Course Information
• Nisa Trainings
• IBM Netezza Online Training
• Duration: 30 Hours
• Timings: Weekdays (1-2 Hours per day) [OR] Weekends (2-3 Hours per day)
• Training Method: Instructor Led Online One-on-One Live Interactive Sessions.
Netezza Online Training by www.etraining.guru in IndiaRavikumar Nandigam
This document provides an overview of the IBM PureData System for Analytics (formerly known as IBM Netezza). It discusses that Netezza is a pre-configured data warehousing appliance that simplifies data warehousing and provides massively parallel processing. It also includes course outlines on using and administering Netezza.
The document compares Netezza and Teradata data warehousing platforms. It discusses their key design principles, technology requirements, physical architectures, parameters for comparison, and evaluation considerations. Netezza uses a two-tier architecture with SMP and SPUs compared to Teradata's node-based architecture. Netezza is also designed for scalability and high performance through its AMPP architecture and intelligent query streaming. The document evaluates both platforms on factors like scalability, manageability, cost and proven track record in supporting large enterprises.
The document provides information about the IBM PureData System for Analytics (Netezza). It discusses the components and architecture of the IBM PureData System models, including the N1001 and N2001 models. It explains the key hardware components like snippet blades, hosts, and storage arrays and how they work together using Netezza's Asymmetric Massively Parallel Processing architecture to optimize analytics workloads.
User can run queries via MicroStrategy’s visual interface without the need to write unfamiliar HiveQL or MapReduce scripts. In essence, any user, without programming skill in Hadoop, can ask questions against vast volumes of structured and unstructured data to gain valuable business insights.
This document discusses Revolution R Enterprise for IBM Netezza, which allows users to run R analytics code directly in an IBM Netezza database for improved performance. Key features include running R code against large datasets in Netezza in a massively parallel manner using various R packages like nzA, nzR, and nzMatrix. This enables capabilities like predictive modeling, data manipulation, and matrix operations directly in the database. The document provides an example use case of building credit risk models on Netezza data and demonstrates the end-to-end workflow in Revolution R Enterprise.
Jon cohn exton pa corporate data architectureJon Cohn
The document discusses several principles and best practices for corporate data architecture going forward, including storing data first and analyzing later as storage is cheap; defaulting to real-time data processing instead of batch processing where possible; using document-oriented data models to store complex and polymorphic data as efficiently as structured data; adopting agile development methodologies requiring adaptable databases; using multiple database technologies as needed rather than a one-size-fits-all approach; deploying on commodity hardware; using solid state drives for random I/O; and following other principles around architecture, trends, and best practices.
Microsoft Fabric is the next version of Azure Data Factory, Azure Data Explorer, Azure Synapse Analytics, and Power BI. It brings all of these capabilities together into a single unified analytics platform that goes from the data lake to the business user in a SaaS-like environment. Therefore, the vision of Fabric is to be a one-stop shop for all the analytical needs for every enterprise and one platform for everyone from a citizen developer to a data engineer. Fabric will cover the complete spectrum of services including data movement, data lake, data engineering, data integration and data science, observational analytics, and business intelligence. With Fabric, there is no need to stitch together different services from multiple vendors. Instead, the customer enjoys end-to-end, highly integrated, single offering that is easy to understand, onboard, create and operate.
This is a hugely important new product from Microsoft and I will simplify your understanding of it via a presentation and demo.
Agenda:
What is Microsoft Fabric?
Workspaces and capacities
OneLake
Lakehouse
Data Warehouse
ADF
Power BI / DirectLake
Resources
NVIDIA DEEP LEARNING INFERENCE PLATFORM PERFORMANCE STUDY
| TECHNICAL OVERVIEW
| 1
Introduction
Artificial intelligence (AI), the dream of computer scientists for over half
a century, is no longer science fiction—it is already transforming every
industry. AI is the use of computers to simulate human intelligence. AI
amplifies our cognitive abilities—letting us solve problems where the
complexity is too great, the information is incomplete, or the details are
too subtle and require expert training.
