This document contains a presentation about operationalizing machine learning. It begins with copyright information and a disclaimer about forward-looking statements. Next, it introduces the presenter Kelly Feagans and provides background on machine learning concepts such as the different types of machine learning. The presentation then discusses use cases for machine learning in IT operations, security, and business analytics. It describes the machine learning process and how Splunk can be used for machine learning. Finally, it promotes an upcoming Splunk conference and machine learning app.
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
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Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Big Data presence in the high volume in the data storages can help in various ways to learn more about the need and trends of the current market which will be useful for all type of organizations. Modern information technology used to analyze the relationship between social trends and market insights is a useful way to have indirectly interlinked to customers and their interests from unstructured and semi-structured data. Such analysis will give organizations a broader view towards the practical needs of customers and once banking industry or any industry could know the customers, they can serve better and with more flexibility. In this presentation, team has primarily created the platform and designed the architecture in big data technology for banking industry to maximize the users of credit card.
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Big Data presence in the high volume in the data storages can help in various ways to learn more about the need and trends of the current market which will be useful for all type of organizations. Modern information technology used to analyze the relationship between social trends and market insights is a useful way to have indirectly interlinked to customers and their interests from unstructured and semi-structured data. Such analysis will give organizations a broader view towards the practical needs of customers and once banking industry or any industry could know the customers, they can serve better and with more flexibility. In this presentation, team has primarily created the platform and designed the architecture in big data technology for banking industry to maximize the users of credit card.
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Data Science Tutorial | What is Data Science? | Data Science For Beginners | ...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
In this PPT on Data Science Tutorial, you’ll get an in-depth understanding of Data Science and you’ll also learn how it is used in the real world to solve data-driven problems. It’ll cover the following topics in this session:
Need for Data Science
Walmart Use case
What is Data Science?
Who is a Data Scientist?
Data Science – Skill set
Data Science Job roles
Data Life cycle
Introduction to Machine Learning
K- Means Use case
K- Means Algorithm
Hands-On
Data Science certification
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
Through a comprehensive exploration, this talk would intend to uncover the inner workings of GANs and demystify their training process. This talk shall help you gain insights into the different types of GANs, such as conditional GANs and style-based GANs, and how they contribute to the advancement of generative AI. To truly appreciate the significance of GANs, this talk will also discuss their wide-ranging industrial applications, spanning image synthesis, video generation, data augmentation, and virtual reality.
Data Science Training | Data Science Tutorial | Data Science Certification | ...Edureka!
This Edureka Data Science Training will help you understand what is Data Science and you will learn about different Data Science components and concepts. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
6. Introduction to Machine Learning using R
7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
10. Deep Learning
To take a structured training on Data Science, you can check complete details of our Data Science Certification Training course here: https://goo.gl/OCfxP2
Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science for Beginners" PPT talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This PPT talks about the most crucial aspects of data science and covers the following topics:
Why Data Science?
What is Data Science?
Who is a Data Scientist?
What does a Data Scientist do?
How to solve a problem in Data Science?
Data Science Tools
Demo
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete YouTube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
Data Analytics PowerPoint Presentation SlidesSlideTeam
This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Analytics PowerPoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
Artificial intelligent systems in finance have exploded over the last few years. Many institutions are struggling to leverage these new AI systems and machine learning approaches to risk management. This is particularly true for applications to risk models that are subject to regulatory scrutiny where transparency limits applications of these new approaches. Co-sponsored with PRMIA (Professional Risk Managers’ International Association), this session will provide an overview of the current state of applied machine learning and artificial intelligence for risk modeling and how it can be applied for monitoring risk and building new risk models.
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Data Science Tutorial | What is Data Science? | Data Science For Beginners | ...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
In this PPT on Data Science Tutorial, you’ll get an in-depth understanding of Data Science and you’ll also learn how it is used in the real world to solve data-driven problems. It’ll cover the following topics in this session:
Need for Data Science
Walmart Use case
What is Data Science?
Who is a Data Scientist?
Data Science – Skill set
Data Science Job roles
Data Life cycle
Introduction to Machine Learning
K- Means Use case
K- Means Algorithm
Hands-On
Data Science certification
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
Through a comprehensive exploration, this talk would intend to uncover the inner workings of GANs and demystify their training process. This talk shall help you gain insights into the different types of GANs, such as conditional GANs and style-based GANs, and how they contribute to the advancement of generative AI. To truly appreciate the significance of GANs, this talk will also discuss their wide-ranging industrial applications, spanning image synthesis, video generation, data augmentation, and virtual reality.
Data Science Training | Data Science Tutorial | Data Science Certification | ...Edureka!
