MLOps is collaboration framework between data scientists and the operations or production team to reduce development cycle, improve productivity, eliminate waste, automate as much as possible, and produce richer, more consistent insights with machine learning. MLOps follows a similar pattern to DevOps. It shortens production life cycles by creating better products with each iteration and drives insights you can trust and put into play more quickly.
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The success of machine learning in a wide range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts¹. Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. AutoML makes machine learning available in a true sense, even to people with no major expertise in this field.
With a wave of investment, a raft of new products, and a rising tide of enterprise deployments, artificial intelligence is making a splash in the Internet of Things (IoT). Companies crafting an IoT strategy or seeking to get more value from an existing IoT deployment may want to explore a role for AI. Gartner predicts that by 2022, more than 80 percent of enterprise IoT projects will include an AI component, up from only 10 percent today.
