A Practical Guide to Enterprise Machine Learning Platforms By Tellago Research

Contents Overview ....................................................................................................................................................... 3

Key Characteristics of Enterprise Machine Learning Solutions .................................................................... 3

Cloud vs. On-Premise Machine Learning Platforms...................................................................................... 5

Enterprise Cloud Machine Learning Platforms ............................................................................................. 5

Azure Machine Learning .......................................................................................................................... 6

AWS Machine Learning ............................................................................................................................ 7

IBM Watson Developer Cloud ................................................................................................................. 8

Databricks ................................................................................................................................................. 9

On-Premise Enterprise Machine Learning Platforms ................................................................................... 9

Revolution Analytics .............................................................................................................................. 10

Dato ........................................................................................................................................................ 11

Spark MLib and Spark R ......................................................................................................................... 12

PredictionIO ............................................................................................................................................ 13

Scikit-Learn ............................................................................................................................................. 13

Summary ..................................................................................................................................................... 14

Overview

Machine learning is becoming one of the most important aspects of modern

enterprise applications. Recent years has seen an explosion in the innovation of

machine learning platforms taking it from a domain constrained to a few data

scientists to a mainstream developer audience. As a result, companies are now in a

position to build really comprehensive machine learning applications that were

completely impossible just 2-3 years ago.

The explosion in machine learning technologies doesn’t come without a price for

enterprises. As any other rapidly emerging technology trends, machine learning has

experienced a rapid growth in the number of new platforms and startups that

provide relevant machine learning capabilities for enterprises. As a result, many

enterprises struggle navigating the new ecosystem of machine learning

technologies and platforms.

This paper provides an analysis of some of the most relevant technologies in the

machine learning space along with experiences that Tellago’s data science practice

team has implementing machine learning solutions in the real world. The analysis

illustrated in this paper is solely based on practical experiences and not theoretical

exercises.

Key Characteristics of Enterprise Machine Learning Solutions

Integration with Mainstream Data Stores

The integration with diverse data stores is a key element for the mainstream

adoption of machine learning platforms. Databases, SaaS platforms, ERPs, CRMs

are just some of the data sources that can be relevant in machine learning

scenarios. The ability to seamlessly integrate with different line of business systems

drastically simplifies the adoption of machine learning platforms in enterprise

environments.

Integration with R and Python

R and Python have been the main platforms used in machine learning and data

science applications. Consequently, there are many widely adopted machine

learning frameworks implemented in R and Python. The interoperability with R and

Python libraries allows machine learning platforms to take advantage of well-

established data science practices and techniques implemented in those

frameworks. In that sense, enterprises can benefit from machine learning platforms

that can natively leverage R and Python libraries.

Simple Infrastructure

Scaling machine learning infrastructures can be a complex endeavor. Even worse,

the complexities around the configuration of machine learning infrastructures

sometimes become a friction point for the early adoption of machine learning

platforms. To avoid those challenges, enterprises should look for machine learning

platforms that can be relatively simple to setup and don’t require massive

investments in infrastructure. This will allow organizations to focus on the

evaluation of core machine learning capabilities instead of the infrastructure behind

them.

Programmatic Interfaces

Executing and evaluating machine learning models is often seen as an activity

exclusively performed by humans. However, incorporating machine learning models

into business applications is incredibly relevant in the enterprise. To achieve that,

machine learning platforms should support the programmatic execution of models

via APIs or mainstream enterprise programming platforms such as .NET or Java.

Monitoring and Management Tools

Monitoring and managing the execution of machine learning models is an essential

element to guarantee the adoption of these type of platforms in enterprise

environments. From the monitoring perspective, machine learning platforms should

enable both analytics about the results of executed models as well as operational

metrics related to the execution of those models. Additionally, organizations should

favor machine learning platforms that provide a simple but robust management

experience.

Extensibility

Until a few years ago, machine learning platforms were notoriously closed systems.

That factor really limited the mainstream adoption of these platforms in enterprise

environments, as many machine learning solutions require complex levels of

customizations that require extending the core platform. In that sense,

organizations should carefully evaluate the extensibility models of machine learning

platforms and analyze how those can help to optimize the platform for their specific

scenarios.

Cloud vs. On-Premise Machine Learning Platforms

A simple way to simplify the really crowded machine learning platform market is to

make a distinction between cloud and on-premise platforms. For many

organizations, the nature of the underlying infrastructure (cloud vs. on-premise) is

a determining factor in terms of which machine learning platforms to evaluate.

Deciding between and on-premise and cloud platform is always an interesting

dilemma for most organizations but its even more relevant when it comes to data-

centric platforms. While cloud machine learning platforms abstract the complexity

of the underlying machine learning infrastructure and are rapidly driving innovation

in the space, they lack the levels of control and extensity that you can achieve with

on-premise machine learning stacks.

