As the world moves towards rapid technological advancements, terms like ‘BigData’, ‘Artificial Intelligence’, ‘Robotics’ are heard on a regular basis. As vast as the study of these fields are, they share a common functionality and performance unit known as Machine Learning. Machine Learning (ML)is a way of making computers act without being explicitly programmed where the system itself analyses the data provided and formulates a decision.
Software Developers and Data Scientists unlock potential for customers to do more than they otherwise could in a timely manner by building Application Programming Interface (APIs) for Machine Learning. API is a set of code between two or more software programmes. It delivers your request to the provider that you are requesting it from and then delivers back the response to you.
APIs of ML simplify the process for the clients by providing a primary structure through which the client can develop their model further according to the needs of the application. The client need not know what all is going on the back-end of the programme.
There are many ML APIs that work as a service provider (MLaaS) where the client can approach the pre-defined toolsets to handle the modulations and performances of language processing, image recognition, speech translation etc.
Being a established giant in the market, Amazon Web Services is one of the top revenue generators of Amazon.
The pay-as-you-go mega-service provider is a utility in ML and ML adjacent services also. Few of it’s common and vividly used ML APIs are:-
As the name suggests, Comprehend refers to the ability to understand something. The operations of synchronizing the machines to understand the user inputted text files falls under the subject of Natural Language Processing (NLP). Comprehend NLP service uses machine learning to decryt or find insights and relationships in the structured or unstructured text material.
It can work as a customer sentiment analyst where the service becomes familiar with the lingual; pulls out the dominant terms; understands the nature of the text and further analyse the text by splitting the words and sentences (tokenization); and automatically segregates the collections of text files into appropriable topics.
It is an exclusive service to improve user’s Java Code experience by operating in two parts - ACG Profiler and ACG Reviewer. The CodeGuru Profiler collects runtime performance data from your live applications using ML algorithms to help the user in deducting and correcting the most expensive or inefficient line of codes. It basically improves the overall performance by removing the CPU bottlenecks.
While the CodeGuru Reviewer doesn’t highlight syntax errors, it identifies more complex problems and suggest improvements from the AWS CodeCommit, BitBucket, and Github severs repositories on issues like concurrency, resource leak preventions and input validations.
It is the service behind Amazon Alexa! This ‘chatbot’ deep learning technology is now easily available to any developer to build efficient and organised workplace conversational models.
This is one of most powerful and efficient AWS tool to provide Search capabilities in user’s websites and applications. Three features Alexa Kendra will soon start working upon are:
SageMaker is a fully managed service that gives powerful compute engines and huge processing power by selecting the user based configurations and creating a virtual PC.
It provides every developer and data scientist with the freedom to frame and implement ML models using the SageMaker toolkits like Studio, AutoPilot, Ground Truth, Experiments, Notebooks, Marketplace,Debugger, Model Monitor and Neo.
The SageMaker is a well-packed tool that reduces the complexity from each step of the ML workflow so the user can conveniently deploy more ML used cases, anything from debugging algorithms to utilising and sharing the pre-built test codes to tracking the iterations made to a particular model .
Most frequently used Google Cloud Services include Computing and hosting,Storage,Databases,Networking,Big data and Machine learning. Few of the Machine Learning APIs are as follows:
This API works on pre-trained machine models through REpresentational State Transfer(REST) and Remote Procedure Call (RPC) APIs. It is based on optical character recognition where it can detect objects and faces, label images by reading printed and handwritten texts and thus convert valuable meta data into image catalogues.
The Text Analysis feature seems to be quite effective in Google Cloud Natural Language where it can find relation in sentences, detect Point-of-Sale (POS) tags and provide morphological analysis to find relations between words. This API falls under Conversational AI, which also supports speech APIs to convert audio to text or vice versa, provided conversational virtual assistances and dialogue-flow capabilities similar to Amazon Lex.
The translation API uses deep network for translation and can convert and detect over 100 languages, the maximum any platform based service provider has been capable of so far. The pricing for conversion of language is different from that detection of language.
Azure Machine Learning services are broadly divided into 5 basic categories through which they extend their solutions and trainings to ML models.
Along with chief capabilities of content personalisation and moderation, Azure is the only major platform providing Anomaly Detector Preview as a service which ingests time series data and allows the customer to fine tune the sensitivity to potential anomalies.
The QnA Maker is an additional API service provided by Azure other than the usual Text Analysis and Translations. It allows the user to create a conversational interrogative and responsive layer from the pre-existing data. The Immersive Reader is the reading technology that empowers users of different age groups with features like reading aloud.
The security technique of Speaker Recognition identifies and verifies the person speaking based on unique voice signatures created during voice enrolments. Like other major platforms, it also provides features to convert speech to text and vice-versa.
The Computer Vision API can be used for digital market campaigns, facial recognitions, extracting text key value pairs from documents. The independently avaialble Ink Recogniser API can recognise digital handwritings, shapes and layout of inked documents.
Further classified under Bing categories, the web search APIs can automate queries and spell-checks and make engine, entity, image, news, video and visual (search using images)searches.
Geneea ML API is based on Python Software Development Kit (SDK) and mainly consists of 4 elements:
The unique NLP feature of this API is its ability to perform Diacritics Corrections (the signs or symbols over alphabets can be automatically added indicating change in pronunciation). Like all other APIs it can detect text over 30 languages, analyse client’s sentiments, extract necessary information like places, products etc, or simplify the searches to broader topics.
The feature can also be accessed over experimental Kotlin SDK but should be avoided since it can be changed by Geneea in non-compatible ways.
This API detects news article by semantics tagging which are integrated and compartmentalised in its Knowledge Base either in generic or in private buckets. The tags are then customised (numbers,labels,ids etc) and reviewed according to the feedback given by user.
This can evaluate customer feedback by detecting topics customers talk about. Geneeva’s featured Keboola App is the simplest way to use this API.
This one is used to detect non-parametrised intent in a text. The feature can be used to detect the violation of parametrised tests.
The General and VoC APIs can be activated directly by the user but the Media and Intent APIs need prior authorisation from Geneea.
While the above mentioned foremost service API platforms can solely and efficiently act as a backbone for any business or e-commerce firm, there are many free API libraries available for experimentaion for individual users as well.
TensorFlow is an open source library powered by google and has numerous APIs (Keras, Distribution, Model Subclassing etc.) to access application packages for different kinds of ML models. It is based mostly at neural network tasks where the model can be trained to recognise patterns and is composed of neurons in different layers. The networks are iteratively improved by varying values in different layers.
As the readers could guess, this API is used in predicting data patterns and hence computes the probability of a future dependent event. This service is divided into three parts, responsible for applying and evaluating ML algorithms on engines.
The Event Server unifies events from multiple platforms and the Template Gallery can be accessed to download engine templates for different type of machine learning applications. This API is built on Apache software with the assistance of resourceful libraries like Apache Spark, MLLib, HBase, Spray and Elasticsearch.
It’s actually APIs running down to ones and zeroes providing abstractions at many levels. The Machine learning practitioners need to ensure that they are clear on their application requirements and issues they need to work upon since every complication cannot be resolved with ML APIs.
Also, the user should try and stick with the service provider they usually work and are most comfortable with unless there is a requirement which can not be fulfiled by the present service provider.
Go ahead and try the limitless powers of Machine Learning!
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