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Speak With A MaaS Expert


MODELS AS A SERVICE

Model as a Service (MaaS) is a concept that has been gaining traction in the
data science community in recent years. The idea behind MaaS is to provide you,
the data scientists, with an end-to-end solution for deploying and maintaining
your machine learning models in a production environment. MaaS abstracts away
many of the technical details involved in deploying machine learning models.
This can free up your time to focus on what you do best: building and running
models that deliver meaningful insights and predictions.

For data scientists, the key to successfully using MaaS is to be aware of its
strengths and limitations and to use it in conjunction with other tools and
techniques. For example, while MaaS can simplify the deployment process, data
scientists still need to be involved in testing and validating their models.



Additionally, MaaS solutions are not a replacement for traditional data science
tools and workflows. Instead, they are complementary tools that can help to
streamline the deployment process and make it more efficient.

The success of a MaaS solution depends on several factors, including the quality
of the models being deployed, the availability of high-quality data, and the
infrastructure that supports the deployment process. MaaS helps streamline the
deployment process of your machine-learning models. Combining your models and
our execution platform is the recipe for predictive success.



Is MaaS For Me?



THE CHALLENGE

Develop and Train



 * Data science teams are very good at picking up data, train models and apply
   them to test sets
 * Great to showcase the value & business case
 * Not ready for full deployment with continuous operations




Execute and Refine



 * For operations, an execution framework to deploy, operate, collect model
   feedback and retrain the model is required
 * The Framework is also ensuring data and model governance & life cycle
   management, consistent model inference, and auditability





Figure 1. Squirro’s MaaS focuses on the technical details so that you can focus
on delivering valuable insights for your organization.


HOW IT WORKS



Squirro's Model as a Service, platform is a comprehensive solution for data
scientists who want to deploy their models in a production environment. Here's
how it works:



Ingesting Data



Squirro ingests data from various sources, including databases, APIs, and file
systems. Data scientists can select the data sources they want to use for their
models and configure the ingestion process to ensure that the data is properly
structured and formatted.



Appending Security



Squirro takes security very seriously, so all data is securely stored and
processed. Data scientists can set up access controls to ensure that only
authorized users can access the data and models.



Fetching Models



Once the data has been ingested, data scientists can fetch their models and
integrate them with the data. Squirro provides a simple interface for uploading
models, so data scientists can easily get started with the deployment process.



Inference Job



Squirro runs an inference job on the data and models, generating predictions and
insights. The results of the inference job are stored in a centralized
repository, so data scientists can easily access the output and use it to build
end-user applications. Both CPU and GPU-based inference jobs are available.



Building End-User Applications



Data scientists can use the output of the inference job to build a wide range of
end-user applications, including dashboards, reports, and alert systems. Squirro
provides various tools and templates to make it easy for data scientists to
build applications that meet the needs of their stakeholders.



To summarize, Squirro's Model as a Service platform provides data scientists
with a comprehensive solution for deploying their models in a production
environment.



Schedule A Call


Figure 2. Combining your models, or models from libraries, with our platform to
ensure smooth deployment and running of the models.




WHAT'S IN IT FOR YOU?



Squirro is a cutting-edge Model as a Service (MaaS) platform that offers a wide
range of benefits for data scientists. Here are just a few of the reasons why
Squirro is particularly well-suited for model deployment:



Model as a Service



Develop your models in your own environment(s) inside your company or use models
available on platforms such as Hugging Face.

Squirro allows for inference jobs based on CPU or GPU-based infrastructures. You
can also direct Squirro to use your inference infrastructure and write the
output back to the Squirro platform to be used in applications.



Integration



Squirro integrates with a wide range of enterprise data sources, so data
scientists can deploy models that take advantage of a variety of data sources
and data types. The results of your model jobs may be used in your 3rd party
applications, injected into Apps you build yourself on the platform, or used in
some of the ready-to-use apps provided for marketing, sales, service, and risk.



Scalability



Squirro is built to scale, so data scientists can deploy models that can handle
large amounts of data and complex use cases. Whether you're deploying a model
for a small team or a large organization, Squirro has you covered.



Speed



Squirro's fast and efficient deployment process means that data scientists can
get their models up and running quickly, so they can start getting insights and
predictions right away.



Security



Squirro's robust security features ensure that your models and your data are
secure and protected. At ingestion time, full ACL compliance is ensured. Data
scientists can be confident that their models are deployed in a secure
environment.



User-friendly interface



Squirro's user-friendly interface makes it easy for data scientists to deploy
their models and manage their deployments. With a simple drag-and-drop
interface, data scientists can easily upload their models and integrate them
with other data sources and tools.



Squirro is an outstanding Model-as-a-Service platform that offers data
scientists a powerful and easy-to-use solution for deploying their models.



Ready To Speak!


USE CASE: AUTOMATIC CASE CLASSIFICATION

1


THE CHALLENGE

Squirro’s MaaS solution has already proven effective in multiple cases, one of
which is regarding case classification, where several million cases of customer
requests had to be classified. This often resulted in slow responses, unhappy
clients, and frustrated support teams. Ultimately, costly remedies would have to
be used to compensate for the lack of automation:





2


THE SOLUTION

Squirro’s MaaS solution offered a fast and reliable way to automate case
classification with more than 90% accuracy. The resolution handling was also
drastically improved by, amongst others, automatically providing the right
resolution documents to the right person based on previously resolved similar
cases:



3


ADDITIONAL BENEFITS

Squirro’s MaaS solution came with additional benefits beyond just the execution
of the case support classification and regular handling. It was able to provide
insights that would have otherwise been left unutilized. Amongst these was the
automated alerting of line managers and relationship managers if the request
were business critical and required extra attention. Another insight came from
the clustering of repetitive complaints. This exposed operational issues that
would have otherwise been harder to pinpoint and immediately provided
well-documented proof:




NEXT STEPS



If this brief product overview has made you curious to what extent Squirro’s
MaaS solutions can benefit your organization, please fill out this form. One of
Squirro’s MaaS product experts will contact you to discuss the possibilities
tailored to your needs.




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Squirro's Gartner Peer Insights Reviews
4.2
32 Ratings on Gartner Peer Insights
(Submit a review)
As of 23 Feb 2023

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