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TAIPEI, TAIWAN\u00a0<\/b>–\u00a0Media OutReach<\/a>\u00a0–\u00a022 February 2021 –<\/span>\u00a0<\/b>Appier<\/a>, a leading artificial intelligence (AI) company, today
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Businesses today are
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Appier observes that one
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Based on Appier’s years of
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Additionally, sometimes
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Usually, the actions
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Finally, for a B2B AI company
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In many cases when an unexpected outcome is
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Ensuring the overall
\nannounces its viewpoints of challenges and opportunities when adopting Machine
\nLearning as a Service in the real world. Machine learning (ML) is a
\nvital technology for companies seeking a competitive advantage, as it can
\nprocess large volumes of data fast that can help businesses overcome challenges
\nsuch as more effectively make recommendations to customers, hone manufacturing
\nprocesses or anticipate changes to a market. Machine Learning as a Service (MLaaS)
\nis defined in a business context as companies designing and implementing ML
\nmodels to provide a continuous and consistent service to customers.<\/p>\n
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\ndealing with huge amounts of data and the volume is growing faster than ever.
\nAt the same time, the competitive landscape is changing rapidly and it’s
\ncritical for commercial organizations to make decisions fast. Business success
\ncomes from making quick, accurate decisions using the best possible
\ninformation. This is critical in areas where customer needs and behaviours
\nchange rapidly. For example, in 2020, people had to change how they shop, work
\nand socialize as a direct result of the COVID-19 pandemic, and businesses have
\nhad to shift how they service customers to meet their needs. This means that
\nthe technology they are using to gather and process data also needs to be
\nflexible and adaptable to new data inputs, allowing businesses to move fast and
\nmake the best decisions. <\/p>\n
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\ncurrent challenge of taking ML models to MLaaS has to do with how we currently
\nbuild ML models and how we teach future ML talent to do it. Most research and
\ndevelopment of ML models focuses on building individual models that use a set
\nof training data (with pre-assigned features and labels) to deliver the best
\nperformance in predicting the labels of another set of data (normally we call
\nit testing data). However, if we’re looking at real-world businesses trying to
\nmeet the ever-evolving needs of real-life customers, the boundary between
\ntraining and testing data becomes less clear. The testing or prediction data
\nfor today can be exploited as the training data to create a better model in the
\nfuture. <\/p>\n
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\npractical experience, the data used for training a model will no doubt be
\nimperfect for several reasons. Besides the fact that real-world data sources
\ncan be incomplete or unstructured (such as open answer customer questionnaires),
\nthey can come from a biased collection process. For instance, the data to be
\nused for training a recommendation model are normally collected from the
\nfeedback of another recommender system currently serving online. Thus, the data
\ncollected are biased by the online serving model. <\/p>\n
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\nthe outcome we care about most is the hardest to evaluate. Let’s take digital
\nmarketing for ecommerce as an example. The most straightforward customer
\njourney would be ‘click item, view item, add item to cart, purchase item’.
\nHowever, the process is rarely this simple- people might look at an item
\nseveral times on different devices, and they may remove it from the cart before
\nputting it back in or abandon the purchase altogether.\u00a0 <\/p>\n
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\nin the deeper funnel (i.e. purchase) are much harder to obtain than the ones on
\nthe upper funnel. For example, If the consumer does not complete the purchase
\non your platform, you will never know if he has lost interest in the product,
\nor if there’s another reason he didn’t buy the item. If an MLaSS model relies
\nonly on the simplest metrics (i.e. clicks and view), its suggestion (e.g. when
\nto send out marketing messages) will not align with the ultimate business goal.<\/p>\n
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\nthat provides machine learning services, they normally need to serve thousands
\nor even more customers from different domains. This means there will always be
\nat least multi-thousand models serving online. Furthermore, for those models to
\nconsistently perform to meet ongoing and constantly shifting business goals,
\nthey need to be retrained or updated every day to keep up with evolving
\nreal-world scenarios. To achieve those goals, one needs to design not only an
\nautomated training pipeline but also to guarantee that models will have close
\nto zero probability to converge to a bad local optimal. <\/p>\n
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\ndelivered by Machine Learning model, it’s not the machine learning that has
\nbroken down but some other part of the chain. For example, a recommendation
\nengine may have offered a product to a customer, but the connection between the
\nsales system and the recommendation could be broken, and it takes time to find
\nthe bug. In this case, it would be hard to tell the model if the recommendation
\nwas successful. Troubleshooting issues like this can be quite labor intensive
\nand is a capability that companies adopting MLaaS need to have in place.<\/p>\n
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\nstability and robustness of MLaaS models is critical. It requires significant
\nongoing investment, research and experimentation, but the rewards for
\nbusinesses can be huge, allowing them to quickly adapt and pivot to changing
\nbusiness environments and allowing them to stay ahead of the game. For more
\nartificial intelligence and machine learning information, please refer to the Appier blog<\/a>.<\/p>\n
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