The next section in this document provides an analysis of some of the most

relevant cloud and on-premise platforms in the machine learning space.

Enterprise Cloud Machine Learning Platforms Machine learning platforms are rapidly emerging as one of the most important

components of platform as a service (PaaS) technologies. While the first iteration of

cloud big data technologies focused on providing a seamless experience for hosting

and provisioning a Hadoop based infrastructure, the lead platforms in the space are

rapidly adding value data intelligence capabilities including machine learning. This

movement has been led by platforms like Microsoft, Amazon or IBM, which have

added sophisticated machine learning capabilities to their existing PaaS offerings.

Additionally, there is a large number of startups trying to provide specialized

machine learning cloud services that simplify the experience for organizations trying

to apply machine learning models to specific business scenarios. When analyzing

the cloud machine learning platform space, organizations should consider Azure,

AWS, IBM and Databricks as some of the leader in the space.

Azure Machine Learning Overview: Azure machine learning is a fully managed service included in the

Azure platform that allows the implementation of predictive analytics

solutions using machine learning. The service provides interfaces for building,

deploying and managing machine learning models and its tightly integrated

with other Azure services. Currently, Azure machine learning is included as

part of the Cortana Analytics suite.

Key Capabilities: Azure machine learning includes some of the following

capabilities

o Machine Learning Studio: Microsoft Azure Machine Learning Studio

is a collaborative, drag-and-drop tool you can use to build, test, and

deploy predictive analytics solutions on your data. Machine Learning

Studio publishes models as web services that can easily be consumed

by custom apps or BI tools such as Excel.

o API Generation: Azure machine learning provides the infrastructure

to expose machine learning models as APIs that can be

programmatically accessed by client applications. These APIs can also

be integrated with the Azure API Gateway to enable more

sophisticated management and monitoring features.

o R and Python Extensibility: Azure machine learning allows

developers to incorporate custom R and Python scripts into models.

This extensibility mechanism allows developers to implement machine

learning applications that combine the capabilities of Azure with many

of the popular R and Python machine learning frameworks in the

market.

Challenges: Azure machine learning is still relatively limited in terms of the

integration with on-premise data stores, which are predominant in the

enterprise. Additionally, we feel Azure machine learning can benefit for more

complete extensibility mechanisms beyond the ones provided by R and

Python scripts.

AWS Machine Learning Overview: Amazon Machine Learning is a native AWS service that makes it

easy for developers of all skill levels to use machine learning technologies.

Amazon Machine Learning provides visualization tools and wizards that guide

developers through the process of creating machine learning (ML) models

without having to learn complex ML algorithms and technology. Amazon

Machine Learning makes it easy to obtain predictions for your application

using simple APIs, without having to implement custom prediction generation

code, or manage any infrastructure

Key Capabilities: Azure Machine Learning enables some of the following key

capabilities:

o Model Creation: AWS APIs and wizards make it easy for any

developer to create and fine-tune ML models from data stored

in different data stores and query these models for predictions. The

service’s built-in data processors, scalable ML algorithms, interactive

data and model visualization tools, and quality alerts help you build

and refine your models quickly.

o Prediction Services: AWS machine learning provides the

mechanisms for quickly and reliably generate predictions for your

applications based on previously created machine learning models. The

prediction services can be elastically scaled using AWS infrastructure.

o Data Transformation DSL: AWS machine learning includes a domain

specific language (DSL) that allows developers to model

transformations on the data processed by machine learning models.

Data transformation implemented using these DSLs can be published

as “recipes” and reused across other transformation processes.

Challenges: The experience of getting started with AWS machine learning is

relatively complex compared to its competitors in the space. We believe the

AWS machine learning service can benefit from incorporating more visual

tools that facilitate the authoring of machine learning models. Another

challenging factor in AWS machine learning applications remains the

communication with on-premise data stores.

IBM Watson Developer Cloud Overview: IBM Watson developer cloud is a series of cognitive data services

included as part of the IBM Bluemix platform. The Watson developer cloud

includes services such as vision analysis, text analytics, text-to-speech

transformation, concept expansion, among a dozen of other that enable

developers to incorporate deep learning and cognitive data capabilities within

their applications.

Key Capabilities: The Watson developer cloud includes some of the

following capabilities.

o Text Analytics: Watson developer cloud provides a large number of

text analytics related services including relationship extraction,

concept insights, sentiment analysis etc. These services can be easily

integrated with other machine learning or business applications.

o Vision Analytics: Watson developer cloud provides a group of

innovative services that abstract key image analysis capabilities such

as face recognition, object detection, image link extraction etc. These

services can complement image libraries required in line of business

applications and solutions.

o Integration with Bluemix Services: Watson developer cloud is

included as part of IBM Bluemix and, consequently, is tightly

integrated with other Bluemix platform services. As a result,

developers can implement really robust applications that leverage

cognitive data services

Challenges: Watson developer cloud is a collection of APIs that enable

cognitive data capabilities. As a result, Watson developer cloud is typically

used as a complement to machine learning applications and can’t be

considered a complete machine learning solution.

