Eclipse Con Europe 2014 How to use DAWN Science ProjectMatthew Gerring
This document summarizes the DawnScience Eclipse project, which is an open source not-for-profit project on GitHub. It aims to provide APIs and reference implementations for loading, describing, slicing, transforming, and plotting multidimensional scientific data. Phase 1 from 2014-2015 defined long-term APIs and a reference implementation for HDF5 loading, data description, plotting, and slicing interfaces. Phase 2 in 2016 will release concrete implementations. The project utilizes Eclipse technologies and collaborates with scientific facilities.
Introduction to machine learning with GPUsCarol McDonald
The document provides an introduction to machine learning concepts including supervised and unsupervised learning. It discusses classification and regression as examples of supervised learning techniques and clustering as an example of unsupervised learning. It also provides an overview of deep learning using neural networks and examples of convolutional neural networks and recurrent neural networks. The document emphasizes how GPUs have accelerated machine learning by enabling parallel processing.
Tom and Spike classifier using TensorFlow Object Detection. Presentation slides of the meetup TFOD conducted on 17/11/2018 at Algoscale Technologies Inc.
Best Practices for Building and Deploying Data Pipelines in Apache SparkDatabricks
Many data pipelines share common characteristics and are often built in similar but bespoke ways, even within a single organisation. In this talk, we will outline the key considerations which need to be applied when building data pipelines, such as performance, idempotency, reproducibility, and tackling the small file problem. We’ll work towards describing a common Data Engineering toolkit which separates these concerns from business logic code, allowing non-Data-Engineers (e.g. Business Analysts and Data Scientists) to define data pipelines without worrying about the nitty-gritty production considerations.
We’ll then introduce an implementation of such a toolkit in the form of Waimak, our open-source library for Apache Spark (https://github.com/CoxAutomotiveDataSolutions/waimak), which has massively shortened our route from prototype to production. Finally, we’ll define new approaches and best practices about what we believe is the most overlooked aspect of Data Engineering: deploying data pipelines.
Eclipse Con Europe 2014 How to use DAWN Science ProjectMatthew Gerring
This document summarizes the DawnScience Eclipse project, which is an open source not-for-profit project on GitHub. It aims to provide APIs and reference implementations for loading, describing, slicing, transforming, and plotting multidimensional scientific data. Phase 1 from 2014-2015 defined long-term APIs and a reference implementation for HDF5 loading, data description, plotting, and slicing interfaces. Phase 2 in 2016 will release concrete implementations. The project utilizes Eclipse technologies and collaborates with scientific facilities.
Introduction to machine learning with GPUsCarol McDonald
The document provides an introduction to machine learning concepts including supervised and unsupervised learning. It discusses classification and regression as examples of supervised learning techniques and clustering as an example of unsupervised learning. It also provides an overview of deep learning using neural networks and examples of convolutional neural networks and recurrent neural networks. The document emphasizes how GPUs have accelerated machine learning by enabling parallel processing.
Tom and Spike classifier using TensorFlow Object Detection. Presentation slides of the meetup TFOD conducted on 17/11/2018 at Algoscale Technologies Inc.
Best Practices for Building and Deploying Data Pipelines in Apache SparkDatabricks
Many data pipelines share common characteristics and are often built in similar but bespoke ways, even within a single organisation. In this talk, we will outline the key considerations which need to be applied when building data pipelines, such as performance, idempotency, reproducibility, and tackling the small file problem. We’ll work towards describing a common Data Engineering toolkit which separates these concerns from business logic code, allowing non-Data-Engineers (e.g. Business Analysts and Data Scientists) to define data pipelines without worrying about the nitty-gritty production considerations.
We’ll then introduce an implementation of such a toolkit in the form of Waimak, our open-source library for Apache Spark (https://github.com/CoxAutomotiveDataSolutions/waimak), which has massively shortened our route from prototype to production. Finally, we’ll define new approaches and best practices about what we believe is the most overlooked aspect of Data Engineering: deploying data pipelines.
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016MLconf
This document discusses using DL4J and DataVec to build deep learning workflows for modeling time series sensor data with recurrent neural networks. It provides an example of loading and transforming time series data from sensors using DataVec, configuring an RNN using DL4J to classify the trends in the sensor data, and training the network both locally and distributed on Spark. The document promotes DL4J and DataVec as tools that can help enterprises overcome challenges to operationalizing deep learning and producing machine learning models at scale.
Deep Anomaly Detection from Research to Production Leveraging Spark and Tens...Databricks
This document discusses Swedbank's work on anomaly detection from research to production. Some key points:
- Swedbank's Analytics & AI team works on advanced analytics and AI research, delivering applications to address business needs in the short and long term.
- They describe their approach to building deep anomaly detection models using generative adversarial networks and deploying them using TensorFlow Serving.
- The team has developed an "Analytical Ops" framework to streamline the process from building models in a research environment to translating, packaging, and publishing them for production use.
- Lessons learned include the importance of joint data science and engineering efforts, using a feature store for reuse, and infrastructure to support hyperparameter
Time-Evolving Graph Processing On Commodity ClustersJen Aman
Tegra is a system for efficiently processing time-evolving graphs on commodity clusters. It uses a distributed graph snapshot index to represent and retrieve multiple snapshots of evolving graphs. It introduces a timelapse abstraction to perform temporal analytics on windows of snapshots, avoiding redundant computation. Tegra supports both bulk and incremental graph computations using this representation, allowing results to be reused when graphs are updated. An evaluation on real-world graphs shows Tegra can store more snapshots in memory and reduce computation time compared to baseline approaches.
