The Keys for a Successful AI Application Deployment
Most artificial intelligence (AI) projects fail to scale. About 80% never reach full deployment, according to Gartner Research.
While AI has very strong development potential, many obstacles can slow its deployment. Going beyond PoC (Proof of Concept) stage requires greater accessibility of data, robust data ingestion pipelines, and highly flexible platforms that adapt to specific business needs and offer monitoring tools to ensure the highest level of performance over time.
AI applications, including visual intelligence, represent the convergence of four technologies: elastic cloud computing, visual data, IoT sensor networks, and AI and machine learning (ML). But at the end of the day, an AI application is similar in many ways to traditional enterprise software. From an operational standpoint, an AI application will go through the same phases throughout its lifecycle: application development, deployment and operations, and ongoing maintenance. As such, it is necessary to build a platform that focuses not only on the algorithms but also on the tools to ingest data quickly, maintain and improve the performance of the algorithms over time, and integrate seamlessly into the existing enterprise software ecosystem. In fact, the algorithm is <5 % of the code in enterprise ML systems. The necessary surrounding infrastructure is vast and complex to cover all the aforementioned aspects.
Flexible architecture to support changing requirements
Once in production, AI and ML models generate inferences (e.g., predictions, forecasts, classifications, segmentation, object detection) based on the incoming data and trigger alerts or downstream actions. An enterprise AI platform should support continuous analytic processing, manage the inference workload required by the business use case, and offer a flexible distributed architecture to support changing requirements (such as data volume, model inference frequencies, or the number of end-users).
Monitoring model performance over time
Unlike typical enterprise software, enterprise AI applications require much more attention and ongoing rigorous monitoring once in production. This upkeep is called incremental learning. Incremental learning is a machine learning paradigm where the learning process occurs whenever a new example or situation emerges and adjusts based on what has been learned according to the latest example. The most prominent difference between incremental learning and traditional machine learning is that it does not assume the availability of a sufficient training set before the learning process, but the training examples appear over time.
Incremental learning is important because the model performance (defined as prediction accuracy, precision, or recall) doesn’t stay constant and will change over time depending on external factors or the apparition of new situations, a phenomenon called model drift. An enterprise AI platform should offer a comprehensive ML Ops framework to monitor model performance, detect model drift, track machine learning features, capture business feedback from end-users, and trigger incremental learning seamlessly to sustain a high level of performance.
An adaptable deployment model
Lastly, each organization has unique deployment requirements defined by its underlying cloud and data strategies, country-specific regulation, and security requirements. For example, an AI application might need to use data stored in a private cloud, leverage microservices from a third party, or publish insights to another public cloud. An AI platform should offer deployment flexibility and support multi-cloud, hybrid-cloud, private-cloud, and edge deployments to address varying requirements across the enterprise.
What is visual intelligence ?
Visual intelligence is a subset of enterprise AI that leverages visual data and artificial intelligence techniques as fundamental layers to drive digital transformation.
Alteia is the Visual Intelligence Platform for Enterprise. Alteia offers a comprehensive software platform that enables enterprise customers to leverage visual data to make better decisions. With Alteia, data scientists, business analysts, and operation teams can work together around a central repository of all their visual data, where they rapidly build predictive models and tailored high-value business applications.
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