What if your team's collective knowledge could flow as freely as conversation, but never get lost? We know how often knowledge silos, information fragmentation, and finding what you need can slow teams down. We'll be talking about how to make Confluence Cloud a more centralised, accessible knowledge hub so that your teams can work better together.

Présentation de Hacene sur l'utilisation de Jira demande métier sur le plan national GRDF.

Data governance can contribute local optimizations to a company's value chain, such as better data discovery via a data catalog, or quality-monitored and cleansed data sets. From a 30,000 ft data strategy view, it is even more desirable to connect the dots for business objects frequently reused among business processes and make them available as governed, quality-controlled, easily accessible data products. The speaker successfully launched a Data Governance program in a company traditionally ranking metal higher than data and will share experiences on the ongoing data product journey.

This presentation explores deploying retrieval augmented generation (RAG) on Vertex AI Search to enhance QAD's internal data search (Jira, Confluence, Google Sites). Discover how GenAI improves query responses, utilizing a user-friendly web app on Google App Engine to counteract the loss of institutional knowledge. Join us for insights into this innovative enterprise search solution. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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Delve into the intricate confluence of AI-driven search, software development, and online education and reflect upon its implications for developers, educators, and tech enthusiasts.

ABOUT THE TALK: Quarto is a multi-language, open-source toolkit for creating data-driven websites, reports, presentations, and scientific articles, built on Jupyter.

This talk teaches you how to use Quarto to publish Jupyter notebooks as production quality websites, books, blogs, presentations, PDFs, Office documents, and more. It covers how to publish notebooks within existing content management systems like Hugo, Docusaurus, and Confluence and also explore how Quarto works under the hood along with how the system can be extended to accommodate unique requirements and workflows.

ABOUT THE SPEAKER: J.J. Allaire is the founder of RStudio and the creator of the RStudio IDE. He is an author of several packages in the R Markdown publishing ecosystem and has also worked extensively on the R interfaces to Python and TensorFlow. J.J. is now leading the Quarto project, which is a new Jupyter-based scientific and technical publishing system.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

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One of the biggest challenges facing the adoption of machine learning and AI in Data Science is understanding, interpreting, and explaining models and their outcomes to produce higher certainty, accountability, and fairness.

Serg Masis is a Climate & Agronomic Data Scientist at Syngenta and the author of the book, Interpretable Machine Learning with Python. For the last two decades, Serg has been at the confluence of the internet, application development, and analytics. Serg is a true polymath. Before his current role, he co-founded a search engine startup incubated by Harvard Innovation Labs, was the proud owner of a Bubble Tea shop, and more.

Throughout the episode, Serg spoke about the different challenges affecting model interpretability in machine learning, how bias can produce harmful outcomes in machine learning systems, the different types of technical and non-technical solutions to tackling bias, the future of machine learning interpretability, and much more.

As a head of product on the Confluence team at Atlassian, I own the metrics associated with user happiness. This a common area of ownership for heads of product, GMs, CEOs. But how do you actually use data to move the needle on user happiness, and how do you convert user activity and engagement insights into clear actions that end up positively impacting user happiness? In this talk, I would like to share the approach we developed jointly with our data analytics team to understand, operationalize and report on our journey on make Confluence users happier. This talk will be useful for data analytics and data science practitioners, product executives, and anyone faced with a task of operationalizing improvement of a "fuzzy" metric like NPS or CSAT.

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Analyze vast amounts of data in record time using Apache Spark with Databricks in the Cloud. Learn the fundamentals, and more, of running analytics on large clusters in Azure and AWS, using Apache Spark with Databricks on top. Discover how to squeeze the most value out of your data at a mere fraction of what classical analytics solutions cost, while at the same time getting the results you need, incrementally faster. This book explains how the confluence of these pivotal technologies gives you enormous power, and cheaply, when it comes to huge datasets. You will begin by learning how cloud infrastructure makes it possible to scale your code to large amounts of processing units, without having to pay for the machinery in advance. From there you will learn how Apache Spark, an open source framework, can enable all those CPUs for data analytics use. Finally, you will see how services such as Databricks provide the power of Apache Spark, without you having to know anything aboutconfiguring hardware or software. By removing the need for expensive experts and hardware, your resources can instead be allocated to actually finding business value in the data. This book guides you through some advanced topics such as analytics in the cloud, data lakes, data ingestion, architecture, machine learning, and tools, including Apache Spark, Apache Hadoop, Apache Hive, Python, and SQL. Valuable exercises help reinforce what you have learned. What You Will Learn Discover the value of big data analytics that leverage the power of the cloud Get started with Databricks using SQL and Python in either Microsoft Azure or AWS Understand the underlying technology, and how the cloud and Apache Spark fit into the bigger picture See how these tools are used in the real world Run basic analytics, including machine learning, on billions of rows at a fraction of a cost or free Who This Book Is For Data engineers, data scientists, and cloud architects who want or need to run advanced analytics in the cloud. It is assumed that the reader has data experience, but perhaps minimal exposure to Apache Spark and Azure Databricks. The book is also recommended for people who want to get started in the analytics field, as it provides a strong foundation.

Written by a team of experts at the forefront of the cyber-physical systems (CPS) revolution, this book provides an in-depth look at security and privacy, two of the most critical challenges facing both the CPS research and development community and ICT professionals. It explores, in depth, the key technical, social, and legal issues at stake, and it provides readers with the information they need to advance research and development in this exciting area. Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon the seamless integration of computational algorithms and physical components. Advances in CPS will enable capability, adaptability, scalability, resiliency, safety, security, and usability far in excess of what today’s simple embedded systems can provide. Just as the Internet revolutionized the way we interact with information, CPS technology has already begun to transform the way people interact with engineered systems. In the years ahead, smart CPS will drive innovation and competition across industry sectors, from agriculture, energy, and transportation, to architecture, healthcare, and manufacturing. A priceless source of practical information and inspiration, Security and Privacy in Cyber-Physical Systems: Foundations, Principles and Applications is certain to have a profound impact on ongoing R&D and education at the confluence of security, privacy, and CPS.

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today's big data world. The author demonstrates how to leverage a company's existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will "learn data mining by doing data mining". By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website with further resources for all readers, and Powerpoint slides, a solutions manual, and suggested projects for instructors who adopt the book