This document provides an overview of Skymind, a company that offers deep learning products and services. Skymind was founded by Adam Gibson and Chris Nicholson and offers Deeplearning4j, an open-source deep learning framework for Java. The document outlines Skymind's services including secure deep learning workflows, its Skymind Intelligence Layer for building production-grade models, and containerized deep learning support. Use cases covered include fraud detection, recommendation systems, and predictive analytics.
Survival Guilde for Deep Learning Age at Coding Talks, Istanbul Coding Talks Meet Up (2017)
Presentation video: https://www.youtube.com/watch?v=P2MwuGpRgSQ&t=308s
Coding Talks: https://www.meetup.com/tr-TR/Coding-Talks/
Presentation from the San Diego Advanced Defense Technology Cluster Meeting on December 17 2013 to prime and other small companies focused on providing technology to help keep the world safe,both real and online.
Survival Guilde for Deep Learning Age at Coding Talks, Istanbul Coding Talks Meet Up (2017)
Presentation video: https://www.youtube.com/watch?v=P2MwuGpRgSQ&t=308s
Coding Talks: https://www.meetup.com/tr-TR/Coding-Talks/
Presentation from the San Diego Advanced Defense Technology Cluster Meeting on December 17 2013 to prime and other small companies focused on providing technology to help keep the world safe,both real and online.
Insights: July 26 big data workshop for causesLarry Eason
Insights: Big Data was hosted by IBM at their Innovation Center in Waltham, MA July 26, 2013. Sponsors were my firm DotOrgPower, Findability Sciences and Mind Over Media, Inc.
When AI Meets Education: Opportunities and Innovations 2017.11.02Brad Zdenek
Presentation for the November 2, 2017 OLC Leadership Team meeting, describing the opportunities and recommended methods for spurring and supporting innovation at the intersection of higher education and artificial intelligence.
Institute for the Future (IFTF) Reconfiguring Reality Workshop, Palo Alto, CA Apache Opehnw OpenWhisk Linux Foundation Hyperledger Blockchain Artificial Intelligence Leaderboards
An overview of Tensorflow, and then we'll walk through how to utilize this library within the H2O platform. Tensorflow is an open source, deep learning framework utilized by Google and Deepmind. #h2ony
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Adam Coates at AI Frontiers: AI for 100 Million People with Deep LearningAI Frontiers
Large scale deep learning has made it possible for small teams of researchers and engineers to tackle hard AI problems that previously entailed massive engineering efforts. Adam shares the story of Baidu’s Deep Speech engine: how a recurrent neural network has evolved into a state-of-the-art production speech recognition system in multiple languages, often exceeding the abilities of native speakers. He covers the vision, the implementation, and some lessons learned to illustrate what it takes to build new AI technology that 100 million people will care about.
DeepLearning4J and Spark: Successes and Challenges - François Garillotsparktc
At the recent sold-out Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered a lightning talk called DeepLearning4J and Spark: Successes and Challenges.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
Advanced Spark and Tensorflow Meetup - London - Nov 15, 2016 - Deploy Spark M...Chris Fregly
YouTube Video: https://www.youtube.com/watch?v=RnnweVC7wFc
In this completely 100% Open Source demo-based talk, Chris Fregly from PipelineIO will be addressing an area of machine learning and artificial intelligence that is often overlooked: the real-time, end-user-facing "serving” layer in a hybrid-cloud and on-premise deployment environment using Jupyter, NetflixOSS, Docker, and Kubernetes.
Serving models to end-users in real-time in a highly-scalable, fault-tolerant manner requires not only an understanding of machine learning fundamentals, but also an understanding of distributed systems and scalable microservices.
Chris will combine his work experience from both Databricks and Netflix to present a 100% open source, real-world, hybrid-cloud, on-premise, and NetflixOSS-based production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly-scalable and highly-available robustness.
Speaker Bio
Chris Fregly is a Research Scientist at PipelineIO - a Streaming Analytics and Machine Learning Startup in San Francisco.
Chris is an Apache Spark Contributor, Netflix Open Source Committer, Founder of the Global Advanced Spark and TensorFlow Meetup, and Author of the upcoming book, Advanced Spark, and Creator of the upcoming O'Reilly video series, Scaling TensorFlow Distributed in Production.
