  The rapid development in ubiquitous computing has enabled the use of
microcontrollers as edge devices. These devices are used to develop truly
distributed IoT-based mechanisms where machine learning (ML) models are
utilized. However, integrating ML models to edge devices requires an
understanding of various software tools such as programming languages and
domain-specific knowledge. Anomaly detection is one of the domains where a high
level of expertise is required to achieve promising results. In this work, we
present AnoML which is an end-to-end data science pipeline that allows the
integration of multiple wireless communication protocols, anomaly detection
algorithms, deployment to the edge, fog, and cloud platforms with minimal user
interaction. We facilitate the development of IoT anomaly detection mechanisms
by reducing the barriers that are formed due to the heterogeneity of an IoT
environment. The proposed pipeline supports four main phases: (i) data
ingestion, (ii) model training, (iii) model deployment, (iv) inference and
maintaining. We evaluate the pipeline with two anomaly detection datasets while
comparing the efficiency of several machine learning algorithms within
different nodes. We also provide the source code
(https://gitlab.com/IOTGarage/anoml-iot-analytics) of the developed tools which
are the main components of the pipeline.