Databricks Overview: Databricks is a cloud integrated platform that enables the

implementation and operation of Apache Spark applications. As part of the

current capabilities, Databricks provides strong support for Spark MLib and

Spark R.

Key Capabilities:

o Model Performance: Databricks provides a highly scalable

architecture that powers the performance of Spark MLib models. This

capability allows developers to focus on writing Spark MLib solutions

without worrying about the underlying infrastructure.

o Support for R: In addition to Spark MLib, Databricks provides support

for Spark R. This capability allows developers to write very

sophisticated applications that combine traditional machine learning

and R models to achieve optimal results.

o On-premise Support: One of the biggest advantages of Databricks is

that is completely based on Apache Spark. That model allows

developers to write machine learning applications that can seamlessly

work in both on-premise and cloud topologies.

Challenges: The current feature set of Spark MLib and Spark R is relatively

limited compared to some of its cloud competitors. Additionally, Databricks is

a Spark-exclusive cloud, which means that it doesn’t include complementary

platform services comparable the ones provided by PaaS solutions like Azure,

AWS or Bluemix.

On-Premise Enterprise Machine Learning Platforms Similar to the cloud space, the on-premise machine learning space is experiencing

an explosion in the number of technologies and platforms that enable the

implementation of enterprise-ready machine learning solutions. Differently from the

cloud space, new on-premise machine learning technologies seem to be actively

built on popular open source data science frameworks such as R and Python instead

of building proprietary stacks. As a result, many of the lead machine learning

platforms are also delivered as open source distributions. The following sections in

this paper evaluates some of the key on-premise machine learning stacks such as

Revolution Analytics, Data, Spark, PredicitonIO and Scikit-learn.

Revolution Analytics Overview: Revolution R Enterprise provides the infrastructure for

implementing enterprise-ready analytics applications based on R. Supporting

a variety of big data statistics, predictive modeling and machine learning

capabilities, Revolution R Enterprise is also 100% R. Revolution R Enterprise

supports a variety of analytical capabilities including exploratory data

analysis, model building and model deployment.

Key Capabilities: Revolution R provides some of the following key

capabilities:

o Scalable R: Revolution R Enterprise scales and accelerates R, running

R scripts in a high-performance, parallel architecture that supports

systems from workstations to clusters and grids including Hadoop and

enterprise data warehouses.

o Enterprise-Ready R Capabilities: Revolution R expands R with

enterprise-ready capabilities such as logging, instrumentation,

security, monitoring among other features that are essential to

operationalize R solutions in the enterprise.

o Integration with Mainstream Analytic Tools: Revolution R

provides integration with many of the most popular analytics tools in

the enterprise such as Tableau, Excel or Qlikview. Additionally,

Revolution R also integrates with traditional reporting platforms such

as Cognos, Business Objects etc.

Challenges: Revolution R is optimized for authoring applications in the R

language. Sometimes, this model results limited for the implementation of

complete enterprise applications. Additionally, the applications implemented

with Revolution R can be complex to integrate into other enterprise solutions.

Dato Overview: Dato enables the rapid development, simple deployment, and

robust management of real-time services and applications that use machine

learning. Dato leverages the advancements in Python machine learning

libraries to enable the implementation of highly sophisticated, enterprise-

ready machine learning solutions. The Dato platform includes three key

products: Graphlb Create, Dato Distributed and Dato Predictive Services.

Key Capabilities:

o Model Creation: Dato’s Graphlab Create is an extensible machine

learning framework that enables developers and data scientists to

easily build and deploy intelligent applications and services at scale. It

includes distributed data structures and rich libraries for data

transformation and manipulation as well as scalable task-oriented

machine learning toolkits for creating, evaluating, and improving

machine learning models.

o Scalable Execution: The Dato platform includes Data Distributed

which is a server product that allows distributed execution of machine

learning jobs on a cluster of machines. Jobs can include distributed

training of machine learning models, parallel model scoring &

predictions, distributed hyperparameter tuning, model ensembling,

and evaluation tasks. This capability abstracts the complexities of

scaling machine learning models in enterprise environments.

o API Access: Dato Predictive Services enables the execution of Dato

machine learning models as high performance APIs. This capability

allows developers to easily incorporate machine learning models into

new applications without having to use any proprietary libraries.

Challenges: As any new product, enterprises adopting Dato faced the

challenge of embracing a product without a large community of developers

and system implementers. However, the communities around Dato are

rapidly growing. Additionally, Dato is completely Python-centric which makes

it challenging to adopt by organizations without that in-house expertise.

