  The explosion of data volumes generated by an increasing number of
applications is strongly impacting the evolution of distributed digital
infrastructures for data analytics and machine learning (ML). While data
analytics used to be mainly performed on cloud infrastructures, the rapid
development of IoT infrastructures and the requirements for low-latency, secure
processing has motivated the development of edge analytics. Today, to balance
various trade-offs, ML-based analytics tends to increasingly leverage an
interconnected ecosystem that allows complex applications to be executed on
hybrid infrastructures where IoT Edge devices are interconnected to Cloud/HPC
systems in what is called the Computing Continuum, the Digital Continuum, or
the Transcontinuum.Enabling learning-based analytics on such complex
infrastructures is challenging. The large scale and optimized deployment of
learning-based workflows across the Edge-to-Cloud Continuum requires extensive
and reproducible experimental analysis of the application execution on
representative testbeds. This is necessary to help understand the performance
trade-offs that result from combining a variety of learning paradigms and
supportive frameworks. A thorough experimental analysis requires the assessment
of the impact of multiple factors, such as: model accuracy, training time,
network overhead, energy consumption, processing latency, among others.This
review aims at providing a comprehensive vision of the main state-of-the-art
libraries and frameworks for machine learning and data analytics available
today. It describes the main learning paradigms enabling learning-based
analytics on the Edge-to-Cloud Continuum. The main simulation, emulation,
deployment systems, and testbeds for experimental research on the Edge-to-Cloud
Continuum available today are also surveyed. Furthermore, we analyze how the
selected systems provide support for experiment reproducibility. We conclude
our review with a detailed discussion of relevant open research challenges and
of future directions in this domain such as: holistic understanding of
performance; performance optimization of applications;efficient deployment of
Artificial Intelligence (AI) workflows on highly heterogeneous infrastructures;
and reproducible analysis of experiments on the Computing Continuum.