While the machine learning field has been active for decades, deep
learning (DL) has boomed over the last five years. In 2012, Alex
Krizhevsky of the University of Toronto won the ImageNet image
recognition competition using a deep neural network trained on NVIDIA
GPUs—beating all the human expert algorithms that had been honed
for decades. That same year, recognizing that larger networks can learn
more, Stanford’s Andrew Ng and NVIDIA Research teamed up to develop
a method for training networks using large-scale GPU computing
systems. These seminal papers sparked the “big bang” of modern AI,
setting off a string of “superhuman” achievements. In 2015, Google and
Microsoft both beat the best human score in the ImageNet challenge. In
2016, DeepMind’s AlphaGo recorded its historic win over Go champion
Lee Sedol and Microsoft achieved human parity in speech recognition.
GPUs have proven to be incredibly effective at solving some of the most
complex problems in deep learning, and while the NVIDIA deep learning
platform is the standard industry solution for training, its inferencing
capability is not as widely understood. Some of the world’s leading
enterprises from the data center to the edge have built their inferencing
solution on NVIDIA GPUs. Some examples include:
Netezza Online Training by www.etraining.guru in IndiaRavikumar Nandigam
This document provides an overview of the IBM PureData System for Analytics (formerly known as IBM Netezza). It discusses that Netezza is a pre-configured data warehousing appliance that simplifies data warehousing and provides massively parallel processing. It also includes course outlines on using and administering Netezza.
The document compares Netezza and Teradata data warehousing platforms. It discusses their key design principles, technology requirements, physical architectures, parameters for comparison, and evaluation considerations. Netezza uses a two-tier architecture with SMP and SPUs compared to Teradata's node-based architecture. Netezza is also designed for scalability and high performance through its AMPP architecture and intelligent query streaming. The document evaluates both platforms on factors like scalability, manageability, cost and proven track record in supporting large enterprises.
The document provides information about the IBM PureData System for Analytics (Netezza). It discusses the components and architecture of the IBM PureData System models, including the N1001 and N2001 models. It explains the key hardware components like snippet blades, hosts, and storage arrays and how they work together using Netezza's Asymmetric Massively Parallel Processing architecture to optimize analytics workloads.
User can run queries via MicroStrategy’s visual interface without the need to write unfamiliar HiveQL or MapReduce scripts. In essence, any user, without programming skill in Hadoop, can ask questions against vast volumes of structured and unstructured data to gain valuable business insights.
This document discusses Revolution R Enterprise for IBM Netezza, which allows users to run R analytics code directly in an IBM Netezza database for improved performance. Key features include running R code against large datasets in Netezza in a massively parallel manner using various R packages like nzA, nzR, and nzMatrix. This enables capabilities like predictive modeling, data manipulation, and matrix operations directly in the database. The document provides an example use case of building credit risk models on Netezza data and demonstrates the end-to-end workflow in Revolution R Enterprise.
Jon cohn exton pa corporate data architectureJon Cohn
The document discusses several principles and best practices for corporate data architecture going forward, including storing data first and analyzing later as storage is cheap; defaulting to real-time data processing instead of batch processing where possible; using document-oriented data models to store complex and polymorphic data as efficiently as structured data; adopting agile development methodologies requiring adaptable databases; using multiple database technologies as needed rather than a one-size-fits-all approach; deploying on commodity hardware; using solid state drives for random I/O; and following other principles around architecture, trends, and best practices.
Microsoft Fabric is the next version of Azure Data Factory, Azure Data Explorer, Azure Synapse Analytics, and Power BI. It brings all of these capabilities together into a single unified analytics platform that goes from the data lake to the business user in a SaaS-like environment. Therefore, the vision of Fabric is to be a one-stop shop for all the analytical needs for every enterprise and one platform for everyone from a citizen developer to a data engineer. Fabric will cover the complete spectrum of services including data movement, data lake, data engineering, data integration and data science, observational analytics, and business intelligence. With Fabric, there is no need to stitch together different services from multiple vendors. Instead, the customer enjoys end-to-end, highly integrated, single offering that is easy to understand, onboard, create and operate.
This is a hugely important new product from Microsoft and I will simplify your understanding of it via a presentation and demo.
Agenda:
What is Microsoft Fabric?
Workspaces and capacities
OneLake
Lakehouse
Data Warehouse
ADF
Power BI / DirectLake
Resources
NVIDIA DEEP LEARNING INFERENCE PLATFORM PERFORMANCE STUDY
| TECHNICAL OVERVIEW
| 1
Introduction
Artificial intelligence (AI), the dream of computer scientists for over half
a century, is no longer science fiction—it is already transforming every
industry. AI is the use of computers to simulate human intelligence. AI
amplifies our cognitive abilities—letting us solve problems where the
complexity is too great, the information is incomplete, or the details are
too subtle and require expert training.
