The Hive Think Tank: AI in The Enterprise by Venkat SrinivasanThe Hive
This The Hive Think Tank talk by Venkat Srinivasan, CEO of RAGE Frameworks, focuses on successful applications of AI in the Enterprise. We start with a broad and more inclusive definition of AI in the context of enterprise business processes.
We introduce a taxonomy of AI solution methods that broaden the focus beyond a narrow focus on deep learning based on neural nets. In line with the taxonomy, we present several successful AI applications in use today at major corporations across industries including financial services, manufacturing/retail, professional services, logistics. These applications range from commercial lending, contract review, customer service intelligence, market and competitive intelligence, signals for capital markets, regulatory compliance and others.
Meg Mude, Intel - Data Engineering Lifecycle Optimized on Intel - H2O World S...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/cnU6sqd31JU
Developing meaningful AI applications requires complete data lifecycle management. Sourcing, harvesting, labelling and ensuring the conduit to consume data structures and repositories is critical for model accuracy....but, one of the least talked about subjects. Intel’s optimized technologies enable efficient delivery of complete data samples to develop (and deploy) meaningful outcomes. During this session, we’ll review the considerations and criticality of data lifecycle management for the AI production pipeline.
Bio: Meg brings more than 17 years of global product, engineering and solutions experience. She is presently a Solutions Architect with Intel Corporation specializing in Visual Compute and AAI (Analytics and AI) Architecture. She is passionate about the potential for technology to improve the quality of peoples’ lives and humanity on the whole.
Introdution to Dataops and AIOps (or MLOps)Adrien Blind
This presentation introduces the audience to the DataOps and AIOps practices. It deals with organizational & tech aspects, and provide hints to start you data journey.
Choosing the Right Document Processing Solution for Healthcare OrganizationsProvectus
Looking to automate document processing in your healthcare organization? Learn from Provectus & AWS experts how to make data capture, conversion, and analytics more efficient. Process and manage documents faster and on a larger scale with AI & Machine Learning.
In this presentation, we offer management and engineering perspectives on document processing with AI, to help you explore available options. Whether you are looking for a ready-made solution or plan to build a custom solution of your own, this webinar will help you find the best fit for your healthcare use cases.
The Hive Think Tank: AI in The Enterprise by Venkat SrinivasanThe Hive
This The Hive Think Tank talk by Venkat Srinivasan, CEO of RAGE Frameworks, focuses on successful applications of AI in the Enterprise. We start with a broad and more inclusive definition of AI in the context of enterprise business processes.
We introduce a taxonomy of AI solution methods that broaden the focus beyond a narrow focus on deep learning based on neural nets. In line with the taxonomy, we present several successful AI applications in use today at major corporations across industries including financial services, manufacturing/retail, professional services, logistics. These applications range from commercial lending, contract review, customer service intelligence, market and competitive intelligence, signals for capital markets, regulatory compliance and others.
Meg Mude, Intel - Data Engineering Lifecycle Optimized on Intel - H2O World S...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/cnU6sqd31JU
Developing meaningful AI applications requires complete data lifecycle management. Sourcing, harvesting, labelling and ensuring the conduit to consume data structures and repositories is critical for model accuracy....but, one of the least talked about subjects. Intel’s optimized technologies enable efficient delivery of complete data samples to develop (and deploy) meaningful outcomes. During this session, we’ll review the considerations and criticality of data lifecycle management for the AI production pipeline.
Bio: Meg brings more than 17 years of global product, engineering and solutions experience. She is presently a Solutions Architect with Intel Corporation specializing in Visual Compute and AAI (Analytics and AI) Architecture. She is passionate about the potential for technology to improve the quality of peoples’ lives and humanity on the whole.
Introdution to Dataops and AIOps (or MLOps)Adrien Blind
This presentation introduces the audience to the DataOps and AIOps practices. It deals with organizational & tech aspects, and provide hints to start you data journey.
Choosing the Right Document Processing Solution for Healthcare OrganizationsProvectus
Looking to automate document processing in your healthcare organization? Learn from Provectus & AWS experts how to make data capture, conversion, and analytics more efficient. Process and manage documents faster and on a larger scale with AI & Machine Learning.