Previously, Chris was an engineer at Databricks and Netflix - as well as a Founding Member of the IBM Spark Technology Center in San Francisco.
These slides accompanied a demo of Deeplearning4j, while the meetup explored distributed clustering and various deep learning explanations.
http://www.meetup.com/SF-Neural-Network-Afficianados-Discussion-Group/events/182645252/
Deep-learning is useful in detecting anomalies like fraud, spam and money laundering; identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
Insights: July 26 big data workshop for causesLarry Eason
Insights: Big Data was hosted by IBM at their Innovation Center in Waltham, MA July 26, 2013. Sponsors were my firm DotOrgPower, Findability Sciences and Mind Over Media, Inc.
When AI Meets Education: Opportunities and Innovations 2017.11.02Brad Zdenek
Presentation for the November 2, 2017 OLC Leadership Team meeting, describing the opportunities and recommended methods for spurring and supporting innovation at the intersection of higher education and artificial intelligence.
Institute for the Future (IFTF) Reconfiguring Reality Workshop, Palo Alto, CA Apache Opehnw OpenWhisk Linux Foundation Hyperledger Blockchain Artificial Intelligence Leaderboards
An overview of Tensorflow, and then we'll walk through how to utilize this library within the H2O platform. Tensorflow is an open source, deep learning framework utilized by Google and Deepmind. #h2ony
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Adam Coates at AI Frontiers: AI for 100 Million People with Deep LearningAI Frontiers
Large scale deep learning has made it possible for small teams of researchers and engineers to tackle hard AI problems that previously entailed massive engineering efforts. Adam shares the story of Baidu’s Deep Speech engine: how a recurrent neural network has evolved into a state-of-the-art production speech recognition system in multiple languages, often exceeding the abilities of native speakers. He covers the vision, the implementation, and some lessons learned to illustrate what it takes to build new AI technology that 100 million people will care about.
DeepLearning4J and Spark: Successes and Challenges - François Garillotsparktc
At the recent sold-out Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered a lightning talk called DeepLearning4J and Spark: Successes and Challenges.
Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries — all with an easy-access interface.
Along the way, he pointed out the strategic points of parallelization of computation across machines and gave insight on where Spark helps — and where it doesn't.
Advanced Spark and Tensorflow Meetup - London - Nov 15, 2016 - Deploy Spark M...Chris Fregly
YouTube Video: https://www.youtube.com/watch?v=RnnweVC7wFc
In this completely 100% Open Source demo-based talk, Chris Fregly from PipelineIO will be addressing an area of machine learning and artificial intelligence that is often overlooked: the real-time, end-user-facing "serving” layer in a hybrid-cloud and on-premise deployment environment using Jupyter, NetflixOSS, Docker, and Kubernetes.
Serving models to end-users in real-time in a highly-scalable, fault-tolerant manner requires not only an understanding of machine learning fundamentals, but also an understanding of distributed systems and scalable microservices.
Chris will combine his work experience from both Databricks and Netflix to present a 100% open source, real-world, hybrid-cloud, on-premise, and NetflixOSS-based production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly-scalable and highly-available robustness.
Speaker Bio
Chris Fregly is a Research Scientist at PipelineIO - a Streaming Analytics and Machine Learning Startup in San Francisco.
Chris is an Apache Spark Contributor, Netflix Open Source Committer, Founder of the Global Advanced Spark and TensorFlow Meetup, and Author of the upcoming book, Advanced Spark, and Creator of the upcoming O'Reilly video series, Scaling TensorFlow Distributed in Production.
Previously, Chris was an engineer at Databricks and Netflix - as well as a Founding Member of the IBM Spark Technology Center in San Francisco.
These slides accompanied a demo of Deeplearning4j, while the meetup explored distributed clustering and various deep learning explanations.
http://www.meetup.com/SF-Neural-Network-Afficianados-Discussion-Group/events/182645252/
Deep-learning is useful in detecting anomalies like fraud, spam and money laundering; identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
TensorFrames: Google Tensorflow on Apache SparkDatabricks
Presentation at Bay Area Spark Meetup by Databricks Software Engineer and Spark committer Tim Hunter.