While the machine learning field has been active for decades, deep
learning (DL) has boomed over the last five years. In 2012, Alex
Krizhevsky of the University of Toronto won the ImageNet image
recognition competition using a deep neural network trained on NVIDIA
GPUs—beating all the human expert algorithms that had been honed
for decades. That same year, recognizing that larger networks can learn
more, Stanford’s Andrew Ng and NVIDIA Research teamed up to develop
a method for training networks using large-scale GPU computing
systems. These seminal papers sparked the “big bang” of modern AI,
setting off a string of “superhuman” achievements. In 2015, Google and
Microsoft both beat the best human score in the ImageNet challenge. In
2016, DeepMind’s AlphaGo recorded its historic win over Go champion
Lee Sedol and Microsoft achieved human parity in speech recognition.
GPUs have proven to be incredibly effective at solving some of the most
complex problems in deep learning, and while the NVIDIA deep learning
platform is the standard industry solution for training, its inferencing
capability is not as widely understood. Some of the world’s leading
enterprises from the data center to the edge have built their inferencing
solution on NVIDIA GPUs. Some examples include:
Optimized Systems: Matching technologies for business success.Karl Roche
Tom Rosamilia, General Manager, Power and z Systems, IBM Corporation outlines the way business can optimize it's systems to enhance performance, reduce cost per workload and drive innovation. Presented at the Smarter Computing Executive Summit, 25th May 2011.
This document discusses optimizing Apache Spark machine learning workloads on OpenPOWER platforms. It provides an overview of Spark, machine learning, and deep learning. It then discusses how OpenPOWER systems are well-suited for these workloads due to features like high memory bandwidth, large caches, and GPU support. The document outlines various techniques for tuning Spark performance on OpenPOWER, such as configuration of executors, cores, memory, and storage levels. It also presents examples analyzing the performance of a matrix factorization machine learning application under different Spark configurations.
Vectorization is a new database technology that provides significant performance improvements through parallel processing. It fully utilizes multiple types of parallelism including symmetric multiprocessing (SMP), massively parallel processing (MPP) clusters, graphics processing units (GPUs), and vector processing instructions in Intel CPUs. Early adopters using fully vectorized databases are seeing dramatically lower costs and ability to handle new types of workloads and applications compared to traditional database technologies.
The document describes the IBM PureData System for Analytics N3001 appliance. It is a high-performance, scalable appliance that enables analytics on large volumes of data. It provides faster query performance, supports thousands of users, and includes business intelligence and Hadoop starter kits. The appliance requires minimal administration and maintenance, providing low total cost of ownership.
This document provides an overview of big data analysis tools and methods presented by Ehsan Derakhshan of innfinision. It discusses what data and big data are, important questions about database selection, and several tools and solutions offered by innfinision including MongoDB, PyTables, Blosc, and Blaze. MongoDB is highlighted as a scalable and high performance document database. The advantages of these tools include optimized memory usage, rich queries, fast updates, and the ability to analyze and optimize queries.
SAP Sybase IQ uses a technique called distributed query processing (DQP) that can improve query performance by breaking queries into pieces and distributing the pieces across multiple SAP Sybase IQ servers. DQP provides both intra-query and inter-query parallelism. It dynamically manages resources to balance workloads and avoid saturating the system. For DQP to be effective, the storage area network must have sufficient performance to support the increased parallelism.
1. The document describes building an analytical platform for a retailer by using open source tools R and RStudio along with SAP Sybase IQ database.
2. Key aspects included setting up SAP Sybase IQ as a column-store database for storage and querying of data, implementing R and RStudio for statistical analysis, and automating running of statistical models on new data.
3. The solution provided a low-cost platform capable of rapid prototyping of analytical models and production use for predictive analytics.
1. The customer asked the author to build an analytical platform to store data in a database and perform statistical analysis from a front-end interface.
2. The author chose an SAP Sybase IQ column-store database to store data, the open-source R programming language to perform statistical analysis, and RStudio as the front-end interface.