In this presentation, we offer management and engineering perspectives on document processing with AI, to help you explore available options. Whether you are looking for a ready-made solution or plan to build a custom solution of your own, this webinar will help you find the best fit for your healthcare use cases.
GITEX Big Data Conference 2014 – SAP PresentationPedro Pereira
Big, Fast and Predictive Data: How to Extract Real Business Value – in real time.
90% of the world’s data was created in the last two years. If you can harness it, it will revolutionize the way you do business. Big Data solutions can help extract real business value – in real time.
Artificial Intelligence and Data-centric businesses by Óscar Méndez at Big Da...Big Data Spain
Artificial Intelligence and Data-centric businesses.
https://www.bigdataspain.org/2017/talk/tbc
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Overview of end-to-end lifecycle to productize and commercialize alternative datasets at S&P Global Market Intelligence
Benefits to discuss:
How S&P Market Intelligence develops new alternative datasets
How S&P Market Intelligence develops robust production processes for alternative data
S&P Global Market Intelligence GTM strategy and capabilities to sell alternative data
Big Data Roundtable. Why, how, where, which, and when to start doing Big DataRaul Goycoolea Seoane
Big Data Roundtable. Why, how, where, which, and when to start doing Big Data. Why Big Data is not just a new keyword, can be a competitive advantage if it's doing right and on time, and most important, before you competition.
Driving Digital Transformation through Service-Centric AIOpsOpsRamp
Driving Digital Transformation through Service-Centric AIOps. Reduce the noise with artificial intelligence.
To learn more about how OpsRamp can help you manage the unmanageable, visit us at - https://www.opsramp.com
Also, follow us on social media channels to learn about product highlights, news, announcements, events, conferences and more -
Twitter - https://www.twitter.com/OpsRamp
LinkedIn - https://www.linkedin.com/company/opsramp
AI Data Acquisition and Governance: Considerations for SuccessDatabricks
data pipeline, governance, and for growth and updating models regularly needs to be part of the AI strategy from the outset.
This session will cover:
Defining AI governance: What this means and how definitions of subjects like ethics and effectiveness can differ between organizations.
Data governance: Companies must rely on an AI governance program to ensure only high-quality, unbiased and consistent data are used in training.
AI is a growing necessity for enterprises / businesses; it provides an avenue for scaling quickly and efficiently.
Best practices / implementation: how to implement AI that meets the requirements of the organization’s defined sets of governances.
Planning the data pipeline and growing/updating the models: AI is not static in the real world; models must be frequently updated to maintain relevance and accuracy.
3 key takeaways or attendee benefits of the session:
Understand how to assess your organization’s need for AI; how to identify the opportune areas for transforming processes, interactions, scaling, cost.
How to start the implementation process. Defining data and AI governance and how to build the training data pipeline within that framework.
Best practices for maintaining AI; how to use data to evaluate models and continuously iterate on them to reflect the real world.
The Hive Think Tank: Talk by Mohandas Pai - India at 2030, How Tech Entrepren...The Hive
Over the next 15 years, India's growth will be fueled by its startups. Today, there are over 20,000 startups in India that have created a value of $80 billion and employ 325,000 people. Over the next ten years, by 2025, there will be 100,000 startups in the country that would have created over $500 billion of value and employ 3.2 million people.
This talk is about India's growth over the next 15 years and the prominent role that entrepreneurs and startups will play in its rapid evolution.
What's in store for Big Data in 2015? Will the 'Internet of Things' fuel the Industrial Internet? Will Big Data get Cloudy? Check out the top five Big Data predictions for 2015 according to Quentin Gallivan, CEO, Pentah0
Mobile device management (MDM) provides the endpoint-focused processes and solutions for accelerating user productivity and device reliability. However, selecting an MDM platform that directly addresses an organization’s unique requirements and challenges can often be confusing given the diverse range of features and cost elements offered by competing solution providers.
These slides from Steve Brasen, managing research director at leading IT analyst firm Enterprise Management Associates (EMA), reveal key results from the recently published EMA Radar™ on Mobile Device Management. In this side-by-side comparison of the 12 leading MDM platforms, solutions are empirically compared and graded against a broad range of measurements to objectively determine overall product strengths and cost efficiencies.
