The talk is dedicated to the machines' failures prediction (Predictive Maintenance - PdM). We'll clearly set the goal, present the methodology, and sketch the estimations on the size of the market, including automotive, oil and gas, chemistry, energy, etc. We'll then present new prediction techniques, including deep learning, as well as a broad performance comparison to the state-of-the-art PdM methods together with an idea of dealing with long-period prediction with DL models. We'll show the gain and its origins in detail. We'll introduce two approaches: centralized PdM system and autonomous predictive maintenance devices. The former is the best option for IIoT-typed problems – where all the monitored devices are constantly connected to the internet – and the latter broadens the range of PdM for devices with or without costly network connections, such as cars, trains, or mining equipment. Within the centralized system, we use NVIDIA Tesla GPUs and for the autonomous devices we use NVIDIA Tegra chipsets, which guarantees us both the energy and the computational efficiency. Finally, we'll present case studies of real, production data and the experience gathered while implementing solutions for our clients.