3. The solution provided a simple way to load and query large amounts of data, automated running of statistical models, and could be deployed in the cloud.
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.
The document discusses using MapReduce for a sequential web access-based recommendation system. It explains how web server logs could be mapped to create a pattern tree showing frequent sequences of accessed web pages. When making recommendations for a user, their access pattern would be compared to patterns in the tree to find matching branches to suggest. MapReduce is well-suited for this because it can efficiently process and modify the large, dynamic tree structure across many machines in a fault-tolerant way.
FIWARE Global Summit - Knowage Hands On: Visualizing Data InsightsFIWARE
Presentation by Marco Cortella
Senior Solution Developer, Engineering Ingegneria Informatica S.p.A.
FIWARE Global Summit
23-24 October 2019 - Berlin, Germany
This document provides a summary of a presentation on innovating with AI at scale. The presentation discusses:
1. Implementing AI use cases at scale across industries like retail, life sciences, and transportation.
2. Deploying AI models to the edge using tools like TensorFlow and TensorRT for high-performance inference on devices.
3. Best practices and frameworks for distributed deep learning training on large clusters to train models faster.
The document discusses scalable storage systems and key-value stores as an alternative to traditional databases. It provides an overview of vertical and horizontal scalability. Traditional databases are not well-suited for scalable systems due to their complexity, wasted features, and multi-step query processing. Key-value stores offer simpler data models and interfaces that are designed from the start for scaling across hundreds of machines. Performance comparisons show key-value stores significantly outperforming traditional databases. The document also outlines how key-value storage systems work at the aggregation and storage layers.
Building a scalable analytics environment to support diverse workloadsAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Building a scalable analytics environment to support diverse workloads
Tom Panozzo, Chief Technology Officer (Aunalytics)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Graph Data: a New Data Management FrontierDemai Ni
Graph Data: a New Data Management Frontier -- Huawei’s view and Call for Collaboration by Demai Ni:
Huawei provides Enterprise Databases, and are actively exploring the latest technology to provide end-to-end Data Management Solution on Cloud. We are looking at to bridge classic RDMS to Graph Database on a distributed platform.
AtomicDB is a proprietary software technology that uses an n-dimensional associative memory system instead of a traditional table-based database. This allows information to be stored and related in a way analogous to human memory. The technology does not require extensive programming and can rapidly build and modify information systems to meet evolving needs. It provides significant cost and performance advantages over traditional databases for managing complex, relational data.
Choosing the Right Database: Exploring MySQL Alternatives for Modern Applicat...Mydbops
Choosing the Right Database: Exploring MySQL Alternatives for Modern Applications by Bhanu Jamwal, Head of Solution Engineering, PingCAP at the Mydbops Opensource Database Meetup 14.
This presentation discusses the challenges in choosing the right database for modern applications, focusing on MySQL alternatives. It highlights the growth of new applications, the need to improve infrastructure, and the rise of cloud-native architecture.
The presentation explores alternatives to MySQL, such as MySQL forks, database clustering, and distributed SQL. It introduces TiDB as a distributed SQL database for modern applications, highlighting its features and top use cases.
Case studies of companies benefiting from TiDB are included. The presentation also outlines TiDB's product roadmap, detailing upcoming features and enhancements.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Optimized Systems: Matching technologies for business success.Karl Roche
Tom Rosamilia, General Manager, Power and z Systems, IBM Corporation outlines the way business can optimize it's systems to enhance performance, reduce cost per workload and drive innovation. Presented at the Smarter Computing Executive Summit, 25th May 2011.
This document discusses optimizing Apache Spark machine learning workloads on OpenPOWER platforms. It provides an overview of Spark, machine learning, and deep learning. It then discusses how OpenPOWER systems are well-suited for these workloads due to features like high memory bandwidth, large caches, and GPU support. The document outlines various techniques for tuning Spark performance on OpenPOWER, such as configuration of executors, cores, memory, and storage levels. It also presents examples analyzing the performance of a matrix factorization machine learning application under different Spark configurations.
Vectorization is a new database technology that provides significant performance improvements through parallel processing. It fully utilizes multiple types of parallelism including symmetric multiprocessing (SMP), massively parallel processing (MPP) clusters, graphics processing units (GPUs), and vector processing instructions in Intel CPUs. Early adopters using fully vectorized databases are seeing dramatically lower costs and ability to handle new types of workloads and applications compared to traditional database technologies.