Hadoop is regarded as a key capability for implementing Big Data initiatives in the enterprise, but organizations have yet to realize its full business benefits. In this webinar, Pivotal and guest Forrester Research, Inc. Identify the use cases driving Hadoop adoption, and explore what is needed to transform initial investments into results.
Learn about:
Challenges Hadoop introduces, and how the right tools and platforms can help address them
Shifts in the industry with regards to SQL and NoSQL systems and their implications to Big Data analytics
Applying in-memory technologies for data management systems, data analytics, transactional processing and operational databases
Watch the on-demand webinar here:
http://www.pivotal.io/big-data/pivotal-forrester-operationalizing-data-analytics-webinar
Learn how to maximize business value from all of your data here: http://www.pivotal.io/big-data/pivotal-hd
Using Machine Learning at Scale: A Gaming Industry Experience!Databricks
Games earn more money than movies and music combined. That means a lot of data is generated as well. One of the development considerations for ML Pipeline is that it must be easy to use, maintain, and integrate. However, it doesn’t necessarily have to be developed from scratch. By using well-known libraries/frameworks and choice of efficient tools whenever possible, we can avoid “reinventing the wheel”, making it flexible and extensible.
Part 3: Models in Production: A Look From Beginning to EndCloudera, Inc.
3 Things to Learn About:
-How to uplevel your existing analytics stack with a collaborative environment that supports the latest open source languages and libraries.
-How to get better use of your core data management investments while opening up new supported tools for data science.
-How to expand data science outside of silo’d environments and enable self-service data science access.
GITEX Big Data Conference 2014 – SAP PresentationPedro Pereira
Big, Fast and Predictive Data: How to Extract Real Business Value – in real time.
90% of the world’s data was created in the last two years. If you can harness it, it will revolutionize the way you do business. Big Data solutions can help extract real business value – in real time.
Artificial Intelligence and Data-centric businesses by Óscar Méndez at Big Da...Big Data Spain
Artificial Intelligence and Data-centric businesses.
https://www.bigdataspain.org/2017/talk/tbc
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Overview of end-to-end lifecycle to productize and commercialize alternative datasets at S&P Global Market Intelligence
Benefits to discuss:
How S&P Market Intelligence develops new alternative datasets
How S&P Market Intelligence develops robust production processes for alternative data
S&P Global Market Intelligence GTM strategy and capabilities to sell alternative data
Big Data Roundtable. Why, how, where, which, and when to start doing Big DataRaul Goycoolea Seoane
Big Data Roundtable. Why, how, where, which, and when to start doing Big Data. Why Big Data is not just a new keyword, can be a competitive advantage if it's doing right and on time, and most important, before you competition.
Driving Digital Transformation through Service-Centric AIOpsOpsRamp
Driving Digital Transformation through Service-Centric AIOps. Reduce the noise with artificial intelligence.
To learn more about how OpsRamp can help you manage the unmanageable, visit us at - https://www.opsramp.com
Also, follow us on social media channels to learn about product highlights, news, announcements, events, conferences and more -
Twitter - https://www.twitter.com/OpsRamp
LinkedIn - https://www.linkedin.com/company/opsramp
AI Data Acquisition and Governance: Considerations for SuccessDatabricks
data pipeline, governance, and for growth and updating models regularly needs to be part of the AI strategy from the outset.
This session will cover:
Defining AI governance: What this means and how definitions of subjects like ethics and effectiveness can differ between organizations.
Data governance: Companies must rely on an AI governance program to ensure only high-quality, unbiased and consistent data are used in training.
AI is a growing necessity for enterprises / businesses; it provides an avenue for scaling quickly and efficiently.
Best practices / implementation: how to implement AI that meets the requirements of the organization’s defined sets of governances.
Planning the data pipeline and growing/updating the models: AI is not static in the real world; models must be frequently updated to maintain relevance and accuracy.
3 key takeaways or attendee benefits of the session:
Understand how to assess your organization’s need for AI; how to identify the opportune areas for transforming processes, interactions, scaling, cost.