Spark MLib and Spark R Overview: Apache Spark includes two main libraries for machine learning

applications: Spark MLib and Spark R. MLlib is Spark’s scalable machine

learning library consisting of common learning algorithms and utilities,

including classification, regression, clustering, collaborative filtering,

dimensionality reduction, as well as underlying optimization primitives. Spark

R is an R package that provides a light-weight frontend to use Apache Spark

from R. Spark R provides a distributed data frame implementation that

supports operations like selection, filtering, aggregation etc. (similar to R

data frames, dplyr) but on large datasets.

Key Capabilities: Spark provides the following key capabilities for machine

learning applications:

o Scalability: Because Spark MLib and Spark R are built on the Spark

platform; they enjoy the scalability and performance benefits of the

Spark architecture. In that sense, Spark machine learning models can

run across large topologies with hundreds of nodes and recover from

unexpected errors.

o Support for R: The addition of Spark R offers developers a very

unique option of combining R and machine learning models as part of

the same applications. More importantly, both Spark R and Spark MLib

are provisioned, scaled and managed using the same underlying

infrastructure.

o Developer and System Integrator Community: Apache Spark is

enjoying a rapidly growing community of developers and system

integrators. As a result, organizations can enjoy a strong support for

machine learning applications built on Apache Spark and Apache R.

Challenges: The infrastructure required to run Spark Mlib and Spark R

applications at an enterprise scale can result is a very complex endeavor.

Additionally, the tools to fully operationalize Spark Mlib and Spark R

applications are still limited compared to other platforms in the space.

PredictionIO Overview: PredictionIO is an open-source Machine Learning server for

developers and data scientists to build and deploy predictive applications in a

fraction of the time. PredictionIO template gallery offers a wide range of

predictive engine templates for download where developers can customize

them easily. PredictionIO is built on top of Apache Spark and it expands it

with enterprise-ready capabilities such as event-based activations, API

generation or monitoring tools.

Key Capabilities:

o Template Based Authoring: PredictionIO provides a model for

authoring simple machine learning applications based on templates.

These templates abstract some of the underlying complexity of a

machine learning model and can be extended and customized for

specific scenarios.

o Event Based Activation: PredictionIO includes an event server

component that enables the asynchronous activation of machine

learning engines. This architecture provides a scalable model to

execute machine learning applications across diverse topologies.

o Monitoring and Management Tools: PredictionIO extends Apache

Aprk with sophisticated management and monitoring tools that

facilitate the operational readiness of machine learning applications.

Challenges: Although incredibly easy to use for simple machine learning

scenarios, PredictionIO can result limited in the implementation of more

complex models. Additionally, PredictionIO still hasn’t been able to build

large developer and system integrator communities and streamline its

implementation in enterprise environments.

Scikit-learn Overview: Scikit-learn is framework provides a range of supervised and

unsupervised learning algorithms via a consistent interface in Python. It is

licensed under a permissive simplified BSD license and is distributed under

many Linux distributions, encouraging academic and commercial use.

Key Capabilities:

o Rich Machine Learning Algorithm Library: Scikit-learn provides

what can be considered the richest collection of machine learning

algorithms of any framework in the space. The framework also

combines features from popular frameworks like Numpy, Scipy or

Sympy to provide sophisticated capabilities in areas such as symbolic

mathematics or scientific computing.

o Simple Programming Model: Despite its large feature set, Scikit-

learn provides a very simple programming model that allow developers

without strong expertise in machine learning to implement highly

sophisticated data science applications.

o Rich Data Visualizations: Scikit-learn provides a strong set of data

visualization capabilities that can be combines with the machine

learning model to rapidly evaluate the effectiveness of the models.

Challenges: Scikit-learn is a programming framework and not a machine

learning platform. In that sense, Scikit-learn does not provide the scalability

models or the monitoring and management tools typically included in

machine learning platforms. As a result, enterprises should look to leverage

the rich capabilities of Scikit-learn in conjunction with other machine learning

platforms to implement enterprise-ready data science solutions.

Summary Machine learning is becoming one of the most relevant aspects of data intelligence

solutions in the enterprise. Enterprises evaluating machine learning platforms

should consider both cloud and on-premise options. Cloud enterprise machine

learning platforms excel on abstracting the underlying infrastructure needed to run

and scale machine learning models. On-premise enterprise machine learning

platforms offer rich extensibility models and typically rely on open source

distribution channels.

Platforms like Azure, AWS and IBM are leading the charge in the cloud enterprise

machine space. Vendors like DAtabricks are also bringing a lot of innovation to the

space. In the on-premise arena, companies like Data and PredictionIO as well as

popular open source frameworks like Apache Spark or Scikit-learn are some of the

robust options for enterprises building data science solutions. This paper included

an analysis of some of the key machine learning platforms including their strengths

and weaknesses based on our experience in real world implementations.