Predictive APIs

PredicSis AI,
PredictionIO and Seldon01
Google Firebase aims to catalyse
the app market with free
backend tools
02
Check out the APIs of Google
Analytics competitors03
Watson Discovery, an IBM API
adding value to corporate data04
Predictive APIs
A new level to value data

01
PredicSis AI,
PredictionIO and Seldon,
three analytic services based on predictive APIs
PredicSis AI, PredictionIO and Seldon are three platforms using APIs
to collect and manage large volumes of aggregated data that is used
to create predictive models. Here we explain how they work and
their main features.
www.bbvaopen4u.com

The value of analytics as a key element in business
decision-making is beyond question. Companies
have gradually incorporated analytics and the
concept of metrics of success into their decision-
making processes, although it is something not yet
universally extended. There are still many
companies that base all their decisions on the
intuition of their senior and experienced
professionals or directly on the decisions of their
www.bbvaopen4u.com 01. PredicSis AI, PredictionIO and Seldon
CEOs. Within the analytics field there are some
interesting tools that go a step further: they base
their analyses on the power of predictive APIs,
which generate value through machine learning
and continuous learning.

www.bbvaopen4u.com
Predictive APIs are enabling the accessible use of
machine learning. They detect data patterns and
assign the probability that a future fact belongs to
that particular pattern, and they generate an
efficient prediction model. These APIs enable
developers to create models using historic
information in financial institutions to detect
fraud, in large corporations to control price
policies, in electric companies to anticipate
demand, etc. There are numerous examples.
There is analytical software in the market that
uses machine learning and data to meet business
goals.

PredicSis AI is a platform that is essentially aimed at
certain technical, analysis and business profiles in
any company: company executives, software
engineers, account executives, business
analysts, DevOps teams and data scientists. To
test it, any company can request a trial by filling
out a form on their website.
There are four steps in the process that establish
business predictions:
• Implementation of indicators: PredicSis AI can
be used to gather a large amount of aggregated
data from many different sources. This large set
of information is the basis from which decisions
will be made.
• Construction of segments: it is essential for
professionals in general, but much more for
those who have no technical training, to
establish optimal segments to discover the real
relationships between data, especially if these
relationships come from business decisions. It
shows a large volume of information using
simple and relevant ideas.
• Prediction model: in this phase, PredicSis AI
generates a formula or model that is used to
predict future behavior. In this process, it is
important to control the deviation of this
prediction. If there is a large deviation of values,
business decisions may be wrong.
• Final prediction: based on the three previous
steps, the model uses the most recent data to
generate the prediction using the target
audience. The predictive model can improve the
performance of campaigns and processes.

PredicSis AI is machine learning
software that uses supervised
learning algorithms to create
models. Professionals who use
the platform can access it
through a graphical interface in
a web browser, as if browsing
the internet, or through a
programming interface using
their SDK in Python or through
calls to their API REST.
Apache PredictionIO
Event Server
Engine 1 Engine 2
Engine 3 Engine 4
Mobile App
Email
Campaign
Website
Data Query via REST Predicted Result

Apache PredictionIO is an open source machine
learning server built on the top of the stack so
that developers and data scientists can create
predictive engines and models with business
goals. If you look at the image above,
PredictionIO can include prediction models and
machine learning processes in a mobile
application. Doing this from scratch requires a
lot of effort, time and higher costs in training
an algorithm. PredictionIO is nothing more than
a LAMP server for the analysis of data through
predictive models, which is responsible for the
entire cumbersome process of managing the
algorithms, their training, their implementation
at the top of an application where they are
executed, the different dependencies, etc.

This service has the following characteristics:
• Responds to dynamic queries in real time.
• Unifies data from different platforms in batches
or in real time.
• It has machine learning libraries and data
processing such as Spark MLLib and
OpenNLP. Spark MLLib contains logistic
regression algorithms and support vector
machines (SVM), Bayesian regression tree
models; least squares techniques; Gaussian
mix models; analysis of K-means clustering;
latent dirichlet allocation (LDA); singular values
decompression (SVD); principal component
analysis (PCA); linear regression; isotonic
regression, etc.
• Facilitates data infrastructure management.
Apache PredictionIO can be installed as a
complete machine learning stack, with
Apache Spark, MLLib, HBase, Spray
and Elasticsearch.

Seldon is a predictive platform that provides
content recommendations that are built on a
Kubernetes cluster. Kubernetes is an open source
system created by Google to program the
deployment, scaling and monitoring of applications
packaged in containers, hosted in the cloud and in
need of computing. This system is present in
search engine projects as relevant as Google Drive
or Google Maps. The packaging provided by
Kubernetes allows users to take the applications to
Seldon
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any platform and execute them, which can be
Amazon Web Services, Google Cloud Platform or
Microsoft Azure.