How to start the implementation process. Defining data and AI governance and how to build the training data pipeline within that framework.
Best practices for maintaining AI; how to use data to evaluate models and continuously iterate on them to reflect the real world.
The Hive Think Tank: Talk by Mohandas Pai - India at 2030, How Tech Entrepren...The Hive
Over the next 15 years, India's growth will be fueled by its startups. Today, there are over 20,000 startups in India that have created a value of $80 billion and employ 325,000 people. Over the next ten years, by 2025, there will be 100,000 startups in the country that would have created over $500 billion of value and employ 3.2 million people.
This talk is about India's growth over the next 15 years and the prominent role that entrepreneurs and startups will play in its rapid evolution.
What's in store for Big Data in 2015? Will the 'Internet of Things' fuel the Industrial Internet? Will Big Data get Cloudy? Check out the top five Big Data predictions for 2015 according to Quentin Gallivan, CEO, Pentah0
Mobile device management (MDM) provides the endpoint-focused processes and solutions for accelerating user productivity and device reliability. However, selecting an MDM platform that directly addresses an organization’s unique requirements and challenges can often be confusing given the diverse range of features and cost elements offered by competing solution providers.
These slides from Steve Brasen, managing research director at leading IT analyst firm Enterprise Management Associates (EMA), reveal key results from the recently published EMA Radar™ on Mobile Device Management. In this side-by-side comparison of the 12 leading MDM platforms, solutions are empirically compared and graded against a broad range of measurements to objectively determine overall product strengths and cost efficiencies.
Hadoop is regarded as a key capability for implementing Big Data initiatives in the enterprise, but organizations have yet to realize its full business benefits. In this webinar, Pivotal and guest Forrester Research, Inc. Identify the use cases driving Hadoop adoption, and explore what is needed to transform initial investments into results.
Learn about:
Challenges Hadoop introduces, and how the right tools and platforms can help address them
Shifts in the industry with regards to SQL and NoSQL systems and their implications to Big Data analytics
Applying in-memory technologies for data management systems, data analytics, transactional processing and operational databases
Watch the on-demand webinar here:
http://www.pivotal.io/big-data/pivotal-forrester-operationalizing-data-analytics-webinar
Learn how to maximize business value from all of your data here: http://www.pivotal.io/big-data/pivotal-hd
Using Machine Learning at Scale: A Gaming Industry Experience!Databricks
Games earn more money than movies and music combined. That means a lot of data is generated as well. One of the development considerations for ML Pipeline is that it must be easy to use, maintain, and integrate. However, it doesn’t necessarily have to be developed from scratch. By using well-known libraries/frameworks and choice of efficient tools whenever possible, we can avoid “reinventing the wheel”, making it flexible and extensible.
Part 3: Models in Production: A Look From Beginning to EndCloudera, Inc.
3 Things to Learn About:
-How to uplevel your existing analytics stack with a collaborative environment that supports the latest open source languages and libraries.
-How to get better use of your core data management investments while opening up new supported tools for data science.
-How to expand data science outside of silo’d environments and enable self-service data science access.
3 Things to Learn About:
* How Sparklyr supports a complete backend for dplyr, a popular tool for working with data frame objects both in memory and out of memory
* How Sparklyr llows data scientists to use dplyr to translate R code into Spark SQL
* How Sparklyr supports MLlib so data scientists can run classifiers, regressions, and many other machine learning algorithms in Spark
Unlocking data science in the enterprise - with Oracle and ClouderaCloudera, Inc.
Today, leading organizations struggle to make their data scientists productive in their modern data platforms. Data scientists find it difficult to use their existing open source languages (e.g. Python, R) and libraries with Hadoop, especially when the clusters are secured with Kerberos. At the same time, IT doesn't want to give special access to these users, who require very diverse and specific environment configurations to run their experiments. As a result, most data science teams work away from the big data cluster, often on their laptops or in other data silos. The negative business impacts are a lack of insight and agility for the most advanced users, and the security, governance, and cost issues that arise from data silos.