Low Power High-Performance Computing on the BeagleBoard Platforma3labdsp
The ever increasing energy requirements of supercomputers and server farms is driving the scientific and industrial communities to take in deeper consideration the energy efficiency of computing equipments. This contribution addresses the issue proposing a cluster of ARM processors for high-performance computing. The cluster is composed of five BeagleBoard-xM, with one board managing the cluster, and the other boards executing the actual processing. The software platform is based on the Angstrom GNU/Linux distribution and is equipped with a distributed file system to ease sharing data and code among the nodes of the cluster, and with tools for managing tasks and monitoring the status of each node. The computational capabilities of the cluster have been assessed through High-Performance Linpack and a cluster-wide speaker diarization algorithm, while power consumption has been measured using a clamp meter. Experimental results obtained in the speaker diarization task showed that the energy efficiency of the BeagleBoard-xM cluster is comparable to the one of a laptop computer equipped with a Intel Core2 Duo T8300 running at 2.4 GHz. Furthermore, removing the bottleneck due to the Ethernet interface, the BeagleBoard-xM cluster is able to achieve a superior energy efficiency.
Profiling PyTorch for Efficiency & Sustainabilitygeetachauhan
From my talk at the Data & AI summit - latest update on the PyTorch Profiler and how you can use it for optimizations for efficiency. Talk also dives into the future and what we need to do together as an industry to move towards Sustainable AI
Arun Rathinasabapathy, Senior Software Engineer, LexisNexis at MLconf ATL 2016MLconf
Big Data Processing Above and Beyond Hadoop: Data-intensive computing represents a new computing paradigm to address Big Data processing requirements using high-performance architectures supporting scalable parallel processing to allow government, commercial organizations, and research environments to process massive amounts of data and implement new applications previously thought to be impractical or infeasible. The fundamental challenges of data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. The open source HPCC (High-Performance Computing Cluster) Systems platform offers a unified approach to Big Data processing requirements: (1) a scalable, integrated computer systems hardware and software architecture designed for parallel processing of data-intensive computing applications, and (2) a new programming paradigm in the form of a high-level, declarative, data-centric programming language designed specifically for big data processing. This presentation explores the challenges of data-intensive computing from a programming perspective, and describes the ECL programming language and the HPCC architecture designed for data-intensive computing applications. HPCC is an alternative to the Hadoop platform, and ECL is compared to Pig Latin, a high-level language developed for the Hadoop MapReduce architecture.
This document summarizes two GPU programming models - Accelerator and CUDA. It describes the basic steps in Accelerator programming including creating data arrays, loading them into data-parallel array objects, processing the arrays using Accelerator operations, creating a result object, and evaluating the result on a target processor. It also provides an example code showing the use of ParallelArrays and FloatParallelArray objects. The document then briefly introduces CUDA as a parallel computing platform and programming model for GPUs that provides lower and higher-level APIs.
This deck was presented at the Spark meetup at Bangalore. The key idea behind the presentation was to focus on limitations of Hadoop MapReduce and introduce both Hadoop YARN and Spark in this context. An overview of the other aspects of the Berkeley Data Analytics Stack was also provided.
The document discusses two Spark algorithms: outlier detection on categorical data and KNN join. It describes how the algorithms work, including mapping attributes to scores for outlier detection and using z-order curves to map points to a single dimension for KNN joins. It also provides performance results and best practices for implementing the algorithms in Spark and discusses applications in graph algorithms.
This contains the agenda of the Spark Meetup I organised in Bangalore on Friday, the 23rd of Jan 2014. It carries the slides for the talk I gave on distributed deep learning over Spark
Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
are advancing and people uses these technologies in day to day activities, this data is termed as Big Data
having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose
frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by
traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets
but it has large communication cost which reduces execution efficiency. This proposed new pre-processed
k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans
algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets
from generated clusters using MapReduce programming model. Results shown that execution efficiency of
ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as
one of the pre-processing technique.
Surge: Rise of Scalable Machine Learning at Yahoo!DataWorks Summit
Andy Feng discusses Yahoo's use of scalable machine learning for search and advertisement applications with massive datasets and features. Three machine learning algorithms - gradient boosted decision trees, logistic regression, and ad-query vectors - presented challenges of scale that were addressed using Hadoop and YARN across hundreds of servers. Approximate computing techniques like streaming, distributed training, and in-memory processing enabled speedups of 30x to 1000x and scaling to billions of examples and terabytes of data, allowing daily model training. Hadoop and distributed processing on CPU and GPU resources were critical to solving Yahoo's needs for scalable machine learning on big data.
Spark and the Future of Advanced Analytics by Thomas DinsmoreSpark Summit
This document discusses the need for distributed platforms for machine learning and analytics. It argues that distributed systems are necessary because data sources and targets are distributed, data movement is expensive, and data and model requirements are growing. It presents Spark as currently the best option for a distributed framework, and notes that vendors are working to integrate their tools with Spark to enable distributed workflows. In summary, distributed machine learning is needed due to expanding data and computing demands, and Spark has emerged as the leading framework for distributed analytics and machine learning.
Learn how graph technologies can be applied to real-world use cases, using medical, network security, and financial data. By combining graph models and machine learning techniques, we can discover relationships, classify information, and identify patterns and anomalies in data. We can answer questions such as “How did other investigators approach similar cases?” and “Do these symptoms seem similar to ones we’ve seen in other diseases?” Presented by Sungpack Hong, Research Director, Oracle Labs.
(1) Learning visual representations for unfamiliar environments is challenging due to domain shift between training and test data distributions. (2) The paper proposes learning asymmetric transformations to map target domain data to the source domain in order to address this domain shift problem. (3) The key aspects of the approach include learning nonlinear kernel-based transformations between domains in a regularized manner and evaluating its ability to generalize to novel target classes not seen during training.