Some of the most important Seldon characteristics
are:
• Content and product recommendations: Seldon
allows users to capture and record user actions
through their REST API, and then use that
information to deliver personalized
recommendations to other users. Seldon's
infrastructure consists of a set of layers: a real-
time layer, responsible for managing real-time
predictive API requests; the storage layer, which
manages the storage of the various components
of the infrastructure; and the statistics layer,
which monitors and analyzes the system in
operation.

• Making predictions: the aggregated data,
which is the basis of any predictive model, is
sent to the platform via the REST API in real-
time. The application development interface
collects data from multiple sources to create
the predictive model. Usually, these data are
sent in JSON format and then a data modeling
process is created using algorithms, among
other reasons because JSON is not the best
format to create machine learning models.

02
Google Firebase aims to catalyse
the app market with free backend tools
Firebase is a backend as a service platform offering a series
of app development tools: data storage and synchronization
in the cloud, measuring user behavior and solutions for
monetizing products.
www.bbvaopen4u.com

Everyone knows that Google has important
interests in the consumption of content and
services on mobile devices. It has an app store,
Google Play; it has a mobile operating system like
Android; it has recently launched a terminal like
Google Pixel; and before all this came the devices
from its Nexus ranges. At the beginning of October
2015 the search engine launched its Accelerated
Mobile Pages, its fast-loading open standard for
consuming information on mobile networks. It also
has its Progressive Web Apps project, a mixture of
websites and apps, and our topic today: Google
Firebase, its SaS (Software as Service) platform for
storing and synchronizing data for apps in the
cloud.
www.bbvaopen4u.com 02. Firebase

Firebase is based on four major business pillars,
which it intends to leverage as a catalyst for hosting
third-party apps:
• A series of features for the development of
mobile apps: Google offers data storage in the
cloud, user authentication processes, database
service, error reporting and more.
• Analytics service: Google offers Firebase
Analytics, an analytics service included as part
of the free service for measuring user behavior
within the products from the same metrics
panel.
• Resources for growth: the search engine looks
at distribution for growing the audience –for
example, through notifications. This includes
everything related to advertising within
Adwords.
• Monetization: Firebase is integrated with a
monetization tool called AdMob, which includes
advertising in apps.

The only thing you need to develop a project with
Google Firebase is to have a Gmail account, the
same as for almost all the search engine's products.
Firebase gives app developers a platform that
stores and synchronizes the information housed in
a NoSQL database in the cloud with each
application. These data are always synchronized in
real time and continue to be available even when
the app loses its Internet connection.
How to develop apps with Firebase

This NoSQL database is known by the name of
Firebase Realtime Database. It stores the data in
the JSON format, and synchronizes them with
all the customers who happen to be connected
at that time. The idea is that when a
development team creates an application with
Firebase's Android, iOS or JavaScript SDKs, all
those customers share the same instance within
the NoSQL database, and the updated data is
always received in real-time in response to the
users' requests.
Once a free account has been set up in the
Firebase console, each new app in the console
is identified with a single URL that will serve to
both store and synchronize the data in the
database and to authenticate users. New apps
can be registered within the console, and also
administered or deleted. Here again, changes
can be made in the database and the user
authentication can be administered in each
project. This is the Firebase operations center.

User authentication with Firebase
With the Firebase Authentication service, the whole
user identity authentication process is done under
market standards such as OAuth 2.0 and OpenID
Connect. The idea is that the apps should be capable
of recognizing the user's identity, store their personal
details and offer the same personalized experience
from any device and from any operating system.
To start a user session in an application housed in
the cloud like Firebase, you need to provide an email
address and password, or use an OAuth token from
a provider of federated identities if you log in
through popular external services like Facebook and
Twitter, or Google Plus.

Storage and analytics with Firebase
The other two major pillars of Firebase are its
storage and its analytics service, designed for
metrics to measure user behavior. The first product,
Firebase Storage, offers the possibility of hosting
images, audio and video and any other content
generated by the users themselves. Firebase
Storage is supported by Google Cloud Storage, a
larger service that acts as back-up. The aim of this
storage is to give service to applications that store
and provide content created by their users.
Although Firebase Storage is backed by Google
Cloud Storage, that doesn't mean this is a non-
scalable service – quite the reverse. Its scalability
occurs automatically whenever required by the
customer. Access and administration of the data

accumulated in Firebase Storage is through its SDKs
or through Google Cloud APIs, which manage all the
information in two standard formats: XML and JSON.
It is worth noting that Firebase Analytics is a free and
almost unlimited solution. In fact, it has the capacity
to generate reports on metrics from a total of 500
events, a fairly high number for a totally free service.
These events can be implemented in any native
application for whatever environment (iOS or Android)
through the analytics service SDK.

www.bbvaopen4u.com
03
Check out the APIs of
Google Analytics competitors
Chartbeat, Webtrends and KISSmetrics are three good examples of
tools that, thanks to their APIs, can compete perfectly with Google
Analytics, although the competition scenario is not that simple.

