Why is it that 80% of enterprises fail to scale AI? Data scientists face operational, collaborative and infrastructure complexities at each step of the ML lifecycle. MLOps practices have the ability to solve many ML operational concerns such as project deployment, testing, serving and monitoring. In this webinar, Yochay Ettun, CEO and Co-founder of cnvrg.io will discuss the ways that MLOps solutions empower data scientists to successfully operationalize ML by applying DevOps principles to the ML lifecycle.
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The global spread of the novel coronavirus (COVID-19) and the economic impact that followed has prompted many businesses to furlough the workforce or migrate from the traditional office to remote-working environments. The volatile landscape has created incremental risks, especially for organizations heavily relying on IT Sec/Ops teams to monitor security and privacy and enforce regulatory compliance.
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As rare diseases are so rare, patients are often misdiagnosed for many years, or never correctly diagnosed. Electronic health records hold much important information that can be used to correctly diagnose and treat these patients, but identification of phenotypic sets is hard to extract from reams of data.
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Did you know that most migrations fail in some way, either missing project deadlines, overshooting budgets, or running into complications in the new environment?
A lack of visibility into data can seriously jeopardize—and sometimes even ruin—a migration project.
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