This document provides an overview of machine learning and the scikit-learn library. It discusses predictive modeling using historical data to build executable models for making predictions on new data. It describes how scikit-learn provides machine learning algorithms and tools through a simple API using Python, NumPy and SciPy. It highlights improvements in scikit-learn 0.15, including reduced training times for ensemble methods and optimized memory usage. It demos income classification using scikit-learn with Census data in an IPython notebook.
Flux - Open Machine Learning Stack / PipelineJan Wiegelmann
This document describes Flux, an open machine learning stack for training and evaluating machine learning models at scale. It provides:
- Native format support for ROS data through input formats and serialization.
- An end-to-end machine learning workflow including data ingestion, preprocessing, model training, re-simulation, and deployment.
- A scale-out architecture using Apache Spark and Hadoop for distributed processing optimized for cost, time and storage.
This document provides an agenda for an introduction to running AI workloads on PowerAI. It includes:
- An overview of IBM PowerAI and demos of AI workloads using TensorFlow and PyTorch hands-on labs.
- A demonstration of running the MNIST workload using TensorFlow to classify handwritten digits, including downloading the workload, training a basic model, and predicting classes of new images.
- An introduction to PyTorch, describing it as a flexible deep learning framework that supports dynamic computation graphs, native Python packages, and automatic differentiation.
Hussein Mehanna, Engineering Director, ML Core - Facebook at MLconf ATL 2016MLconf
Applying Deep Learning at Facebook Scale: Facebook leverages Deep Learning for various applications including event prediction, machine translation, natural language understanding and computer vision at a very large scale. There are more than a billion users logging on to Facebook every daily generating thousands of posts per second and uploading more than a billion images and videos every day. This talk will explain how Facebook scaled Deep Learning inference for realtime applications with latency budgets in the milliseconds.
Multiplatform Spark solution for Graph datasources by Javier DominguezBig Data Spain
This document summarizes a presentation given by Javier Dominguez at Big Data Spain about Stratio's multiplatform solution for graph data sources. It discusses graph use cases, different data stores like Spark, GraphX, GraphFrames and Neo4j. It demonstrates the machine learning life cycle using a massive dataset from Freebase, running queries and algorithms. It shows notebooks and a business example of clustering bank data using Jaccard distance and connected components. The presentation concludes with future directions like a semantic search engine and applying more machine learning algorithms.
Madeo - a CAD Tool for reconfigurable HardwareESUG
This document discusses Madeo, a CAD tool for programming reconfigurable hardware using an object-oriented methodology. Madeo was developed over 10 years and allows describing circuits as objects in a high-level language. It supports various reconfigurable architectures by modeling them and can generate configuration bitstreams. The tool aims to improve on existing solutions by providing retargetability, exploiting flexibility of reconfigurable hardware, and applying principles like code reuse and portability through a virtual machine-like approach. The document outlines key aspects of Madeo like its architecture modeling, compilation flow, and results demonstrating its capabilities on different targets. It also discusses lessons learned like using meta-modeling for evolution and interchange support.
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016MLconf
This document discusses using DL4J and DataVec to build deep learning workflows for modeling time series sensor data with recurrent neural networks. It provides an example of loading and transforming time series data from sensors using DataVec, configuring an RNN using DL4J to classify the trends in the sensor data, and training the network both locally and distributed on Spark. The document promotes DL4J and DataVec as tools that can help enterprises overcome challenges to operationalizing deep learning and producing machine learning models at scale.
Deep Anomaly Detection from Research to Production Leveraging Spark and Tens...Databricks
This document discusses Swedbank's work on anomaly detection from research to production. Some key points:
- Swedbank's Analytics & AI team works on advanced analytics and AI research, delivering applications to address business needs in the short and long term.
- They describe their approach to building deep anomaly detection models using generative adversarial networks and deploying them using TensorFlow Serving.
- The team has developed an "Analytical Ops" framework to streamline the process from building models in a research environment to translating, packaging, and publishing them for production use.
- Lessons learned include the importance of joint data science and engineering efforts, using a feature store for reuse, and infrastructure to support hyperparameter
Time-Evolving Graph Processing On Commodity ClustersJen Aman
Tegra is a system for efficiently processing time-evolving graphs on commodity clusters. It uses a distributed graph snapshot index to represent and retrieve multiple snapshots of evolving graphs. It introduces a timelapse abstraction to perform temporal analytics on windows of snapshots, avoiding redundant computation. Tegra supports both bulk and incremental graph computations using this representation, allowing results to be reused when graphs are updated. An evaluation on real-world graphs shows Tegra can store more snapshots in memory and reduce computation time compared to baseline approaches.
Low Power High-Performance Computing on the BeagleBoard Platforma3labdsp
The ever increasing energy requirements of supercomputers and server farms is driving the scientific and industrial communities to take in deeper consideration the energy efficiency of computing equipments. This contribution addresses the issue proposing a cluster of ARM processors for high-performance computing. The cluster is composed of five BeagleBoard-xM, with one board managing the cluster, and the other boards executing the actual processing. The software platform is based on the Angstrom GNU/Linux distribution and is equipped with a distributed file system to ease sharing data and code among the nodes of the cluster, and with tools for managing tasks and monitoring the status of each node. The computational capabilities of the cluster have been assessed through High-Performance Linpack and a cluster-wide speaker diarization algorithm, while power consumption has been measured using a clamp meter. Experimental results obtained in the speaker diarization task showed that the energy efficiency of the BeagleBoard-xM cluster is comparable to the one of a laptop computer equipped with a Intel Core2 Duo T8300 running at 2.4 GHz. Furthermore, removing the bottleneck due to the Ethernet interface, the BeagleBoard-xM cluster is able to achieve a superior energy efficiency.