The document describes the IBM PureData System for Analytics N3001 appliance. It is a high-performance, scalable appliance that enables analytics on large volumes of data. It provides faster query performance, supports thousands of users, and includes business intelligence and Hadoop starter kits. The appliance requires minimal administration and maintenance, providing low total cost of ownership.
This document provides an overview of big data analysis tools and methods presented by Ehsan Derakhshan of innfinision. It discusses what data and big data are, important questions about database selection, and several tools and solutions offered by innfinision including MongoDB, PyTables, Blosc, and Blaze. MongoDB is highlighted as a scalable and high performance document database. The advantages of these tools include optimized memory usage, rich queries, fast updates, and the ability to analyze and optimize queries.
SAP Sybase IQ uses a technique called distributed query processing (DQP) that can improve query performance by breaking queries into pieces and distributing the pieces across multiple SAP Sybase IQ servers. DQP provides both intra-query and inter-query parallelism. It dynamically manages resources to balance workloads and avoid saturating the system. For DQP to be effective, the storage area network must have sufficient performance to support the increased parallelism.
1. The document describes building an analytical platform for a retailer by using open source tools R and RStudio along with SAP Sybase IQ database.
2. Key aspects included setting up SAP Sybase IQ as a column-store database for storage and querying of data, implementing R and RStudio for statistical analysis, and automating running of statistical models on new data.
3. The solution provided a low-cost platform capable of rapid prototyping of analytical models and production use for predictive analytics.
1. The customer asked the author to build an analytical platform to store data in a database and perform statistical analysis from a front-end interface.
2. The author chose an SAP Sybase IQ column-store database to store data, the open-source R programming language to perform statistical analysis, and RStudio as the front-end interface.
3. The solution provided a simple way to load and query large amounts of data, automated running of statistical models, and could be deployed in the cloud.
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.
The document discusses using MapReduce for a sequential web access-based recommendation system. It explains how web server logs could be mapped to create a pattern tree showing frequent sequences of accessed web pages. When making recommendations for a user, their access pattern would be compared to patterns in the tree to find matching branches to suggest. MapReduce is well-suited for this because it can efficiently process and modify the large, dynamic tree structure across many machines in a fault-tolerant way.
FIWARE Global Summit - Knowage Hands On: Visualizing Data InsightsFIWARE
Presentation by Marco Cortella
Senior Solution Developer, Engineering Ingegneria Informatica S.p.A.
FIWARE Global Summit
23-24 October 2019 - Berlin, Germany
This document provides a summary of a presentation on innovating with AI at scale. The presentation discusses:
1. Implementing AI use cases at scale across industries like retail, life sciences, and transportation.
2. Deploying AI models to the edge using tools like TensorFlow and TensorRT for high-performance inference on devices.
3. Best practices and frameworks for distributed deep learning training on large clusters to train models faster.
The document discusses scalable storage systems and key-value stores as an alternative to traditional databases. It provides an overview of vertical and horizontal scalability. Traditional databases are not well-suited for scalable systems due to their complexity, wasted features, and multi-step query processing. Key-value stores offer simpler data models and interfaces that are designed from the start for scaling across hundreds of machines. Performance comparisons show key-value stores significantly outperforming traditional databases. The document also outlines how key-value storage systems work at the aggregation and storage layers.
Building a scalable analytics environment to support diverse workloadsAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Building a scalable analytics environment to support diverse workloads
Tom Panozzo, Chief Technology Officer (Aunalytics)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Graph Data: a New Data Management FrontierDemai Ni
Graph Data: a New Data Management Frontier -- Huawei’s view and Call for Collaboration by Demai Ni:
Huawei provides Enterprise Databases, and are actively exploring the latest technology to provide end-to-end Data Management Solution on Cloud. We are looking at to bridge classic RDMS to Graph Database on a distributed platform.
AtomicDB is a proprietary software technology that uses an n-dimensional associative memory system instead of a traditional table-based database. This allows information to be stored and related in a way analogous to human memory. The technology does not require extensive programming and can rapidly build and modify information systems to meet evolving needs. It provides significant cost and performance advantages over traditional databases for managing complex, relational data.
