The term machine learning has become very popular lately, and its applications are shedding light on multiple aspects of human progress. However, because it is part of AI concepts, also, it is pretty expensive to implement the same, ML applications are kept away from small businesses’ reach. On the other hand, if it is projected as a service to be rendered, you can jump-start and ML initiate without much investment.

With machine learning cloud service, you can build ML working models. Here in this document,  we feature some of the best machine learning platforms on the market, also, some insights ML infrastructure and more.

What Exactly do You Mean by Machine Learning as a Service?

Machine learning as a service (MLaaS) is an overall term for all various cloud-based platforms that solve the major infrastructure issues such as data pre-processing, model training, and model evaluation. This can be achieved through predictions; for prediction results can be combined with your internal IT infrastructure through REST APIs.

Amazon Machine Learning services, Azure Machine Learning, Google Cloud AI, and IBM Watson are the four major players in this particular scenario. It ensures quicker model training and deployment. This document talks on these platforms, also compares machine learning APIs these vendors offer.

MLaaS For Custom Predictive Analytics

Predictive Analytics using Amazon ML

Amazon machine learning services are available in two levels.

  1. Predictive analytics with Amazon ML
  2. SageMaker tool for data scientists

Amazon machine learning for predictive analysis is known as one of the best-automated solutions in the market as of now. It is ideal for deadline-sensitive operations. Moreover, the service is capable of loading data from diverse sources including Amazon RDS, Amazon Redshift, CSV files and more. All data processing operations will be done automatically. The service can identify the categorical as well as numerical fields, also, it won’t ask users to any further data preprocessing methods.

The predictive capacity of Amazon ML  includes three options. Binary classification, Multi-class classification and Regression. And it will not support any unsupervised learning methods; for the user have to select a target variable to label it in a training set.

Amazon Sagemaker for Data Scientists

Sagemaker is simply a machine learning habitat build to facilitate data scientists with tools for quick model building and deployment. The best example for this is Jupyter- an authoring notebook introduced to simplify data exploration and analysis. In addition to this, Amazon has built-in algorithms to handle much larger datasets and computations in distributed networks. Some of those are listed below.

  • Linear learner
  • Factorization machines
  • Image classification
  • Seq2seq
  • K-means
  • Principal component
  • Latent Dirichlet allocation
  • Neural topic model (NTM)
  • BlazingText
  • Random Cut Forest

Apart from this, the Sagemaker offer great freedom to data scientists; It let them define and use their own datasets.

Microsoft Azure Learning Studio

Much similar to Amazon, Microsoft’s machine learning services depicts an environment for data scientists. And guess what! It is much flexible and convenient for out of the box algorithms.

Services from Azure can be divided into two. : Azure Machine Learning Studio and Bot Service. Azure machine learning studio is the main MLaaS package of Microsoft. All the major operations inclusive of data exploration, preprocessing, choosing methods, and validating modelling results can be done using a graphical drag-and-drop interface. Moreover, the Azure studio supports studio supports around 100 methods that address classification (binary+multiclass), anomaly detection, regression, recommendation, text analysis and more.

Azure MLaaS

Azure MLaaS is a set of ML-focused products, built with an aim to help to build and deploy models at scale, using any tool or framework. However, Aure services offer complete lifecycle management; It keeps track of all experiments, store code, configures parameter settings and environment information to make it simple to rank, search, and replicate any previously done experiments.

Besides, the services involve a support system to build models, experiment with them, and use a broad variety of open source components and frameworks. And so, it is considered ideal for experienced data scientists to work on it.

Google Prediction API

Fundamentally Google offers AI services on two different levels; a machine learning engine for data scientists and highly automated Google Prediction API. However, Google Prediction API is no more in the game; for they have pulled the plug on April 30, 2018. However, the limited approach of Google prediction API solves two main issues: classification (both binary and multiclass) and regression.

The problem was Google refused to disclose which algorithms aid drawing predictions, also, restricted engineers to customize models. On contrary to this, it was adequate for running machine learning within tight deadlines. Because the service didn’t get the recognition Google expected, they choose to fall back for a while. Anyhow, they are planning for a come back with g Cloud AutoML- the new product currently in alpha.

Google Machine Learning Engine

Unlike their prediction API, Google ML serves the needs of savvy data scientists. The engine is very flexible and so suggests utilizing cloud infrastructure with TensorFlow as a machine learning driver. Above all, Google is in the process of testing the tools such as XGBoost, sci-kit-learn, and Keras.

Also, you may have heard about the Tensorflow. It is a very familiar open source machine learning library of various data science tools rather than ML-as-a-service.

IBM Watson Studio

Whether you are involved in python or even R Machine Learning Development, IBM Watson is a term you must have heard about. IBM Watson studio is a single machine learning, built with an intention to help both newbies and experienced data scientists. Basically there two varied approaches to Watson studio; automated and manual. Plus, the studio has a model builder so efficient and capable to deploy a fully automated data processing and model building interface. Start processing data, preparing models, and deploying them into production has made simple.

The automated approach involves three main kinds of tasks. They are binary classification, multi-class classification, and regression. You can either choose either a fully automated approach or manually pick the ML method to be used.

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