Profiling PyTorch for Efficiency & Sustainabilitygeetachauhan
From my talk at the Data & AI summit - latest update on the PyTorch Profiler and how you can use it for optimizations for efficiency. Talk also dives into the future and what we need to do together as an industry to move towards Sustainable AI
Arun Rathinasabapathy, Senior Software Engineer, LexisNexis at MLconf ATL 2016MLconf
Big Data Processing Above and Beyond Hadoop: Data-intensive computing represents a new computing paradigm to address Big Data processing requirements using high-performance architectures supporting scalable parallel processing to allow government, commercial organizations, and research environments to process massive amounts of data and implement new applications previously thought to be impractical or infeasible. The fundamental challenges of data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. The open source HPCC (High-Performance Computing Cluster) Systems platform offers a unified approach to Big Data processing requirements: (1) a scalable, integrated computer systems hardware and software architecture designed for parallel processing of data-intensive computing applications, and (2) a new programming paradigm in the form of a high-level, declarative, data-centric programming language designed specifically for big data processing. This presentation explores the challenges of data-intensive computing from a programming perspective, and describes the ECL programming language and the HPCC architecture designed for data-intensive computing applications. HPCC is an alternative to the Hadoop platform, and ECL is compared to Pig Latin, a high-level language developed for the Hadoop MapReduce architecture.
This document summarizes two GPU programming models - Accelerator and CUDA. It describes the basic steps in Accelerator programming including creating data arrays, loading them into data-parallel array objects, processing the arrays using Accelerator operations, creating a result object, and evaluating the result on a target processor. It also provides an example code showing the use of ParallelArrays and FloatParallelArray objects. The document then briefly introduces CUDA as a parallel computing platform and programming model for GPUs that provides lower and higher-level APIs.
This deck was presented at the Spark meetup at Bangalore. The key idea behind the presentation was to focus on limitations of Hadoop MapReduce and introduce both Hadoop YARN and Spark in this context. An overview of the other aspects of the Berkeley Data Analytics Stack was also provided.
The document discusses two Spark algorithms: outlier detection on categorical data and KNN join. It describes how the algorithms work, including mapping attributes to scores for outlier detection and using z-order curves to map points to a single dimension for KNN joins. It also provides performance results and best practices for implementing the algorithms in Spark and discusses applications in graph algorithms.
This contains the agenda of the Spark Meetup I organised in Bangalore on Friday, the 23rd of Jan 2014. It carries the slides for the talk I gave on distributed deep learning over Spark
Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
are advancing and people uses these technologies in day to day activities, this data is termed as Big Data
having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose
frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by
traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets
but it has large communication cost which reduces execution efficiency. This proposed new pre-processed
k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans
algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets
from generated clusters using MapReduce programming model. Results shown that execution efficiency of
ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as
one of the pre-processing technique.
Surge: Rise of Scalable Machine Learning at Yahoo!DataWorks Summit
Andy Feng discusses Yahoo's use of scalable machine learning for search and advertisement applications with massive datasets and features. Three machine learning algorithms - gradient boosted decision trees, logistic regression, and ad-query vectors - presented challenges of scale that were addressed using Hadoop and YARN across hundreds of servers. Approximate computing techniques like streaming, distributed training, and in-memory processing enabled speedups of 30x to 1000x and scaling to billions of examples and terabytes of data, allowing daily model training. Hadoop and distributed processing on CPU and GPU resources were critical to solving Yahoo's needs for scalable machine learning on big data.
Spark and the Future of Advanced Analytics by Thomas DinsmoreSpark Summit
This document discusses the need for distributed platforms for machine learning and analytics. It argues that distributed systems are necessary because data sources and targets are distributed, data movement is expensive, and data and model requirements are growing. It presents Spark as currently the best option for a distributed framework, and notes that vendors are working to integrate their tools with Spark to enable distributed workflows. In summary, distributed machine learning is needed due to expanding data and computing demands, and Spark has emerged as the leading framework for distributed analytics and machine learning.
Learn how graph technologies can be applied to real-world use cases, using medical, network security, and financial data. By combining graph models and machine learning techniques, we can discover relationships, classify information, and identify patterns and anomalies in data. We can answer questions such as “How did other investigators approach similar cases?” and “Do these symptoms seem similar to ones we’ve seen in other diseases?” Presented by Sungpack Hong, Research Director, Oracle Labs.
(1) Learning visual representations for unfamiliar environments is challenging due to domain shift between training and test data distributions. (2) The paper proposes learning asymmetric transformations to map target domain data to the source domain in order to address this domain shift problem. (3) The key aspects of the approach include learning nonlinear kernel-based transformations between domains in a regularized manner and evaluating its ability to generalize to novel target classes not seen during training.
This document provides an overview of machine learning and the scikit-learn library. It discusses predictive modeling using historical data to build executable models for making predictions on new data. It describes how scikit-learn provides machine learning algorithms and tools through a simple API using Python, NumPy and SciPy. It highlights improvements in scikit-learn 0.15, including reduced training times for ensemble methods and optimized memory usage. It demos income classification using scikit-learn with Census data in an IPython notebook.
Flux - Open Machine Learning Stack / PipelineJan Wiegelmann
This document describes Flux, an open machine learning stack for training and evaluating machine learning models at scale. It provides:
- Native format support for ROS data through input formats and serialization.