Google Analytics is one of the market leaders for
web analytics, perhaps due to the huge company
that is behind the product and the high possibilities
of its free service. But it's not the only one. There
are other companies that have truly competitive
web analytics platforms with some interesting
features. What's more, in a particularly delicate
field, they can compete with the huge search
engine: they are tools that have their own
application development interfaces for
extracting, managing and processing third party
data. What gives its rivals the most problems is
Google's ability to link Analytics with other products
such as Adwords, the goose with the golden eggs
and the PPC (Pay Per Click) global reference
platform, and Audience, the human behavior
measuring tool.
Google makes it possible to centralize all the
important metrics in one tool such as Analytics,
both on the product side and the commercial side.
Nevertheless, there is life beyond Google Analytics.
This list shows that:
www.bbvaopen4u.com 03. Check out the APIs of Google Analytics competitors

Chartbeat is the king of real-time metrics, a sector
in which Google Analytics is yet to find its place. It's
true that Analytics has an access report in
this real-time, but not at the same level as
Chartbeat. Today, Google Analytics is a tool with
which customers must wait 24 hours before
getting results from the day before, and which is
focused on obtaining insights from the temporary
contextualization of the metrics. The idea is to find
elements that generate patterns from the previous
six months, years or two years. The important
aspect of metrics, and even more so in Google
Analytics, is the data trend and the explication of
reality it provides in relation to all that information.
Likewise, Chartbeat has real-time and short history
reports.
This sub-division is important because the
Chartbeat REST API also works. The platform's
applications development interface operates by
updating data is these two ways: the real-time
data contained in the API is updated every three
seconds, making this the live margin of error of the
metrics, and then there is the information that is
accumulated and displayed in certain time periods
by the user.
The Chartbeat REST API enables developer teams
to access the data they contain using simple HTTP
requests (GET). As a result of these requests, a
data package is always returned to the customer in
JSON format.
1. Chartbeat

Furthermore, the Chartbeat REST API returns
specific content consumption data in video
format. To give a couple of practical examples of
the video API:
• This GET call returns the most successful
videos from a website:
http://api.chartbeat.com/live/video/videos
/v1/?host={domain}&apikey={apikey}
• In exchange, this call returns specific
information about each video:
http://api.chartbeat.com/live/metrics/?hos
t={domain}&apikey={apikey}&names={na
mes}

Webtrends is another of the platforms that rivals
with Google Analytics within the analytical sector.
This tool has some interesting features:
• Unification of data that comes from different
sources to create ad hoc reports that provide
an overview for customers.
• Collection of an unlimited number of variables.
These variables can be used when they are
required in all types of analytical reports within
Webtrends.
• Ability to create customized reports that can be
shared between the different displays and
shared with other users of the tool.
• Possibility to create segments that can be
saved in a personal library to be recovered
when required in the long-term.
• Webtrends has various application
development interfaces to provide service to
third companies, including a data extraction
API, another to send information
to Webtrends servers and another API
dedicated to real-time data management.
2. Webtrends

The API for the Webtrends Analytics data extraction
is a REST API that allows developers to gather
information from one URI. The interface provides
macros that make it possible to request data from
specific periods of time, it supports information in
numerous formats such as JSON, XML, HTML and
CSV, and makes it possible to create AJAX
applications. This API also facilities data caching, an
interesting feature to greatly improve performance.