Machine Learning Model Deployment: Strategy to ImplementationDataWorks Summit
This talk will introduce participants to the theory and practice of machine learning in production. The talk will begin with an intro on machine learning models and data science systems and then discuss data pipelines, containerization, real-time vs. batch processing, change management and versioning.
As part of this talk, an audience will learn more about:
• How data scientists can have the complete self-service capability to rapidly build, train, and deploy machine learning models.
• How organizations can accelerate machine learning from research to production while preserving the flexibility and agility of data scientists and modern business use cases demand.
A small demo will showcase how to rapidly build, train, and deploy machine learning models in R, python, and Spark, and continue with a discussion of API services, RESTful wrappers/Docker, PMML/PFA, Onyx, SQLServer embedded models, and
lambda functions.
Speakers
Sagar Kewalramani, Solutions Architect
Cloudera
Justin Norman, Director, Research and Data Science Services
Cloudera Fast Forward Labs
Introducing Cloudera Data Science Workbench for HDP 2.12.19Cloudera, Inc.
Cloudera’s Data Science Workbench (CDSW) is available for Hortonworks Data Platform (HDP) clusters for secure, collaborative data science at scale. During this webinar, we provide an introductory tour of CDSW and a demonstration of a machine learning workflow using CDSW on HDP.
Cloudera Altus: Big Data in the Cloud Made EasyCloudera, Inc.
Cloudera Altus makes it easier for data engineers, ETL developers, and anyone who regularly works with raw data to process that data in the cloud efficiently and cost effectively. In this webinar we introduce our new platform-as-a-service offering and explore challenges associated with data processing in the cloud today, how Altus abstracts cluster overhead to deliver easy, efficient data processing, and unique features and benefits of Cloudera Altus.
Manufacturers have an abundance of data, whether from connected sensors, plant systems, manufacturing systems, claims systems and external data from industry and government. Manufacturers face increased challenges from continually improving product quality, reducing warranty and recall costs to efficiently leveraging their supply chain. For example, giving the manufacturer a complete view of the product and customer information integrating manufacturing and plant floor data, with as built product configurations with sensor data from customer use to efficiently analyze warranty claim information to reduce detection to correction time, detect fraud and even become proactive around issues requires a capable enterprise data hub that integrates large volumes of both structured and unstructured information. Learn how an enterprise data hub built on Hadoop provides the tools to support analysis at every level in the manufacturing organization.
Cloudera Analytics and Machine Learning Platform - Optimized for Cloud Stefan Lipp
Take Data Management to the next level: Connect Analytics and Machine Learning in a single governed platform consisting of a curated protable open source stack. Run this platform on-prem, hybrid or multicloud, reuse code and models avoid lock-in.
Introducing the data science sandbox as a service 8.30.18Cloudera, Inc.
How can companies integrate data science into their businesses more effectively? Watch this recorded webinar and demonstration to hear more about operationalizing data science with Cloudera Data Science Workbench on Cazena’s fully-managed cloud platform.
The Vision & Challenge of Applied Machine LearningCloudera, Inc.
Learn how Cloudera provides a unified platform that breaks down data silos commonly seen in organizations. By unifying the data needed for applied machine learning, organizations are better equipped to gather valuable insights from their data.
What it takes to bring Hadoop to a production-ready stateClouderaUserGroups
While Hadoop may be a hot topic and is probably the buzziest big data term, the fact is that many Hadoop projects get stuck in pilot mode. We hear a number of reasons for this.
• “It’s too complicated.”
• “I don’t have the right resources.”
• “Security and compliance are never going to approve this.”
This session digs deep into why certain projects seem destined to remain in development. We’ll also cover what it takes to bring Hadoop to a production-ready state and convince management that it’s time to start using Hadoop to store and analyze real business data.
Quantum Computing (IBM Q) - Hive Think Tank Event w/ Dr. Bob Sutor - 02.22.18The Hive
Dr. Bob Sutor is Vice President for AI, Blockchain, and Quantum Solutions at IBM Research. In this role he is the R&D executive leading a large global group of scientists, software engineers, and designers who create and integrate leading edge science and technologies to give IBM's clients the most advanced solutions available. Our work is often mathematically-based and thus includes AI technologies like machine learning, deep learning, text and image analytics, statistics, predictive analytics, and optimization. Sutor co-leads the IBM Research effort to support IBM's commercial blockchain efforts with advanced innovations across a broad range of its embedded technologies. He leads the group developing the next generation software stack and algorithms for quantum computers.