- An end-to-end machine learning workflow including data ingestion, preprocessing, model training, re-simulation, and deployment.
- A scale-out architecture using Apache Spark and Hadoop for distributed processing optimized for cost, time and storage.
This document provides an agenda for an introduction to running AI workloads on PowerAI. It includes:
- An overview of IBM PowerAI and demos of AI workloads using TensorFlow and PyTorch hands-on labs.
- A demonstration of running the MNIST workload using TensorFlow to classify handwritten digits, including downloading the workload, training a basic model, and predicting classes of new images.
- An introduction to PyTorch, describing it as a flexible deep learning framework that supports dynamic computation graphs, native Python packages, and automatic differentiation.
Hussein Mehanna, Engineering Director, ML Core - Facebook at MLconf ATL 2016MLconf
Applying Deep Learning at Facebook Scale: Facebook leverages Deep Learning for various applications including event prediction, machine translation, natural language understanding and computer vision at a very large scale. There are more than a billion users logging on to Facebook every daily generating thousands of posts per second and uploading more than a billion images and videos every day. This talk will explain how Facebook scaled Deep Learning inference for realtime applications with latency budgets in the milliseconds.
Multiplatform Spark solution for Graph datasources by Javier DominguezBig Data Spain
This document summarizes a presentation given by Javier Dominguez at Big Data Spain about Stratio's multiplatform solution for graph data sources. It discusses graph use cases, different data stores like Spark, GraphX, GraphFrames and Neo4j. It demonstrates the machine learning life cycle using a massive dataset from Freebase, running queries and algorithms. It shows notebooks and a business example of clustering bank data using Jaccard distance and connected components. The presentation concludes with future directions like a semantic search engine and applying more machine learning algorithms.
Madeo - a CAD Tool for reconfigurable HardwareESUG
This document discusses Madeo, a CAD tool for programming reconfigurable hardware using an object-oriented methodology. Madeo was developed over 10 years and allows describing circuits as objects in a high-level language. It supports various reconfigurable architectures by modeling them and can generate configuration bitstreams. The tool aims to improve on existing solutions by providing retargetability, exploiting flexibility of reconfigurable hardware, and applying principles like code reuse and portability through a virtual machine-like approach. The document outlines key aspects of Madeo like its architecture modeling, compilation flow, and results demonstrating its capabilities on different targets. It also discusses lessons learned like using meta-modeling for evolution and interchange support.
The document discusses using Teradata's Unified Data Architecture and SQL-MapReduce functions to analyze customer churn for a telecommunications company. It provides examples of creating views that join customer data from Teradata, Hadoop, and Aster sources. Graphing and visualization tools are used to identify patterns in customer reboot events and equipment issues that may lead to cancellations. The document demonstrates how to gain insights into customer behavior across multiple data platforms.
Linux and Open Source in Math, Science and EngineeringPDE1D
Covers a brief history of Open Source Math, Science and Engineering Software on Linux. A look at the software tools currently available for mathematical analysis and plotting for math science and engineering. Presented at 2011 Ohio LinuxFest.
This document summarizes IBM's announcement of a major commitment to advance Apache Spark. It discusses IBM's investments in Spark capabilities, including log processing, graph analytics, stream processing, machine learning, and unified data access. Key reasons for interest in Spark include its performance (up to 100x faster than Hadoop for some tasks), productivity gains, ability to leverage existing Hadoop investments, and continuous community improvements. The document also provides an overview of Spark's architecture, programming model using resilient distributed datasets (RDDs), and common use cases like interactive querying, batch processing, analytics, and stream processing.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/11/deploying-deep-learning-applications-on-fpgas-with-matlab-a-presentation-from-mathworks/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Jack Erickson, Principal Product Marketing Manager at MathWorks, presents the “Deploying Deep Learning Applications on FPGAs with MATLAB” tutorial at the September 2020 Embedded Vision Summit.
Designing deep learning networks for embedded devices is challenging because of processing and memory resource constraints. FPGAs present an even greater challenge due to the complexity of programming in Verilog or VHDL, and the hardware expertise needed for prototyping on an FPGA. This talk illustrates a workflow to facilitate the design and deployment of these applications to FPGAs using pre-built bitstreams without the need for much hardware expertise.
Starting with a pre-trained model trained either in MATLAB or any framework of your choice, Erickson demonstrates the workflow to prototype and deploy the trained network from MATLAB to an FPGA. He illustrates this flow using a deep learning network for image recognition, deploying it to the Xilinx MPSoC board for inference using APIs from MATLAB. This demonstrates how deep learning algorithm engineers can quickly explore different networks and their performance on an FPGA from MATLAB.
The document contains summaries of several projects completed by Marek Šuplata including a moving object tracker, simulator of coordinating productions, face biometric recognition system, medical CT volume data visualization, power network blackouts monitor, and motion control projects in Matlab/Simulink including a positional servosystem and direct vector control loops for an asynchronous motor. Details provided for each project include description, source code size, tasks, technologies used, and duration.
Berlin buzzwords 2018 TensorFlow on HopsJim Dowling
This document provides an overview of TensorFlow-on-Hops, a platform for running TensorFlow and machine learning workloads on Hadoop clusters. It discusses features like security, GPU resource management, distributed training, hyperparameter optimization, and model serving. The document also provides examples of using Hops to run common ML tasks like image classification and discusses the benefits of the platform for data scientists.
The document discusses distributed deep learning using Hopsworks. It describes how Hopsworks can be used for distributed training, hyperparameter optimization, and model serving. Hopsworks provides a feature store, distributed file system, and workflows for building scalable machine learning pipelines. It supports frameworks like TensorFlow, PyTorch, and Spark for distributed deep learning tasks like data parallel training using collective all-reduce strategies.
