TAIPEI, TAIWAN�-�Media OutReach�-�22 February 2021 -�Appier, a leading artificial intelligence (AI) company, today
announces its viewpoints of challenges and opportunities when adopting Machine
Learning as a Service in the real world. Machine learning (ML) is a
vital technology for companies seeking a competitive advantage, as it can
process large volumes of data fast that can help businesses overcome challenges
such as more effectively make recommendations to customers, hone manufacturing
processes or anticipate changes to a market. Machine Learning as a Service (MLaaS)
is defined in a business context as companies designing and implementing ML
models to provide a continuous and consistent service to customers.

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Businesses today are
dealing with huge amounts of data and the volume is growing faster than ever.
At the same time, the competitive landscape is changing rapidly and it's
critical for commercial organizations to make decisions fast. Business success
comes from making quick, accurate decisions using the best possible
information. This is critical in areas where customer needs and behaviours
change rapidly. For example, in 2020, people had to change how they shop, work
and socialize as a direct result of the COVID-19 pandemic, and businesses have
had to shift how they service customers to meet their needs. This means that
the technology they are using to gather and process data also needs to be
flexible and adaptable to new data inputs, allowing businesses to move fast and
make the best decisions.

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Appier observes that one
current challenge of taking ML models to MLaaS has to do with how we currently
build ML models and how we teach future ML talent to do it. Most research and
development of ML models focuses on building individual models that use a set
of training data (with pre-assigned features and labels) to deliver the best
performance in predicting the labels of another set of data (normally we call
it testing data). However, if we're looking at real-world businesses trying to
meet the ever-evolving needs of real-life customers, the boundary between
training and testing data becomes less clear. The testing or prediction data
for today can be exploited as the training data to create a better model in the
future.

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Based on Appier's years of
practical experience, the data used for training a model will no doubt be
imperfect for several reasons. Besides the fact that real-world data sources
can be incomplete or unstructured (such as open answer customer questionnaires),
they can come from a biased collection process. For instance, the data to be
used for training a recommendation model are normally collected from the
feedback of another recommender system currently serving online. Thus, the data
collected are biased by the online serving model.

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Additionally, sometimes
the outcome we care about most is the hardest to evaluate. Let's take digital
marketing for ecommerce as an example. The most straightforward customer
journey would be 'click item, view item, add item to cart, purchase item'.
However, the process is rarely this simple- people might look at an item
several times on different devices, and they may remove it from the cart before
putting it back in or abandon the purchase altogether.�

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Usually, the actions
in the deeper funnel (i.e. purchase) are much harder to obtain than the ones on
the upper funnel. For example, If the consumer does not complete the purchase
on your platform, you will never know if he has lost interest in the product,
or if there's another reason he didn't buy the item. If an MLaSS model relies
only on the simplest metrics (i.e. clicks and view), its suggestion (e.g. when
to send out marketing messages) will not align with the ultimate business goal.

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Finally, for a B2B AI company
that provides machine learning services, they normally need to serve thousands
or even more customers from different domains. This means there will always be
at least multi-thousand models serving online. Furthermore, for those models to
consistently perform to meet ongoing and constantly shifting business goals,
they need to be retrained or updated every day to keep up with evolving
real-world scenarios. To achieve those goals, one needs to design not only an
automated training pipeline but also to guarantee that models will have close
to zero probability to converge to a bad local optimal.

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In many cases when an unexpected outcome is
delivered by Machine Learning model, it's not the machine learning that has
broken down but some other part of the chain. For example, a recommendation
engine may have offered a product to a customer, but the connection between the
sales system and the recommendation could be broken, and it takes time to find
the bug. In this case, it would be hard to tell the model if the recommendation
was successful. Troubleshooting issues like this can be quite labor intensive
and is a capability that companies adopting MLaaS need to have in place.

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Ensuring the overall
stability and robustness of MLaaS models is critical. It requires significant
ongoing investment, research and experimentation, but the rewards for
businesses can be huge, allowing them to quickly adapt and pivot to changing
business environments and allowing them to stay ahead of the game. For more
artificial intelligence and machine learning information, please refer to the Appier blog.

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About Appier

Appier helps businesses solve their most
challenging problems with artificial intelligence. It is a partner to some of the
world's leading brands, providing a suite of enterprise-grade products to
support data-driven decisions and accelerate business growth. Established in
2012 by a passionate team of computer scientists and engineers, Appier now has
more than 450 employees across 15 offices in APAC and Europe, and is recognized
as a Top 50 AI company by Fortune Magazine. Appier has raised US$162 million in
funding from investors including Sequoia, Softbank, and Line. Learn more at
www.appier.com.