Don’t miss out on our weekly tech talks in Discord on Fridays at 1PM EST! Each session covers a different topic, with something for all audience members. By attending, you’ll be entered for a chance to win Modzy swag! Check out the upcoming sessions for March 2022:

March 4: Computer Vision Deep Dive

Computer vision is a subfield of artificial intelligence that trains computers to understand, interpret, and “see” the visual world. This talk will cover the different types of models that fall under this umbrella, common use cases, and some of the current considerations and challenges associated with this technology.

March 11: gRPC

Google Remote Procedure Call, or gRPC, is an open source framework designed for the HTTP/2 protocol used to accelerate inter-service communication in microservice architectures. Learn some tips on gRPC best practices and how Modzy uses the framework within its microservices.

March 18: Running AI Models at the Edge

When time is of the essence, AI can power real-time insights at the edge where data is stored. However, challenges persist in the field with reducing large model architectures to run on smaller devices, ensure security, bridge skills gaps and enable orchestration back to a centralized solution for monitoring and retraining as needed. We’ll walk through a new method to deploying, running, and securing AI models at the edge that allows for faster processing, reduced latency, and increased security.

March 25: Analyzing Unstructured Data with AI/ML

Unstructured data has no pre-defined format, making it difficult to process and analyze, leaving potentially valuable insights hidden from view. Fortunately, AI and ML can be used to process and add structure to unstructured data, enabling better mining for audio, video, and text data. This tech talk demonstrates how Modzy can transform and analyze any kind of unstructured data.

March 31: Beyond Lime and SHAP: AXAI, the Fastest Approach to AI Explainability

Learn how a novel approach to explainability based on adversarial machine learning can be used to explain the predictions of deep neural networks and produce better results faster than LIME and SHAP. This talk covers our approach, which identifies the relative importance of input features in relation to the predictions based on the behavior of an adversarial attack on the DNN and uses this information to produce the explanations.

Join our Discord server today so you don’t miss out!