Machine Learning as a Service is becoming the next big thing with data becoming cheaper, data science becoming possible and processing power getting better. The growing trend of shifting data storage to cloud, maintaining it and deriving the best insights from it has found an ally in MLaaS which provides solutions at a reduced cost. It basically helps developers or organisations benefit from machine learning cognate cost, time and without human intervention and additional programming.
Much like SaaS, IaaS and PaaS, MLaaS provides users with range of tools as a part of a cloud computing service, which includes – facial recognition, natural language processing, data visualisation, image recognition and deep learning. It is supported by algorithm such as deep neural networks, convolutional neural network, Bayesian networks, probabilistic graphical models, Restricted Boltzmann machine and pattern recognition, among others.
Here we take a lowdown at few machine learning tools which will benefit your organisation:
There is a high level of automation available with Amazon Machine Learning, which offers visual aids and easy-to-access analytics to make machine learning accessible to developers without having to learn complex machine learning algorithms and technology. It also offers companies an easy, highly-scalable-on ramp for interpreting data. It helps businesses build machine learning models without having to create the code themselves.
Amazon Machine Learning can help generate billions of predictions daily, and serve those predictions in real-time and at high throughput, claims the company. According to Amazon, Amazon ML is based on the same proven, highly scalable, machine learning technology used by Amazon to perform critical functions like supply chain management, fraudulent transaction identification, and catalog organization. The Amazon ML service is based on pay-as-you-go pricing model. There are no minimum fees required. The data analysis and model building charges are for $0.42 per hour, with separate fee for batch prediction ($0.10 per 1,000 predictions, rounded up to the next 1,000) and the real-time predictions ($0.0001 per prediction, rounded up to the nearest penny). Charges for data stored in Amazon S3, Amazon RDS, or Amazon Redshift are billed separately.
This machine learning for beginners and experienced data scientist. It offers a range of tools that is more flexible for out-of-the box algorithms. Azure supports a range of operating systems, programming languages, frameworks, databases and devices. It also provide cross-device experiences with support for all major mobile platforms. With the help of an integrated development environment called Machine Learning Studio, the developer can also build data models through drag-and drop gestures and simple data flow diagrams. Thus, it not only helps save a lot of time but minimizes coding through ML Studio’s library of sample experiments. Azure ML Studio also has a huge variety of algorithms, with around 100 methods for developers. The most popular option in Microsoft Azure Machine Learning Studio is available for free, which just requires a Microsoft account. This also includes free access that never expires. It also gives 10 GB storage, R and Python support and predictive web services. However, the standard workspace of ML Studio is for $9.90 and you will require a Azure subscription.
Google Cloud Machine Learning Engine is highly flexible, which offers users an easy alternative to build machine learning models for data of any size and type. Google machine learning engine is based off TensorFlow project. This platform is integrated with all other Google services like Google Cloud Dataflow, Google Cloud Storage, Google BigQuery, among others. But the platform is mostly aimed at deep neural network tasks. You can sign up for a free trial to access Google Cloud Machine Learning. There is no initial charges applied and once you sign up you get $300 to spend on Google Cloud Platform over next 12 months. However, once your free trial ends, you have to pay for the subscription is chargeable.
Watson Machine Learning runs on IBM’s Bluemix, which is capable of both training and scoring. With the help of training function, developers can use Watson to refine an algorithms so that it can learn from dataset. And scoring function helps in predicting an outcome using a trained model. Watson addresses the need of both data scientist and developers. The notebook tool of Watson can help the researchers learn more about machine learning algorithms. According to a report, Watson is intended to address questions of deployment, operationalization, and even deriving business value from machine learning models.
The visual modelling tools of IBM’s Watson machine learning helps users quickly identify patterns, gain insights and make decisions faster. The open source technologies helps users to keep utilising their own Jupyter notebooks with Python, R and Scala.
To use the service, you will need to create an account with Bluemix for the free trial. After your 30 days free trial gets over, you need to choose between Lite, Standard and Professional. While Lite is available for free, under 5,000 predictions an 5 compute hours. Standard and Professional charges you flat rate per each thousand of predictions and per total number of compute hours. Standard is available for $0.500 per 1,000 predictions and Professional is for $0.400 per 1,000 predictions.
Haven OnDemand machine learning service provides developers with services and APIs for building applications. There more than 60 APIs available in Haven which include features like face detection, speech recognition, image classification, media analysis, object recognition, scene change detection, speech recognition. It also provides powerful search curation features that enables the optimisation of search results for developers. With the help of this machine learning services organisations can extract, analyse and index multiple data formats including emails, audio and video archives. The Haven OnDemand pricing plans start at $10 per month.
BigML is easy to use and has a flexible deployment. It allows data imports from AWS, Microsoft Azure, Google Storage, Google Drive, Dropbox etc. BigML has more features available that are integrated into its web UI. It also has a large gallery of free datasets, models. It also has a useful clustering algorithms and visualizations. It has anomaly detection feature that helps in detecting pattern anomalies, which will help you save time and money.
According to a blog, BigML Datasets are very easy to reuse, edit, expand and export. You can easily rename and add descriptions to each one of your fields, add new ones (through normalization, discretization, mathematical operations, missing values replacement, etc), and generate subsets based on sampling or custom filters. It has a flexible pricing, you can choose between subscription plans, starting from $15 per months for students. You can perform unlimited tasks for datasets up to 16 MB for free.
There is also a pay-as-you-go option available in BigML. For companies with stringent data security, privacy or regulatory requirements, BigML offers private deployment that can run on their preferred cloud provider or ISP.
MLJAR is a ‘human-first platform’ for machine learning and is available in beta version. It provides a service for development, prototyping and deploying pattern recognition algorithms. It provides features like built-in hyper-parameters search, one interface for many algorithms, among others. The get started, users need to upload dataset, select input and target attributes and the machine learning service provider will find the matching ML algorithm. MLJAR is also based on pay-as-you-go pricing model. Once your 30 days free trial gets over, there is a different subscription plan for professional developers, startups, businesses and organisations. When you start the subscription, you get 10 free credits for a start.
Arimo uses machine learning algorithms and a large computing platform to crunch massive amounts of data in seconds. It describes itself as ‘behavioural AI for IoT’, which learn from past behaviour, predicts futures action and drives superior business outcomes. The service provider is based on deep learning architecture, that works with time series data to discover patterns of behaviour.
Domino is a platform that support modern data analysis workflow.This platform supports language agnostic like Python, R, MATLAB, Perl, Julia, shell script, among others. Domino is a platform for data scientist, data science managers, IT leaders and executives. This machine learning service can be implemented on-site or in the cloud. Developers can develop, deploy and collaborate using the existing tools and language, claims the company.It also streamlines knowledge management with all projects stored, searchable and forkable. It has a rich functionality in an integrated end-to end platform for version control and collaboration along with one-click infrastructure scalability and deployment and publishing.
It is the collaborative data science platform for data scientists, data analysts and engineers to explore, prototype, build and deliver their own data products more efficiently. The platform supports R, Phyton, Scala , Hive, Pig, Spark etc. It uses customisable drag and drop visual interface at any step of dataflow prototyping process. The platform provides machine learning technologies like Scikit-Learn, MLlib, Xgboost, H2O, among others in a visual user interface.
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