Dr. Sutor has an undergraduate degree from Harvard College and a Ph.D. from Princeton University, both in Mathematics.
The Hive Think Tank: Rendezvous Architecture Makes Machine Learning Logistics...The Hive
Think Tank Event 10/23/2017, hosted by The Hive and presented by Ted Dunning, Chief Application Architect of MapR Technologies and Ellen Friedman of MapR Technologies.
The Hive Think Tank: Machine Learning Applications in Genomics by Prof. Jian ...The Hive
In this The Hive Think Tank talk, Professor Jian Ma introduces machine learning methods that can be used to help tackle some of the most intriguing questions in genomics and biomedicine. He discusses the research projects in his group to study genome structure and function, including algorithms to unravel complex genomic aberrations in cancer genomes and gene regulatory principles encoded in our genome, by utilizing
probabilistic graphical models and deep neural network techniques. The knowledge obtained from such computational methods can greatly enhance our ability to understand disease genomes.
The Hive Think Tank: The Future Of Customer Support - AI Driven AutomationThe Hive
The Hive Think Tank Panel Discussion moderated by Kate Leggett (Forrester) with panelists: Allan Leinwand (ServiceNow), Nitin Narkhede (Wipro), Jason Smale (Zendesk), Dan Turchin (Neva). The future of customer support is AI-driven virtual agents. Soon, we’ll interact conversationally with bots that know who we are, how we’re impacted, and what we need. Soon, the capabilities of virtual agents will far exceed those of today’s best human agents. We’ll receive support that is more reliable than friends, more accurate than social media, and less frustrating than waiting on hold.
The Hive Think Tank: The Content Trap - Strategist's Guide to Digital ChangeThe Hive
In this The Hive Think Tank talk Harvard Business School Professor of Strategy Prof. Bharat Anand shares his insights on the Digital innovation trends that are shaping the way organizations will act in the future.
In this talk, Professor Anand presents the findings from his forthcoming book. To answer these questions, Anand examines a range of businesses around the world, from Chinese internet giant Tencent to Scandinavian digital trailblazer Schibsted, from The New York Times to The Economist, and from talent management to the future of education.
In this The Hive Think Tank talk, Heron team provides an introduction to Heron, how it is being used at Twitter and shares an operating experiences and challenges of running Heron at scale. They recently announced the open sourcing of Heron under the permissive Apache v2.0 license. Heron has been in production nearly 2 years and is widely used by several teams for diverse use cases. Prior to Heron, Twitter used Apache Storm, which we open sourced in 2011. Heron features a wide array of architectural improvements and is backward compatible with the Storm ecosystem for seamless adoption.
The Hive Think Tank: Unpacking AI for Healthcare The Hive
In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.
The Hive Think Tank: Translating IoT into Innovation at Every Level by Prith ...The Hive
In this presentation Prith Banerjee discusses how a sustainable future must become radically more efficient with the way we use energy. He shared how the Internet of Things (IoT) and the convergence of Operational Technology (OT) and Information Technology (IT) are enabling Schneider Electric's innovation at every level, redefining power and automation for a new world of energy which is more electric, decarbonized, decentralized and digitized. Prith shared how, in this new world of energy, Schneider ensures that Life Is On everywhere, for everyone and at every moment. He also shared a set of IoT predictions for the future, based on findings of the company’s recent IoT Survey of 2,500 top business executives.
The Hive Think Tank - The Microsoft Big Data Stack by Raghu Ramakrishnan, CTO...The Hive
Until recently, data was gathered for well-defined objectives such as auditing, forensics, reporting and line-of-business operations; now, exploratory and predictive analysis is becoming ubiquitous, and the default increasingly is to capture and store any and all data, in anticipation of potential future strategic value. These differences in data heterogeneity, scale and usage are leading to a new generation of data management and analytic systems, where the emphasis is on supporting a wide range of very large datasets that are stored uniformly and analyzed seamlessly using whatever techniques are most appropriate, including traditional tools like SQL and BI and newer tools, e.g., for machine learning and stream analytics. These new systems are necessarily based on scale-out architectures for both storage and computation.
