The growing demand to make products faster and at larger volumes means that manufacturers must look to new solutions to improve efficiency and output. In an era where consumers are more informed than ever before, manufacturers must also ensure that consistent, quality products are leaving their doors.
Manufacturing executive, product, and engineering teams at can achieve this using Machine Learning, but face several hurdles:
Skymind is a platform that helps product and engineering teams ship those ML capabilities into enterprise applications faster.
Empower engineering teams to build AI functionality for Java applications in enterprise environments, using the language and environments that they are familiar with.
Eliminate dependencies and interoperability issues between data science and engineering teams. Get enterprise-grade model reliability, performance, and scaling without maintenance, all on the JVM stack.
Build AI-powered capabilities into existing workflows using streaming or stored data from ERP and CRM systems, data lakes, Spark, Kafka, and elsewhere. Improve model performance over time with quality monitoring, A/B testing, and winner selection.
Engineers can integrate and deploy Python-based models developed by data science research teams into JVM environments and applications, or build their own models in a tracked and collaborative environment.
Renowned companies around the world are actively adopting ML in their manufacturing facilities at a growing rate. The global AI in manufacturing market size is projected to hit USD $15,273.7 million in 2025 with implementation of predictive maintenance alone expected to increase by 38% between 2017 and 2021. These numbers suggest that ML represents the future of manufacturing. Manufacturers who adopt ML technologies gain a clear edge over competitors and boast lower operating costs, improved efficiency, and reduced time to market.
The benefits of ML can be seen in all stages of the manufacturing process. Predictive maintenance monitors machine components and historical factors to prevent downtime and avoid premature calibration routines. Computer vision technologies dramatically improve quality control by checking products for defects at each stage of production. Reinforcement learning and simulation modeling improve efficiency with renderings of factory floors and workflows. From the back office to the shipping dock and beyond, machine learning offers manufacturers a substantial advantage.
ML will continue to see implementation in manufacturing facilities as technologies advance and interest in AI grows across the manufacturing world. These technologies support a new age of manufacturing where productivity and consistency are exponentially increased without the traditional burdens placed on human workers to achieve those improvements. Manufacturers who look to ML solutions will be at the forefront of this new era as innovations like Smart Factories come closer to widespread adoption.