This presentation covers how you can use TensorFrames with Tensorflow to distributed computing on GPU.
At StampedeCon 2014, John Tran of NVIDIA presented "GPUs in Big Data." Modern graphics processing units (GPUs) are massively parallel general-purpose processors that are taking Big Data by storm. In terms of power efficiency, compute density, and scalability, it is clear now that commodity GPUs are the future of parallel computing. In this talk, we will cover diverse examples of how GPUs are revolutionizing Big Data in fields such as machine learning, databases, genomics, and other computational sciences.
Presented at the GPU Technology Conference 2012 in San Jose, California.
Tuesday, May 15, 2012.
Standards such as Scalable Vector Graphics (SVG), PostScript, TrueType outline fonts, and immersive web content such as Flash depend on a resolution-independent 2D rendering paradigm that GPUs have not traditionally accelerated. This tutorial explains a new opportunity to greatly accelerate vector graphics, path rendering, and immersive web standards using the GPU. By attending, you will learn how to write OpenGL applications that accelerate the full range of path rendering functionality. Not only will you learn how to render sophisticated 2D graphics with OpenGL, you will learn to mix such resolution-independent 2D rendering with 3D rendering and do so at dynamic, real-time rates.
SIGGRAPH 2012: GPU-Accelerated 2D and Web RenderingMark Kilgard
Video replay: http://nvidia.fullviewmedia.com/siggraph2012/ondemand/SS106.html
Location: West Hall Meeting Room 503, Los Angeles Convention Center
Date: Wednesday, August 8, 2012
Time: 2:40 PM – 3:40 PM
The future of GPU-based visual computing integrates the web, resolution-independent 2D graphics, and 3D to maximize interactivity and quality while minimizing consumed power. See what NVIDIA is doing today to accelerate resolution-independent 2D graphics for web content. This presentation explains NVIDIA's unique "stencil, then cover" approach to accelerating path rendering with OpenGL and demonstrates the wide variety of web content that can be accelerated with this approach.
More information: http://developer.nvidia.com/nv-path-rendering
Accelerating Machine Learning Applications on Spark Using GPUsIBM
Matrix factorization (MF) is widely used in recommendation systems. We present cuMF, a highly-optimized matrix factorization tool with supreme performance on graphics processing units (GPUs) by fully utilizing the GPU compute power and minimizing the overhead of data movement. Firstly, we introduce a memory-optimized alternating least square (ALS) method by reducing discontiguous memory access and aggressively using registers to reduce memory latency. Secondly, we combine data parallelism with model parallelism to scale to multiple GPUs.
Results show that with up to four GPUs on one machine, cuMF can be up to ten times as fast as those on sizable clusters on large scale problems, and has impressively good performance when solving the largest matrix factorization problem ever reported.
This presentation describes the components of GPU ecosystem for compute, provides overview of existing ecosystems, and contains a case study on NVIDIA Nsight
Enabling Graph Analytics at Scale: The Opportunity for GPU-Acceleration of D...odsc
From social networks to protein networks to financial transactions, graphs are everywhere. Graph Analytics represent a key tool for data science to take advance of this type of network information. Many “Bigdata” and NoSQL techniques for analysis and data science that work well for relational and structured data, do not scale effectively when applied to challenges in graph analytics and traversal algorithms. The data locality and graph access patterns challenge existing HW architectures and place a premium on bandwidth to main memory.GPUs currently have 10X advantage over CPUs in this area.
The advantage is projected to grow to 100X by 2016. This talk will discuss why GPUs are game-changer by dramatically improving the price-performance ratio for very large graph analytics over existing technologies. It will present results for work in GPU Acceleration of graph analytics within both research and industry applications.
In this video from SC13, Vinod Tipparaju presents an Heterogeneous System Architecture Overview.
"The HSA Foundation seeks to create applications that seamlessly blend scalar processing on the CPU, parallel processing on the GPU, and optimized processing on the DSP via high bandwidth shared memory access enabling greater application performance at low power consumption. The Foundation is defining key interfaces for parallel computation utilizing CPUs, GPUs, DSPs, and other programmable and fixed-function devices, thus supporting a diverse set of high-level programming languages and creating the next generation in general-purpose computing."
