2017 has been a wonderful year for Machine Learning. Developing smart, intelligent models has now become easier than ever thanks to the extensive research into and development of newer and more efficient tools and frameworks. While the likes of Tensorflow, Keras, PyTorch and some more have ruled the roost in 2017 as the top machine learning and deep learning libraries, 2018 promises to be even more exciting with a strong line-up of open source and enterprise tools ready to take over – or at least compete with – the current lot.
In this article, we take a look at 10 such tools and frameworks which are expected to make it big in 2018.
Amazon Sagemaker
One of the major announcements in the AWS re:Invent 2017 was the general availability of Amazon Sagemaker – a new framework that eases the building and deployment of machine learning models on the cloud.
This service will be of great use to developers who don’t have a deep exposure to machine learning, by giving them a variety of pre-built development environments, based on the popular Jupyter notebook format. Data scientists looking to build effective machine learning systems on AWS and to fine-tune their performance without spending a lot of time will also find this service useful.
DSSTNE
Yet another offering by Amazon, DSSTNE (popularly called as Destiny) is an open source library for developing machine learning models. It’s primary strength lies in the fact that it can be used to train and deploy recommendation models which work with sparse inputs. The models developed using DSSTNE can be trained to use multiple GPUs, are scalable and are optimized for fast performance.
Boasting close to 4000 stars on GitHub, this library is yet another tool to look out for in 2018!
Azure Machine Learning Workbench
Way back in 2014, Microsoft put Machine Learning and AI capabilities on the cloud by releasing Azure Machine Learning. However, this was strictly a cloud-only service. During the Ignite 2017 conference held in September, Microsoft announced the next generation of Machine Learning on Azure – bringing machine learning capabilities to the organizations through their Azure Machine Learning Workbench.
Azure ML Workbench is a cross-platform client which can run on both Windows and Apple machines. It is tailor-made for data scientists and machine learning developers who want to perform their data manipulation and wrangling tasks. Built for scalability, users can get intuitive insights from a broad range of data sources and use them for their data modeling tasks.
Neon
Way back in 2016, Intel announced their intentions to become a major player in the AI market with the $350 million acquisition of Nervana, an AI startup which had been developing both hardware and software for effective machine learning. With Neon, they now have a fast, high-performance deep learning framework designed specifically to run on top of the recently announced Nervana Neural Network Processor.
Designed for ease of use and supporting integration with the iPython notebook, Neon supports training of common deep learning models such as CNN, RNN, LSTM and others. The framework is showing signs of continuous improvement and with over 3000 stars on GitHub, Neon looks set to challenge the major league of deep learning libraries in the years to come.
Microsoft DMLT
One of the major challenges with machine learning for enterprises is the need to scale out the models quickly, without compromising on the performance while minimising significant resource consumption. Microsoft’s Distributed Machine Learning framework is designed to do just that. Open sourced by Microsoft so that it can receive a much wider support from the community, DMLT allows machine learning developers and data scientists to take their single-machine algorithms and scale them out to build high performance distributed models.
DMLT mostly focuses on distributed machine learning algorithms and allows you to perform tasks such as word embedding, sampling, and gradient boosting with ease. The framework does not have support for training deep learning models yet, however, we can expect this capability to be added to the framework very soon.
Google Cloud Machine Learning Engine
Considered to be Google’s premium machine learning offering, the Cloud Machine Learning Engine allows you to build machine learning models on all kinds of data with relative ease. Leveraging the popular Tensorflow machine learning framework, this platform can be used to perform predictive analytics at scale. It also lets you fine-tune and optimize the performance of your machine learning models using the popular HyperTune feature.
With a serverless architecture supporting automated monitoring, provisioning and scaling, the Machine Learning Engine ensures you only have to worry about the kind of machine learning models you want to train. This feature is especially useful for machine learning developers looking to build large-scale models on the go.
Apple Core ML
Developed by Apple to help iOS developers build smarter applications, the Core ML framework is what makes Siri smarter. It takes advantage of both CPU and GPU capabilities to allow the developers to build different kinds of machine learning and deep learning models, which can then be integrated seamlessly into the iOS applications. Core ML supports all popularly used machine learning algorithms such as decision trees, Support Vector Machines, linear models and more.
Targeting a variety of real-world use-cases such as natural language processing, computer vision and more, Core ML’s capabilities make it possible to analyze data on the Apple devices on the go, without having to import to the models for learning.
Apple Turi Create
In many cases, the iOS developers want to customize the machine learning models they want to integrate into their apps. For this, Apple has come up with Turi Create. This library allows you to focus on the task at hand rather than deciding which algorithm to use. You can be flexible in terms of the data set, the scale at which the model needs to operate and what platform the models need to be deployed to.
Turi Create comes in very handy for building custom models for recommendations, image processing, text classification and many more tasks. All you need is some knowledge of Python to get started!
Convnetjs
Move over supercomputers and clusters of machines, deep learning is well and truly here – on your web browsers! You can now train your advanced machine learning and deep learning models directly on your browser, without needing a CPU or a GPU, using the popular Javascript-based Convnetjs library.
Originally written by Andrej Karpathy, the current director of AI at Tesla, the library has since been open sourced and extended by the contributions of the community. You can easily train deep neural networks and even reinforcement learning models on your browser directly, powered by this very unique and useful library. This library is suited for those who do not wish to purchase serious hardware for training computationally-intensive models. With close to 9000 stars on GitHub, Convnetjs has been one of the rising stars in 2017 and is quickly becoming THE go-to library for deep learning.
BigML
BigML is a popular machine learning company that provides an easy to use platform for developing machine learning models. Using BigML’s REST API, you can seamlessly train your machine learning models on their platform. It allows you to perform different tasks such as anomaly detection, time series forecasting, and build apps that perform real-time predictive analytics.
With BigML, you can deploy your models on-premise or on the cloud, giving you the flexibility of selecting the kind of environment you need to run your machine learning models. True to their promise, BigML really do make ‘machine learning beautifully simple for everyone’.
So there you have it! With Microsoft, Amazon, and Google all fighting for supremacy in the AI space, 2018 could prove to be a breakthrough year for developments in Artificial Intelligence. Add to this mix the various open source libraries that aim to simplify machine learning for the users, and you get a very interesting list of tools and frameworks to keep a tab on. The exciting thing about all this is – all of them possess the capability to become the next TensorFlow and cause the next AI disruption.
