As
difficult as it is for data scientists to label data and develop accurate
Machine Learning Models, managing models in production can be even more
daunting. Data science practices are essential to recognize model bias, retrain
models by updating datasets, improving performance, and maintaining core
technology platforms. Without these disciplines, models can produce erroneous
results that significantly impact the business.
Developing
production–ready models is no easy task. According to a Machine Learning study,
55 percent of companies had not placed models in production, and 40 percent
required more than 30 days to deploy a model. Success brings new challenges,
and 41 percent of respondents acknowledge the challenge of changing Machine
Learning Models and reproducibility.
The lesson here is that new barriers arise with the deployment of Machine
Learning Models to production and use in business processes.
Model
management and operations were once a challenge for more advanced data science
teams. Tasks now include:
• Automating retraining models.
• Alerting when the deviation is
significant.
• Recognizing when models require
upgrades.
• Monitoring production Machine Learning
models for drift.
As more organizations invest in Machine Learning, the need to raise awareness
of model management and operations grows.
The good news is that Alteryx simplifies model management and operations
for data science teams.
You
need to analyze your data and relevant external factors – such as hosting,
competition, psychography, and geographic location. This insightful business
intelligence analytics gives you what you need to capitalize on strategic
market opportunities, beat your competitors and generate more revenue from your
existing business.
This
is where Alteryx comes into play. As a result, your only source for strategic
analytics is software that provides Business Intelligence, Data, and Analytics
you need to make confident and informed decisions.
Alteryx provides a deep insight solution in hours, not weeks, by
displaying the blended and advanced analytical data with an intuitive workflow
chart.
By combining data blends and using predictive analysis methods, Alteryx
strengthens the activities of data analysts in the workflow with reporting,
visualization, and analysis applications.
Often
companies have to use different tools and services to manage a Machine Learning
solution end–to–end, including:
• Software engineering tools for writing
and maintaining code;
• Data management tools to clean,
modify, monitor, and secure data;
• Dashboard creation tools to interact
with the solution and view the results;
• Computing services for dealing with
data and training Machine Learning Models;
Try to hide complexity, providing code–free user interfaces to integrate
core Machine Learning.
As
such, tools like Alteryx can be thought of as a higher level of abstraction
that provides more consolidation at the expense of flexibility than using
lower–level tools directly.
Alteryx
is analytics–focused platform comparable to dashboard solutions like Tableau
but with integrated Machine Learning components. It focuses on providing
code–free alternatives to Machine Learning, advanced analytics, and other
features that often require code.
Alteryx is a more comprehensive solution that provides codeless Machine
Learning and Analytics, Data Management, and Dashboard Components.
The
Alteryx platform, combined with its digital transformation experience and
global market access, helps organizations reach solutions faster. The Alteryx
platform provides end–to–end automation of providing analytics, Machine
Learning, and Data Science processes. This allows organizations and individuals
to improve their digital intelligence and share their innovations across a
business and accelerate business results.
Based
on the AutoML framework, Alteryx Machine Learning offers business analysts and
data scientists a guided approach to building Artificial Intelligence (AI)
Models through its early access program, open–source software that creates
artifacts for Artificial Intelligence (AI) Models known as features from
multiple datasets.
Alteryx
Machine Learning also includes a built–in property store repository to manage
these artifacts, an automated insight generation capability that makes it easy
to uncover hidden factors and relationships within datasets, and an integration
with an Alteryx tool to automate the bundled data preparation tasks. In
addition, it includes over 300 reusable building blocks and integrations with
various external data sources.
The low–code/no–code toolset that Alteryx provides makes it easy for
teams of individuals with varying degrees of data science expertise to
collaborate.
However,
as Artificial Intelligence (AI) capabilities in these apps become more
accessible, their user base must expand beyond business analysts who have
historically been the primary users of these apps. As a result, the number of
users making fact–based decisions faster should steadily decline rather than
rely solely on instincts from personal experience.
This managed Machine Learning Platform sells the concept that non–technical people can create Machine Learning solutions without engineers. But in practice, it is usually experienced Machine Learning Engineers who use these tools and services most successfully.