KISSmetrics is another of the serious rivals of
Google Analytics. KISSmetrics has a competitive
advantage over other solutions in the market: it
focuses on giving an analysis solution to its
customers that makes a visual representation of
the life cycle of a user with a product or a
particular service. This is only possible through the
functionality of the KISSmetrics API.
• Automatic tracking of user IDs, without this
data forcefully resulting in their actual
identification, normally via email or username,
which are elements frequently used on
websites or native applications, in both iOS and
Android. The API is also able to collate and
determine when the email and username
belong to the same person to avoid erroneous
duplication of metrics. From this tracking, data
and information are extracted from tracking
the events most used within a metrics tool.
• In addition, KISSmetrics has an A / B test API,
an essential tool for setting priorities when a
company has an online project.
3. KISSmetrics

04
Watson Discovery,
an IBM API adding value to corporate data
IBM’s Discovery API allows companies to structure and understand,
with certain ease, the large mass of data they have, in many cases
from very different sources, such as documents from office
applications, HTML documents, JSON or PDF format, etc.
www.bbvaopen4u.com

People always talk about data as the key that opens
all doors. And they're not wrong. But data is more
than obtaining aggregated information (in volume
or raw), and companies must be able to generate
true business value with it. Achieving this isn't easy.
There are a handful of companies in Spain that are
extracting business value from their data, others
are in the process of having aggregated
information from which to extract this benefit, and
the rest are looking for solutions to have this
information; solutions at infrastructure, back and
front level.
Whilst there are companies who are searching for
this added value, others are offering products and
services to these third parties in order to monetize
their data. IBM is possibly one of the main
companies in the world of artificial intelligence and
in the creation of products and services in fields
such as processing natural language and machine
learning. Most of these products revolve around
Watson, the artificial intelligence computer system
developed around DeepQA technology (Deep
Question Answers). Watson is simply a computer
system to search for answers.
www.bbvaopen4u.com 04. Watson Discovery

This searching for answers is based on the
processing of natural language, a computer and
artificial intelligence science that studies and
improves interaction between machines and
human beings, especially at language level. Thanks
to this, Watson can understand the world of
human beings using senses, learning and people's
own experiences. How does IBM Watson resolve
questions giving relatively correct answers?
• Watson dives into millions of documents to find
answers to the questions it is asked. This is
possible because it accumulates large amounts
of information from different sources such as
Microsoft Word, websites, PDF or JSON
documents, documents in cloud applications.
• It uses a scoring algorithm to assess the quality
of the answer.

Out of all the products related with Watson,
one of the most interesting services is
Discovery. The main goal of this product is
to help companies easier understand their
large quantities of data. This is possible
thanks to three situations that are not at all
simple in AI:
Discovery, an interesting
product in Watson

• Analyzing large amounts of data that may
originally be in a format or conditions that are
not the most favorable. The information
normally comes from completely unstructured
sources from local sources as disparate as
emails and chats. Most data scientists spend a
large amount of time collecting, cleaning and
organizing unstructured data. It is arduous,
complex and necessary work to obtain
information with real value.
• IBM Watson is also able to analyze structured
and semi-structured information from other
sources such as databases or the content
management systems (CMS) that companies
tend to have.
• Artificial intelligence is normally able to give
one determining response to a question or
query. The really difficult aspect is for a system
or platform to be able to contextualize and
provide alternatives. The goal of Watson
Discovery is to contextualize data and provide
responses.

The Watson Discovery API
IBM boasts a service that
doesn't need a PhD to be able
to use it. With know-how, a call
to a REST API is necessary. This
is the way in which the entire
process of extracting,
transforming and loading (ETL)
content is carried out and
directly integrated with
Watson's capabilities of
importing documents and large
volumes of data. From there,
developers can identify tasks,
analyze data, explore
information, search, etc.
In the end, Watson's large block
of APIs allows users to:
• Track, convert, enrich and
normalize data.
• Explore the content of the
client, as well as from
totally free public content
sources, or content that is
under license.
• Add a new value to the
information collected:
Watson uses natural
language processing to
extract concepts, establish
relationships and link
feelings to data that has
been collected.
• Simplify development
while still providing access
to APIs.

The goal of Discovery is to put all the capabilities
that IBM Watson has in technologies, such as
computing and natural language processing, into a
service that is more accessible to all, with the idea
of supporting all types of sectors and contents,
especially those that handle large amounts of data
such as health. With Discovery, the idea is to make
it very simple to prepare unstructured data, make
the necessary queries to locate the information the
user wants and integrate all that into a new
application, a platform or a solution.

Watson Discovery has three different ways of relating,
contextualizing, connecting, and finding brilliant ideas from
among all the data that a company has after the process of
tracking and organizing the unstructured information:
• Using the API: this is the best possible formula if the
content loading is directly integrated with an existing
application or service.
• Discovery tools: the ideal method if the user wants to
directly upload this content to a local service for
querying and using.
• Using the data tracker: the data tracker is a command-
line tool that helps developers take their documents
from the repositories they are in (for example a
database) and push them to the cloud to be used by
Watson Discovery.
How content is added with Watson Discovery

Ebook: APIs, key in the
development of cloud apps
Ebook: Fintech´s next wave Ebook: Big Data in the Fintech
ecosystem
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