Automated ML Workflow for Distributed Big Data Using Analytics Zoo (CVPR2020 ...Jason Dai
This document summarizes a CVPR 2020 tutorial on the Analytics Zoo platform for automated machine learning workflows for distributed big data using Apache Spark. The tutorial covers an overview of Analytics Zoo and the BigDL distributed deep learning framework. It demonstrates distributed training of deep learning models using TensorFlow and PyTorch on Spark, and features of Analytics Zoo like end-to-end pipelines, ML workflow for automation, and model deployment with cluster serving. Real-world use cases applying Analytics Zoo at companies like SK Telecom, Midea, and MasterCard are also presented.
Jump Start into Apache® Spark™ and DatabricksDatabricks
These are the slides from the Jump Start into Apache Spark and Databricks webinar on February 10th, 2016.
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Spark is a fast, easy to use, and unified engine that allows you to solve many Data Sciences and Big Data (and many not-so-Big Data) scenarios easily. Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing. We will leverage Databricks to quickly and easily demonstrate, visualize, and debug our code samples; the notebooks will be available for you to download.
Running Emerging AI Applications on Big Data Platforms with Ray On Apache SparkDatabricks
With the rapid evolution of AI in recent years, we need to embrace advanced and emerging AI technologies to gain insights and make decisions based on massive amounts of data. Ray (https://github.com/ray-project/ray) is a fast and simple framework open-sourced by UC Berkeley RISELab particularly designed for easily building advanced AI applications in a distributed fashion.
Build Large-Scale Data Analytics and AI Pipeline Using RayDPDatabricks
A large-scale end-to-end data analytics and AI pipeline usually involves data processing frameworks such as Apache Spark for massive data preprocessing, and ML/DL frameworks for distributed training on the preprocessed data. A conventional approach is to use two separate clusters and glue multiple jobs. Other solutions include running deep learning frameworks in an Apache Spark cluster, or use workflow orchestrators like Kubeflow to stitch distributed programs. All these options have their own limitations. We introduce Ray as a single substrate for distributed data processing and machine learning. We also introduce RayDP which allows you to start an Apache Spark job on Ray in your python program and utilize Ray’s in-memory object store to efficiently exchange data between Apache Spark and other libraries. We will demonstrate how this makes building an end-to-end data analytics and AI pipeline simpler and more efficient.
ScalaTo July 2019 - No more struggles with Apache Spark workloads in productionChetan Khatri
Scala Toronto July 2019 event at 500px.
Pure Functional API Integration
Apache Spark Internals tuning
Performance tuning
Query execution plan optimisation
Cats Effects for switching execution model runtime.
Discovery / experience with Monix, Scala Future.
Deep learning and streaming in Apache Spark 2.2 by Matei ZahariaGoDataDriven
Matei Zaharia is an assistant professor of computer science at Stanford University, Chief Technologist and Co-founder of Databricks. He started the Spark project at UC Berkeley and continues to serve as its vice president at Apache. Matei also co-started the Apache Mesos project and is a committer on Apache Hadoop. Matei’s research work on datacenter systems was recognized through two Best Paper awards and the 2014 ACM Doctoral Dissertation Award.
1) Express Logic produces the real-time operating system ThreadX which is known for its source code quality and lack of bugs.
2) The presentation will examine ThreadX source code using the static code analysis tools Coverity and Structure101 to analyze code quality and detect any potential bugs or defects.
3) A live demo will show the results of analyzing ThreadX code and identifying any issues, as well as demonstrating the simple ThreadX application programming interface.
This document discusses how Amazon SageMaker can be used to train machine learning models on large datasets using hosted Jupyter notebooks. It notes that DigitalGlobe plans to use SageMaker to train models on petabytes of Earth observation imagery so that users can create and deploy models within one scalable environment. The document also quotes the CTO of Maxar Technologies saying they will use SageMaker to build and deploy novel AI algorithms at scale to solve complex problems.
TWDI Accelerate Seattle, Oct 16, 2017: Distributed and In-Database Analytics ...Debraj GuhaThakurta
Event: TDWI Accelerate Seattle, October 16, 2017
Topic: Distributed and In-Database Analytics with R
Presenter: Debraj GuhaThakurta
Description: How to develop scalable and in-DB analytics using R in Spark and SQL-Server
TDWI Accelerate, Seattle, Oct 16, 2017: Distributed and In-Database Analytics...Debraj GuhaThakurta
R is a popular statistical programming language used for data analysis and machine learning. It has over 3 million users and is taught widely in universities. While powerful, R has some scaling limitations for big data. Several Apache Spark integrations with R like SparkR and sparklyr enable distributed, parallel processing of large datasets using R on Spark clusters. Other options for scaling R include H2O for in-memory analytics, Microsoft ML Server for on-premises scaling, and ScaleR for portable parallel processing across platforms. These solutions allow R programs and models to be trained on large datasets and deployed for operational use on big data in various cloud and on-premises environments.
This document discusses how to optimize HDF5 files for efficient access in cloud object stores. Key optimizations include using large dataset chunk sizes of 1-4 MiB, consolidating internal file metadata, and minimizing variable-length datatypes. The document recommends creating files with paged aggregation and storing file content information in the user block to enable fast discovery of file contents when stored in object stores.
This document provides an overview of HSDS (Highly Scalable Data Service), which is a REST-based service that allows accessing HDF5 data stored in the cloud. It discusses how HSDS maps HDF5 objects like datasets and groups to individual cloud storage objects to optimize performance. The document also describes how HSDS was used to improve access performance for NASA ICESat-2 HDF5 data on AWS S3 by hyper-chunking datasets into larger chunks spanning multiple original HDF5 chunks. Benchmark results showed that accessing the data through HSDS provided over 2x faster performance than other methods like ROS3 or S3FS that directly access the cloud storage.