Choosing the Right Database: Exploring MySQL Alternatives for Modern Applicat...Mydbops
Choosing the Right Database: Exploring MySQL Alternatives for Modern Applications by Bhanu Jamwal, Head of Solution Engineering, PingCAP at the Mydbops Opensource Database Meetup 14.
This presentation discusses the challenges in choosing the right database for modern applications, focusing on MySQL alternatives. It highlights the growth of new applications, the need to improve infrastructure, and the rise of cloud-native architecture.
The presentation explores alternatives to MySQL, such as MySQL forks, database clustering, and distributed SQL. It introduces TiDB as a distributed SQL database for modern applications, highlighting its features and top use cases.
Case studies of companies benefiting from TiDB are included. The presentation also outlines TiDB's product roadmap, detailing upcoming features and enhancements.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
"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
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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.
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.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
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?
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/
The Microsoft 365 Migration Tutorial For Beginner.pptx
IBM Netezza Training.pdf
1. IBM Netezza Training
Nisa IBM Netezza Training is an integrated framework with essential design principles of
simplicity, scalability, speed and analytical strength.
IBM Netezza is one of the new advanced technologies that merge database analytics and data
warehousing into a high–performance, massively advanced, scalable, parallel analytical
platform. It also automates and improves data processing efficiency and handles sophisticated
algorithms in minutes.
IBM Netezza appliances are redundant and fault-tolerant systems. IBM Netezza replication
services for disaster recovery improves fault tolerance by extending redundancy across local
and wide area networks. It protects against data loss by synchronizing data on the primary
system (the master node) with data on one or more target nodes (subordinates). These nodes
make up a replication set. It is one of the new advanced technologies that merge in-database
analytics and data warehousing.
After this IBM Netezza training, you should be able to recognize how the Netezza
architecture and parallel processing capabilities help modelling and analysis paradigms in
large-scale data sets. In this context, you should fully understand data mining approaches to
solve common business problems in real time.
Course Content:
NPS AMPP Architecture & Various Netezza appliance models
Netezza High Availability Architecture (Clustering, Mirroring, failover)
Installing Netezza system and client software
Installing Netezza Emulator for day-to-day practice
NzAdmin: GUI Admin Tool (Installation & Setup)
Netezza Command Line Interface (CLI)
Manage NPS with CLI commands
Manage User access to Netezza Databases
Monitoring Netezza and Linux logs
Netezza Events (Setup & Monitoring)
Databases & Tables
Data Distribution (Hash, Random), Cluster Base Tables, Table Skew
Generate Statistics, Zone Maps, Materialized Views, Groom Table
Backup & Restore (Host Level, Database Level, Table Level)
Database Refreshes & Migrations
Netezza Appliance migration (For Example: 6.x to 7.x migration)
Data Loading/Unloading using External Tables, NZLOAD, NZ_MIGRATE
Data Loading/Unloading using GUI Tools
Optimizer and query plans
Query history collection & Reporting
Netezza Replication/DR Architecture
2. Techniques to improve Netezza performance
Frequent DBA activities such as SPU replacements, etc
ODBC/JDBC/OLEDB Client Connectivity
Working with IBM Netezza Support to resolve issues
Nisa’s IBM Netezza online course participants will be able to learn:
In-Database Analytics, a fully scalable and parallelized
In-database analytics package
R, an open-source statistical language that operates on Netezza
Matrix Engine, a parallelized, linear algebra package.
On completion of Nisa’s IBM Netezza online training, you will be able to:
Understand how the IBM Netezza architecture and parallel processing capabilities
support modelling and analysis paradigms on large-scale data sets.
Understand data mining methods in the context of use cases to solve common
business problems.
Apply new approaches to modelling and analysis made possible by IBM Netezza
analytics
Use Netezza Analytics data mining methods and statistical functions using the R
client or IBM Netezza.
Nisa training is one of the best platforms to learn all the technologies. Learn your favourite
technology from our industry experts. Our trainers have been in the industry for a long time
and have been working on real-time projects. Training is provided with a 1:1 ratio. Nisa’s
IBM Netezza corporate course study material is provided for their reference. IBM Netezza
certification is also provided for the participants.
For More information about IBM, Netezza training, feel free to reach us
Name: Albert
Email: albert@nisatrainings.com
Ph No: +91-9398381825