this article, we look at 5 such tools for AI development which you may or may not have heard of before.

Wit.ai

One of the most popular use-cases of Artificial Intelligence today is building bots that facilitate effective human-computer interaction. Wit.ai, a platform for building these conversational chatbots, finds applications across various platforms, including mobile apps, IoT as well as home automation.

Used by over 150,000 developers across the world, this platform gives you the ability to build conversational UI that supports text categorization, classification, sentiment analysis and a whole host of other features.

Why you should try this machine learning tool out

There are a multitude of reasons why wit.ai is so popular among developers for creating conversational chatbots. Some of the major reasons are:

Support for text as well as voice, which gives you more options and flexibility in the way you want to design your bots

Support for multiple languages such as Python, Ruby and Node.js which facilitates better integration of your app with the website or the platform of your choice

The documentation is very easy to follow

Lots of built-in entities to ease the development of your chatbots

Intel OpenVINO Toolkit

Bringing together two of the most talked about technologies today, i.e. Artificial Intelligence and Edge Computing, we had to include Intel’s OpenVINO Toolkit in this list. Short for Open Visual Inference and Neural Network Optimization, this toolkit brings comprehensive computer vision and deep learning capabilities to the edge devices. It has proved to be an invaluable resource to industries looking to set up smart IoT systems for image recognition and processing using edge devices.

The OpenVINO toolkit can be used with the commonly used popular frameworks such as OpenCV, Tensorflow as well as Caffe. It can be configured to leverage the power of the traditional CPUs as well as customized AI chips and FPGAs. Not just that, this toolkit also has support for the Vision Processing Unit, a processor developed specifically for machine vision.

Why you should try this AI tool out

Allows you to develop smart Computer Vision applications for IoT-specific use-cases

Support for a large number of deep learning and image processing frameworks.

Also, it can be used with the traditional CPUs as well as customized chips for AI/Computer Vision

Its distributed capability allows you to develop scalable applications, which again is invaluable when deployed on edge devices

You can know more about OpenVINO’s features and capabilities in our detailed coverage of the toolkit.

Apache PredictionIO

This one is for the machine learning engineers and data scientists looking to build large-scale machine learning solutions using the existing Big Data infrastructure. Apache PredictionIO is an open source, state-of-the-art Machine Learning server which can be easily integrated with the popular Big Data tools such as Apache Hadoop, Apache Spark and Elasticsearch to deploy smart applications.

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Source: PredictionIO System architecture

As can be seen from the architecture diagram above, PredictionIO has modules that interact with the different components of the Big Data system and uses an App Server to communicate the results of the analysis to the outside devices.

Why you should try this machine learning tool out

Let’s you build production-ready models which can also be deployed as web services

You can also leverage the machine learning capabilities of Apache Spark to build large-scale machine learning models

Pre-built performance evaluation measures available to check the accuracy of your predictive models

Most importantly, this tool helps you simplify your Big Data infrastructure without adding too many complexities

IBM Snap ML

A machine learning library that is 46 times faster than Tensorflow. If that’s not a reason to start using IBM’s Snap ML, what is?

IBM have been taking some giant strides in the field of AI research in a bid to compete with the heavyweights in this space - mainly Google, Microsoft and Amazon. With Snap ML, they seem to have struck a goldmine. A library that can be used for high-speed machine learning models using the cutting edge CPU/GPU technology, Snap ML allows for agile development of models while scaling to process massive datasets.

Why you should try this machine learning tool out

It is insanely fast. Snap ML was used to train a logistic regression classifier on a terabyte-scale dataset in just under 100 seconds.

It allows for GPU acceleration to avoid large data transfer overheads. With the enhanced GPU technology available today, Snap ML is one of the best tools you can have at your disposal to train models quickly and efficiently

It allows for distributed model training and works on sparse data structures as well

You should definitely check out our detailed coverage of Snap ML where we go into the depth of its features and understand why this is a very special tool.

Crypto-ML

It is common knowledge that cryptocurrency, especially Bitcoin, can be traded more efficiently and profitably by leveraging the power of machine learning. Large financial institutions and trading firms have been using the machine learning tools to great effect. However, it’s the individuals, on the other hand, who have relied on historical data and outdated techniques to forecast the trends. All that has now changed, thanks to Crypto-ML.

Crypto-ML is a cryptocurrency trading platform designed specifically for individuals who want to get the most out of their investments in the most reliable, error-free ways. Using state-of-the-art deep learning techniques, Crypto-ML uses historical data to build models that predict future price movement. At the same time, it eliminates any human error or mistakes arising out of emotions.

Why you should try this machine learning tool out

No expertise in cryptocurrency trading is required if you want to use this tool

Crypto-ML only makes use of historical data and builds data models to predict future prices without any human intervention

Per the Crypto-ML website, the average gain on winning trades is close to 53%, whereas the average loss on losing trades is just close to 6%.

If you are a data scientist or a machine learning developer with an interest in finance and cryptocurrency, this platform can also help you customize your own models for efficient trading.

Here’s where you can read on how Crypto-ML works, in more detail.

Other notable mentions

Apart from the tools we mentioned above, there are a quite a few other tools that could not make it to the list, but deserve a special mention. Some of them are:

ABBYY’s Real-time Recognition SDK for document recognition, language processing and data capturing is worth checking out.

Vertex.ai’s PlaidML is an open source tool that allows you to build smart deep learning models across a variety of platforms. It leverages the power of Tile, a new machine learning language that facilitates tensor manipulation.

Facebook recently open sourced MUSE, a Python library for efficient word embedding and other NLP tasks. This one’s worth keeping an eye on for sure!

If you’re interested in browser-based machine learning, MachineLabs recently open sourced the entire code base of their machine learning platform.

NVIDIA’s very own NVVL, their open source offering that provides GPU-accelerated video decoding for training deep learning models

The vast ecosystem of tools and frameworks available for building smart, intelligent use-cases across various domains just points to the fact that AI is finding practical applications with every passing day. It is not an overstatement anymore to suggest that that AI is slowly becoming indispensable to businesses. This is not the end of it by any means either - expect to see more such tools spring to life in the near future, with some having game-changing, revolutionary consequences.

So which tools are you planning to use for your machine learning / AI tasks? Is there any tool we missed out? Let us know!

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