Learn more: http://hsafoundation.com/
Watch the video presentation: http://wp.me/p3RLHQ-aXk
DN18 | Demystifying the Buzz in Machine Learning! (This Time for Real) | Dat ...Dataconomy Media
Abstract of the Presentation:
When Dat Tran started his data science career in 2013, everyone was into big data. In fact, big data was at the peak of inflated expectations (according to Gartner). You had to use tools like Hadoop and Spark to be one of the cool kids. Many data prophets out there told you that data is the new oil, or even gold. Year 2018, things haven’t changed. Data is still cool and going strong. It’s eating the world- and yes, you still need big data, and now also deep deep very deep learning. There’s a lot of bullshit bingo out there.
In this talk, Dat Tran wants to demystify the buzz in machine learning by presenting some simple guidelines for successful data projects and real practical use cases. He will also share use cases from idealo, Germany’s largest price comparison service. And yes it involves deep learning, and yes it can be quite technical sometimes as well.
About the Author:
Dat Tran is currently co-heading the data team at idealo.de, where he leads a team of Data Scientists and Data Engineers. His aim is to turn idealo into a machine learning powerhouse. His research interests are diverse, from traditional machine learning to deep learning. Previously, he worked for Pivotal Labs and Accenture. He is a regular speaker and has presented at PyData and Cloud Foundry Summit. He also blogs about his work on Medium. His background is in Operations Research and Econometrics. Dat received his MSc in Economics from Humboldt University of Berlin.
In an era of rapid technological change, it can be difficult to keep up. Enter our speakers, three library professionals who engage with technology in multiple roles as instructors, programmers, and library administrators. This presentation will highlight key technologies and trends, considering them from a library perspective. By exploring the tech landscape together, we can move our organizations to a proactive, rather than reactive posture.
Java Enterprise Applications in the Cloud: Fast, Fun and Easier than EverStefan Schmidt
Building the next generation of enterprise Web applications is now easier than ever. This presentation shows you how to use Spring Roo to quickly develop high-performance rich internet applications in Java with a UI of your choice.
It covers
• How Java offers an easy, high-performance, tooling-optimized development experience
• Reverse-engineering an existing database to build an application with an MVC, JSF, or GWT front end in minutes
• Using important standards such as JPA, JavaBeans validation, and EJB 3 annotations
• Easily round-tripping changes between your UI, middle tier, and database
• Deploying to clouds such as CloudFoundry, VMforce, Google App Engine, or AWS Elastic Beanstalk
Book: Software Architecture and Decision-MakingSrinath Perera
Uncertainty is the leading cause of mistakes made by practicing software architects. The primary goal of architecture is to handle uncertainty arising from user cases as well as architectural techniques. The book discusses how to make architectural decisions and manage uncertainty. From the book, You will learn common problems while designing a system, a default solution for each, more complex alternatives, and 5Q & 7P (Five Questions and Seven Principles) that help you choose.
Book, https://amzn.to/3v1MfZX
Blog: http://tinyurl.com/swdmblog
Six min video - https://youtu.be/jtnuHvPWlYU
5 facets of cloud computing - Presentation to AGBCRaymond Gao
My presentation to AGBC (American German Business Club) on Cloud Computing and Social Causes. How doing non-profit work helps finding and validates Use Cases, the heart of any application, business venture, etc.
Today it seems like every company is embarking on a journey of Digital Transformation. While this is a necessary shift, only those companies that see the big picture will succeed at it, which means looking at not only the technological aspect of Digital Transformation but its wider impact on processes, people, and programs.
Successful Digital Transformation calls for a platform that can work with existing industrial processes and software while enabling innovation in those areas. It also calls for a platform that team members across the organization can get onboard with and use to collaborate. In this webinar, experts from Inductive Automation will share insights into all this and more, so don’t skip this one!
