Deep Learning has revolutionized classic computer vision techniques to enable even more intelligent and autonomous systems. Multimedia frameworks, such as GStreamer, are a basic complement of automatic recognition and classification systems. On this talk you will hear about a suggested design for GstInference, a GStreamer framework that allows easy integration of deep learning networks into your existing pipeline. Leverage GStreamer's flexibility and scalability with your existing models and achieve high performance inference. Use your pre-trained model from the most popular machine learning frameworks (Keras, Tensorflow, Caffe) to infer and execute them in a variety of platforms (x86, iMX8, TX1/TX2). Link in your tracking, recognition and classification networks into your existing pipeline and achieve real-time deep learning inference. Jose Jimenez-Chavarria is a senior embedded software engineer at Ridgerun working on GNU/Linux and GStreamer based solutions since 2013. Jose has a master degree on computer science specialized on machine learning, his graduation work consisted on deep learning techniques applied in nematode segmentation for microscopy images. He's currently interested on computer vision, AI, image processing, multimedia streaming technologies and machine learning applications on embedded systems.