This document summarizes the current status and focus of the HDF Group. It discusses that the HDF Group is located in Champaign, IL and is a non-profit organization focused on developing and maintaining HDF software and data formats. It provides an overview of recent HDF5, HDF4 and HDFView releases and notes areas of focus for software quality improvements, increased transparency, strengthening the community, and modernizing HDF products. It invites support and participation in upcoming user group meetings.
This document provides an overview of HSDS (HDF Server and Data Service), which allows HDF5 files to be stored and accessed from the cloud. Key points include:
- HSDS maps HDF5 objects like datasets and groups to individual cloud storage objects for scalability and parallelism.
- Features include streaming support, fancy indexing for complex queries, and caching for improved performance.
- HSDS can be deployed on Docker, Kubernetes, or AWS Lambda depending on needs.
- Case studies show HSDS is used by organizations like NREL and NSF to make petabytes of scientific data publicly accessible in the cloud.
This document discusses creating cloud-optimized HDF5 files by rearranging internal structures for more efficient data access in cloud object stores. It describes cloud-native and cloud-optimized storage formats, with the latter involving storing the entire HDF5 file as a single object. The benefits of cloud-optimized HDF5 include fast scanning and using the HDF5 library. Key aspects covered include using optimal chunk sizes, compression, and minimizing variable-length datatypes.
This document discusses updates and performance improvements to the HDF5 OPeNDAP data handler. It provides a history of the handler since 2001 and describes recent updates including supporting DAP4, new data types, and NetCDF data models. A performance study showed that passing compressed HDF5 data through the handler without decompressing/recompressing led to speedups of around 17-30x by leveraging HDF5 direct I/O APIs. This allows outputting HDF5 files as NetCDF files much faster through the handler.
This document provides instructions for using the Hyrax software to serve scientific data files stored on Amazon S3 using the OPeNDAP data access protocol. It describes how to generate ancillary metadata files called DMR++ files using the get_dmrpp tool that provide information about the data file structure and locations. The document explains how to run get_dmrpp inside a Docker container to process data files on S3 and generate customized DMR++ files that the Hyrax server can use to serve the files to clients.
This document provides an overview and examples of accessing cloud data and services using the Earthdata Login (EDL), Pydap, and MATLAB. It discusses some common problems users encounter, such as being unable to access HDF5 data on AWS S3 using MATLAB or read data from OPeNDAP servers using Pydap. Solutions presented include using EDL to get temporary AWS tokens for S3 access in MATLAB and providing code examples on the HDFEOS website to help users access S3 data and OPeNDAP services. The document also notes some limitations, such as tokens being valid for only 1 hour, and workarounds like requesting new tokens or using the MATLAB HDF5 API instead of the netCDF API.
The HDF5 Roadmap and New Features document outlines upcoming changes and improvements to the HDF5 library. Key points include:
- HDF5 1.13.x releases will include new features like selection I/O, the Onion VFD for versioned files, improved VFD SWMR for single-writer multiple-reader access, and subfiling for parallel I/O.
- The Virtual Object Layer allows customizing HDF5 object storage and introduces terminal and pass-through connectors.
- The Onion VFD stores versions of HDF5 files in a separate onion file for versioned access.
- VFD SWMR improves on legacy SWMR by implementing single-writer multiple-reader capabilities
This document discusses user analysis of the HDFEOS.org website and plans for future improvements. It finds that the majority of the site's 100 daily users are "quiet", not posting on forums or other interactive elements. The main user types are locators, who search for examples or data; mergers, who combine or mosaic datasets; and converters, who change file formats. The document outlines recent updates focused on these user types, like adding Python examples for subsetting and calculating latitude and longitude. It proposes future work on artificial intelligence/machine learning uses of HDF files and examples for processing HDF data in the cloud.
This document summarizes a presentation about the current status and future directions of the Hierarchical Data Format (HDF) software. It provides updates on recent HDF5 releases, development efforts including new compression methods and ways to access HDF5 data, and outreach resources. It concludes by inviting the audience to share wishes for future HDF development.
The document describes H5Coro, a new C++ library for reading HDF5 files from cloud storage. H5Coro was created to optimize HDF5 reading for cloud environments by minimizing I/O operations through caching and efficient HTTP requests. Performance tests showed H5Coro was 77-132x faster than the previous HDF5 library at reading HDF5 data from Amazon S3 for NASA's SlideRule project. H5Coro supports common HDF5 elements but does not support writing or some complex HDF5 data types and messages to focus on optimized read-only performance for time series data stored sequentially in memory.
This document summarizes MathWorks' work to modernize MATLAB's support for HDF5. Key points include:
1) MATLAB now supports HDF5 1.10.7 features like single-writer/multiple-reader access and virtual datasets through new and updated low-level functions.
2) Performance benchmarks show some improvements but also regressions compared to the previous HDF5 version, and work continues to optimize code and support future versions.
3) There are compatibility considerations for Linux filter plugins, but interim solutions are provided until MathWorks can ship a single HDF5 version.
HSDS provides HDF as a service through a REST API that can scale across nodes. New releases will enable serverless operation using AWS Lambda or direct client access without a server. This allows HDF data to be accessed remotely without managing servers. HSDS stores each HDF object separately, making it compatible with cloud object storage. Performance on AWS Lambda is slower than a dedicated server but has no management overhead. Direct client access has better performance but limits collaboration between clients.