Hadoop has become a key building block in the new generation of scale-out systems. On the storage side, HDFS has provided a cost-effective and scalable substrate for storing large heterogeneous datasets. However, as key customer and systems touch points are instrumented to log data, and Internet of Things applications become common, data in the enterprise is growing at a staggering pace, and the need to leverage different storage tiers (ranging from tape to main memory) is posing new challenges, leading to caching technologies, such as Spark. On the analytics side, the emergence of resource managers such as YARN has opened the door for analytics tools to bypass the Map-Reduce layer and directly exploit shared system resources while computing close to data copies. This trend is especially significant for iterative computations such as graph analytics and machine learning, for which Map-Reduce is widely recognized to be a poor fit.
While Hadoop is widely recognized and used externally, Microsoft has long been at the forefront of Big Data analytics, with Cosmos and Scope supporting all internal customers. These internal services are a key part of our strategy going forward, and are enabling new state of the art external-facing services such as Azure Data Lake and more. I will examine these trends, and ground the talk by discussing the Microsoft Big Data stack.
The Hive Think Tank - Design Thinking by Bernie Roth, Professor at Stanford U...The Hive
Bernie Roth is a founder of Stanford's d.school and author of The Achievement Habit: how to stop wishing, start doing, and take command of life.
Bernie brings to the d.school a wealth of experience in teaching design, an intimate knowledge of the functioning of Stanford University, and a worldwide reputation as a researcher in kinematics and robotics. Together with Doug Wilde and the late Rolf Faste, Bernie developed the concept of a Creativity Workshop. This has been offered to students, faculty and professionals around the world. These same techniques have been made available to d.school students and are described in his book The Achievement Habit. He has found that these types of learning experiences enhance students’ ability to make meaningful positive difference in their own lives. He is especially pleased that his activities at the d.school have contributed to creating an environment where students and coworkers get the tools and values for realizing the enduring satisfactions that come from assisting others in the human community.
The Hive Think Tank: Machine Learning at Pinterest by Jure LeskovecThe Hive
Machine learning is at the core of Pinterest. Pinterest personalizes and ranks 1B+ pins, 700+ million boards for 100M+ users all over the world, using data gathered from collaborative filtering, user curation, web crawling, and more. At Pinterest we model relationships between pins, handle cold-start problems and deal with real-time recommendations.
In this presentation Jure gave an overview of the problems and effective solutions developed at Pinterest. He focused on systems and effective engineering choices made to enable productive machine learning development and enable multiple engineers effectively develop, test, and deploy machine-learned models.
The Hive Think Tank: Sidechains by Adam Back, President of BlockstreamThe Hive
Over the last couple of years, blockchains have captured a significant mindshare of innovation in financial services, industrial Internet and digital commerce industries. The scope of applications of blockchain as a platform has long surpassed that of its origins in Bitcoin as a cryptocurrency technology. However, none of the new blockchain platforms has been able to reach Bitcoin's levels of scale, security and global reach. There have also been no standards to interoperate between different blockchain platforms for exchange of assets. In order to address these challenges, Sidechains were created as cryptographic systems that securely orchestrate exchange of information between different blockchains by leveraging the scale & maturity of the Bitcoin network. Sidechains are weaving a network of diverse blockchains to bring interoperability and Bitcoin’s scale & maturity. In this talk, Adam Back will talk about its role in building the decentralized world of blockchains.
The Hive Think Tank: Ceph + RocksDB by Sage Weil, Red Hat.The Hive
Rocking the Database World with RocksDB
Sage Weil, Ceph Principal Architect, Red Hat
Sage helped design Ceph as part of his graduate research at the University of California, Santa Cruz. Since then, he has continued to refine the system with the goal of providing a stable next generation distributed storage system for Linux.
Specialties: Distributed system design, storage and file systems, management, software development.
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.