Paasmer Edge Software Suite provides three software components that provides an increased level of data filtering on the Edge.
Edge Analytics; Edge Machine Learning & Edge Artificial Intelligence
EDGE DATA INTELLIGENCE BUILT WITH DOCKERS
Filter IoT Data
Enables data filtering at Edge
Paasmer Edge Analytics is a streaming programming model and runtime designed to accelerate the development of analytics on edge devices. Enabling streaming analytics on edge devices can reduce the load on the data center and communication costs. Additionally, edge analytics can reduce decision making latency and allow device autonomy.
Edge Analytics uses an API layer to compose processing pipelines with immediate per-tuple computation. It also provides a suite of connectors which greatly ease communication with backend cloud operations.
API driven and modular design ensures that EA is compatible with other analytics solutions like Apache Kafka, Apache Spark, and Apache Storm.
Reduced Communication Costs
EA performs real-time analytics on the edge device, separating the interesting from the mundane. Hence you don’t have to send every sensor reading over a network. If 98% of readings are standard, EA detects the 2% anomalies and transmits those for further processing.
Local and Faster Time to Action
EA makes devices more intelligent, enabling them to take immediate action. For example, a connected vehicle running EA can adjust traction control based on the weight of the cargo or passengers.
Learning From Related Devices
EA enables connected devices to learn from other related devices. For example, a car maneuvering roads in Seattle can adjust based on the data received from trucks operating under similar loads and conditions in Oregon.
Train Devices at Edge based on Data
In a few years, the world will be filled with billions of small, connected, intelligent devices. Many of these devices will be embedded in our homes, our cities, our vehicles, and our factories. Some of these devices will be carried in our pockets or worn on our bodies. The proliferation of small computing devices will disrupt every industrial sector and play a key role in the next evolution of personal computing.
Most of these devices will be small and mobile. Many of them will have limited memories (as small as 32 KB) and weak processors (as low as 20 MIPS). Almost all of them will use a variety of sensors to monitor their surroundings and interact with their users. Most importantly, many of these devices will rely on machine-learned models to interpret the signals from their sensors, to make accurate inferences and predictions about their environment, and, ultimately, to make intelligent decisions. Offloading this intelligence to the cloud is often impractical, due to latency, bandwidth, privacy, reliability, and connectivity issues. Therefore, we need to execute a significant portion of the intelligent pipeline on the edge devices themselves.
Modern state-of-the-art machine learning techniques are not a good fit for execution on small, resource-impoverished devices. Today’s machine learning algorithms are designed to run on powerful servers, which are often accelerated with special GPU and FPGA hardware. Therefore, our primary goal is to develop machine learning algorithms that are tailored for embedded platforms. Rather than just optimizing predictive accuracy, our techniques attempt to balance accuracy with runtime resource consumption.
Enables Devices to learn from patterns
Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain.Artificial Intelligence is a machine learning framework that is used to design, build, and train deep learning models.
You can use the Artificial Intelligence library do to numerical computations, which in itself doesn’t seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or nodes, that are communicated between these edges.
You see? The name “Artificial Intelligence” is derived from the operations which neural networks perform on multidimensional data arrays or nodes! It’s literally a flow of nodes. Running Artificial Intelligence workloads on the Edge reduces the need to take all the data to the cloud. However, it also needs high compute power on the Edge devices.
PAASMER EDGE ANALYTICS
Paasmer Edge Analytics communicates with your backend systems through the following message hubs:
Paasmer Edge Analytics can be used to filter the data on the Edge Device using any of the following methods:
PAASMER MACHINE LEARNING
Paasmer Machine learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features.We can separate learning problems in a few large categories:
Paasmer focuses on Supervised learning, in which the data comes with additional attributes that we want to predict. This problem can be either:
Classification: Samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data.
Regression: If the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight.
PAASMER ARITIFICIAL INTELLIGENCE
AI module built on Edge with analysis of video, image and other kinds of data formats can offer numerous benefits.
Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain.
Artificial Intelligence is a machine learning framework that is used to design, build, and train deep learning models. You can use the Artificial Intelligence library do to numerical computations, which in itself doesn’t seem all too special, but these computations are done with data flow graphs.
In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or nodes, that are communicated between these edges.
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