Operationalizing AI and ML has become an unavoidable need in business, as various industries heavily rely on large volumes of real-time data as input to automated decision-making processes to yield the best results. Use cases in the data science field have shown that ML models and AI have few tangible business benefits until they are operationalized. In this e-book, our friends over at MemSQL show us how to successfully deploy model-driven applications into production.
A recent report by Gartner describes AI and Machine Learning as being at the peak of hype in organizations today. Although these technologies are still emerging, they are already delivering practical benefits to help solve real-world problems. As enterprises adopt AI and machine learning, they are gaining significant momentum through their use of data.
There’s new information that suggests enterprises must become data driven in order to effectively compete and win in the next 10 years. This is relevant as most companies have transformed to a fully remote workforce and need their data to be analyzed in real time, so they can make better decisions, optimize the customer experience, and drive efficiency and profitability.
“In the next few years, machine learning and deep learning projects will move out of the data science lab and into production,” said Domenic Ravita, VP of Product Marketing. “With that tremendous change, a modern, cloud-native, ultra-fast, and highly scalable data architecture will be needed to bring this new enterprise to life.”
The e-book lays out a superior approach and architecture to both developing and deploying AI, machine learning, and predictive applications. Features include:
IntroductionAI and Machine Learning Applications TodayChallenges in Delivering Real-Time AI/ML ApplicationsTransform Your Business by Operationalizing AI/ML Applications with MemSQLCustomer Reference ArchitecturesChoosing the Right AI/ML Database
Download MemSQL’s The Future Starts Now e-book containing many useful insights.
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