"What we learned from 5 years of building a data science software that actual...Dataconomy Media
"What we learned from 5 years of building a data science software that actually works for everybody." Dr. Dennis Proppe, CTO and Chief Data Scientist at GPredictive GmbH
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
https://www.youtube.com/c/DataNatives
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Dennis Proppe is the CTO and Chief Data Scientist at Gpredictive, where he helps building software that enables data scientists to build and deploy predictive models in a few minutes instead of weeks. He has 10 years+ of expertise in extracting business value from data. Before co-founding Gpredictive, he worked as a marketing science consultant. Dennis holds a Ph.D. in statistical marketing.
Learn more about enterprise frameworks and why your technology business and you need to be thinking about your software application architecture at scale.
The slide deck from a presentation on Oct. 2, 2018 explaining some of the best ways Engineering and Information Security Teams can work together for the betterment of a technology company.
Material for Azure Machine Learning tutorial lecture, held within Data Mining course of MoS in Engineering in Computer Science at Università degli Studi di Roma "La Sapienza" (A.Y. 2016/2017).
Lecturers:
Fabio Rosato - rosato.1565173@studenti.uniroma1.it
Giacomo Lanciano - lanciano.1487019@studenti.uniroma1.it
Francisco Ferreres Garcia - matakukos@gmail.com
Leonardo Martini - martini.1722989@studenti.uniroma1.it
Simone Caldaro - caldaro.1324152@studenti.uniroma1.it
Na Zhu - nana.zhu@hotmail.com
Github repo: https://github.com/giacomolanciano/Azure-Machine-Learning-tutorial
Video tutorial: https://youtu.be/_zvPX6Kk7z8
I gave a talk on the basics of Artificial Intelligence and Machine Learning in Android Developers Meetup in Gurgaon, India.
In this session I explained the basics of AI/ML, how ML is different from AI and also give brief introduction to Deep learning. I explained how ML works and what are basic types of ML - Supervised Learning, unsupervised learning and Reinforcement learning. What are the applications and most recent prominent examples of ML. And then I moved on to introduce the major frameworks for developers in this field. I gave brief introduction to Google Cloud vision, OpenCV, Pytorch, AWS Rekognition and finally ML Kit from Google. I explained ML kit in detail and how developers can use it. I also gave a demo on ML kit.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
2. ● SKYMIND
○ Deep Learning for enterprise
○ founded by Adam Gibson and Chris Nicholson
● Daehyun Kim
○ DL engineer
● Christine Leigh
○ Head of Biz dev., Korea
WHO WE ARE
3. ● WHAT IS AI?
● USE CASES
● WHAT WE DO
● DL4J
● DL IN PRODUCTION
● SKYMIND SERVICES
OVERVIEW
5. ● Fraud Detection
● Recommender Systems
● Image Recognition
● Predictive Analytics
○ Market Forecasting
○ Churn/Turnover
DEEP LEARNING USE CASES
GENERAL USE CASES
6. ● TELECOM
○ FRANCE TELECOM’S MOBILE UNIT
ORANGE
● DATA CENTER
○ CANONICAL & IBM
● BANK
○ LARGE U.S. BANK
DEEP LEARNING USE CASES
CASE STUDIES
11. ● In Academia vs. Production
● Data Scientists vs. Engineers
● Defining Production
● A solution
DEEP LEARNING IN PRODUCTION
12. ● Secure, production-ready deep learning workflows
for finance, healthcare, retail, manufacturing, telcos.
ENTERPRISE DEEP LEARNING
13. - Skymind’s open-source enterprise distribution
- contains all of the necessary open-source components and proprietary
vendor integrations to build production-grade deep learning solutions.
SKYMIND INTELLIGENCE LAYER (SKIL)
DEFINITION
15. CONTAINERIZED DEEP LEARNING FOR
ANY PLATFORM
● ON PREMISE
● PUBLIC CLOUD
○ AWS
○ AZURE
○ GCE
● HYBRID CLOUD
● DESKTOP
○ WINDOWS
○ MAC
● ANDROID
DOCKER
DC/OS + SPARK
MESOS
19. Written by Skymind’s
Adam Gibson and Josh Patterson.
#1 New Release
in Data Modeling and Design
on Amazon.
DEEP LEARNING : A PRACTITIONER’S
APPROACH