HDF5 and Zarr are data formats that can be used to store and access scientific data. This presentation discusses approaches to translating between the two formats. It describes how HDF5 files were translated to the Zarr format by creating a separate Zarr store to hold HDF5 file chunks, and storing chunk location metadata. It also discusses an implementation that translates Zarr data to the HDF5 format by using a special chunking layout and storing chunk information in an HDF5 compound dataset. Limitations of the translations include lack of support for some HDF5 dataset properties in Zarr, and lack of support for some Zarr compression methods in the HDF5 implementation.
The document discusses HDF for the cloud, including new features of the HDF Server and what's next. Key points:
- HDF Server uses a "sharded schema" that maps HDF5 objects to individual storage objects, allowing parallel access and updates without transferring entire files.
- Implementations include HSDS software that uses the sharded schema with an API and SDKs for different languages like h5pyd for Python.
- New features of HSDS 0.6 include support for POSIX, Azure, AWS Lambda, and role-based access control.
- Future work includes direct access to storage without a server intermediary for some use cases.
This document compares different methods for accessing HDF and netCDF files stored on Amazon S3, including Apache Drill, THREDDS Data Server (TDS), and HDF5 Virtual File Driver (VFD). A benchmark test of accessing a 24GB HDF5/netCDF-4 file on S3 from Amazon EC2 found that TDS performed the best, responding within 2 minutes, while Apache Drill failed after 7 minutes. The document concludes that TDS 5.0 is the clear winner based on performance and support for role-based access control and HDF4 files, but the best solution depends on use case and software.
This document discusses STARE-PODS, a proposal to NASA/ACCESS-19 to develop a scalable data store for earth science data using the SpatioTemporal Adaptive Resolution Encoding (STARE) indexing scheme. STARE allows diverse earth science data to be unified and indexed, enabling the data to be partitioned and stored in a Parallel Optimized Data Store (PODS) for efficient analysis. The HDF Virtual Object Layer and Virtual Data Set technologies can then provide interfaces to access the data in STARE-PODS in a familiar way. The goal is for STARE-PODS to organize diverse data for alignment and parallel/distributed storage and processing to enable integrative analysis at scale.
This document provides an overview and update on HDF5 and its ecosystem. Key points include:
- HDF5 1.12.0 was recently released with new features like the Virtual Object Layer and external references.
- The HDF5 library now supports accessing data in the cloud using connectors like S3 VFD and REST VOL without needing to modify applications.
- Projects like HDFql and H5CPP provide additional interfaces for querying and working with HDF5 files from languages like SQL, C++, and Python.
- The HDF5 community is moving development to GitHub and improving documentation resources on the HDF wiki site.
This document summarizes new features in HDF5 1.12.0, including support for storing references to objects and attributes across files, new storage backends using a virtual object layer (VOL), and virtual file drivers (VFDs) for Amazon S3 and HDFS. It outlines the HDF5 roadmap for 2019-2022, which includes continued support for HDF5 1.8 and 1.10, and new features in future 1.12.x releases like querying, indexing, and provenance tracking.
More from The HDF-EOS Tools and Information Center (20)
High performance Serverless Java on AWS- GoTo Amsterdam 2024Vadym Kazulkin
Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
This presentation shows how The MathWorks products provide an integrated approach for the design of complex systems. Beginning with the concept, the tools provide the ability to develop the system, verify that it satisfies the specifications, allows the designer to optimize the design, and finally, automatically creates the embedded code.
Using examples from aircraft and spacecraft design, the unique features of The MathWorks products that allow this process are highlighted. In particular, the open nature of the products and their integration are exploited.
The talk consists of an introductory demonstration that uses the Lunar Module autopilot design to illustrate MATLAB, Simulink and Stateflow integrated together to provide a complete design.
This is followed by a brief description of The MAthWorks; who we are and how we got started.
The presentation then walks through each of the major products and these are illustrated using examples that have been selected to illustrate the main strengths of each product.
The last part of the discusses “Simulation based Requirements”. The current state of code generation for embedded systems is described, and the way in which this can evolve using integrated tools is discussed.
* WE’RE APPLICABLE TO A WIDE RANGE OF PROBLEMS - if you believe -as we do - that the future of technical computing involvesA MULTIDISCIPLINARY APPROACH, YOU’LL SEE THAT IN DICK’S DEMOS
tHIS IS WHY ASTRONAUTICS IS TALKING TO US ABOUT INCREASING THEIR USAGE FROM 150 TO 1,500 COPIES. THEIR MANAGER OF IS FOR THEIR ENGINEERING AND PRODUCTION DEPARTMENTS SAYS THAT HE SEES US AS DESKTOP PRODUCT FOR ALL THEIR ENGINEERS.
* BECAUSE OF THIS BREADTH OF APP & OPEN SYS PHILOSOPHY…
THE EPI COMMITTEE HAS ASKED US TO LOOK INTO INTERFACING TO OTHER EPI CHOSEN TOOLS LIKE RTM & RDD. OUR INITIAL PASS TELLS US THAT ALL THE HOOKS ARE THERE, SO IT’S VERY LIKELY WE’LL BE DOING THAT
OFFER CATALOG
* WE HAVE EXCELLENT, INDUSTRY LEADING SUPPORT AND SERVICES THAT I’LL GO OVER BRIEFLY IN A MOMENT
* I’M YOUR NAT ACCT MNGR
- CPP & EPI
- TRAINING FILMS
This is a matrix of the products that The MathWorks currently provides.
The MathWorks tools allow the entire process described in this presentation to be accomplished in one seamlessly integrated environment. The goal of an Executable Specification is close.