This Edureka Data Science Training will help you understand what is Data Science and you will learn about different Data Science components and concepts. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
6. Introduction to Machine Learning using R
7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
10. Deep Learning
To take a structured training on Data Science, you can check complete details of our Data Science Certification Training course here: https://goo.gl/OCfxP2
Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science for Beginners" PPT talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This PPT talks about the most crucial aspects of data science and covers the following topics:
Why Data Science?
What is Data Science?
Who is a Data Scientist?
What does a Data Scientist do?
How to solve a problem in Data Science?
Data Science Tools
Demo
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete YouTube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
Data Analytics PowerPoint Presentation SlidesSlideTeam
This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Analytics PowerPoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
Artificial intelligent systems in finance have exploded over the last few years. Many institutions are struggling to leverage these new AI systems and machine learning approaches to risk management. This is particularly true for applications to risk models that are subject to regulatory scrutiny where transparency limits applications of these new approaches. Co-sponsored with PRMIA (Professional Risk Managers’ International Association), this session will provide an overview of the current state of applied machine learning and artificial intelligence for risk modeling and how it can be applied for monitoring risk and building new risk models.
What is Splunk? At the end of this session you’ll have a high-level understanding of the pieces that make up the Splunk Platform, how it works, and how it fits in the landscape of Big Data. You’ll see practical examples that differentiate Splunk while demonstrating how to gain quick time to value.
SplunkLive Brisbane Splunk for Operational Security IntelligenceGabrielle Knowles
You have spent a ton of money on your security infrastructure. But how do you string all those things together so you can achieve your goals of reducing time to response, detecting, preventing threats. And most importantly, having your security team serve your business and mission. Learn how to organize your security resources to get the best benefit. See a live demonstration of operationalizing those resources so your security teams can do more for your organization.
SplunkLive Brisbane Splunk for Operational Security IntelligenceSplunk
You have spent a ton of money on your security infrastructure. But how do you string all those things together so you can achieve your goals of reducing time to response, detecting, preventing threats. And most importantly, having your security team serve your business and mission. Learn how to organize your security resources to get the best benefit. See a live demonstration of operationalizing those resources so your security teams can do more for your organization.
Splunk is a powerful platform for understanding your data. The preview of the Machine Learning Toolkit and Showcase App extends Splunk with a rich suite of advanced analytics and machine learning algorithms, which are exposed via an API and demonstrated in a showcase. In this session, we'll present an overview of the app architecture and API and then show you how to use Splunk to easily perform a wide variety of tasks, including outlier detection, predictive analytics, event clustering, and anomaly detection. We’ll use real data to explore these techniques and explain the intuition behind the analytics.
Splunk is a powerful platform for understanding your data. The preview of the Machine Learning Toolkit and Showcase App extends Splunk with a rich suite of advanced analytics and machine learning algorithms, which are exposed via an API and demonstrated in a showcase. In this session, we'll present an overview of the app architecture and API and then show you how to use Splunk to easily perform a wide variety of tasks, including outlier detection, predictive analytics, event clustering, and anomaly detection. We’ll use real data to explore these techniques and explain the intuition behind the analytics.
Splunk is a powerful platform for understanding your data. The preview of the Machine Learning Toolkit and Showcase App extends Splunk with a rich suite of advanced analytics and machine learning algorithms, which are exposed via an API and demonstrated in a showcase. In this session, we'll present an overview of the app architecture and API and then show you how to use Splunk to easily perform a wide variety of tasks, including outlier detection, predictive analytics, event clustering, and anomaly detection. We’ll use real data to explore these techniques and explain the intuition behind the analytics.
SplunkLive Sydney Machine Learning & AnalyticsSplunk
Splunk is a powerful platform for understanding your data. The preview of the Machine Learning Toolkit and Showcase App extends Splunk with a rich suite of advanced analytics and machine learning algorithms, which are exposed via an API and demonstrated in a showcase. In this session, we'll present an overview of the app architecture and API and then show you how to use Splunk to easily perform a wide variety of tasks, including outlier detection, predictive analytics, event clustering, and anomaly detection. We’ll use real data to explore these techniques and explain the intuition behind the analytics.
Splunk is a powerful platform for understanding your data. The preview of the Machine Learning Toolkit and Showcase App extends Splunk with a rich suite of advanced analytics and machine learning algorithms, which are exposed via an API and demonstrated in a showcase. In this session, we'll present an overview of the app architecture and API and then show you how to use Splunk to easily perform a wide variety of tasks, including outlier detection, predictive analytics, event clustering, and anomaly detection. We’ll use real data to explore these techniques and explain the intuition behind the analytics.
Splunk is a powerful platform for understanding your data. The preview of the Machine Learning Toolkit and Showcase App extends Splunk with a rich suite of advanced analytics and machine learning algorithms, which are exposed via an API and demonstrated in a showcase. In this session, we'll present an overview of the app architecture and API and then show you how to use Splunk to easily perform a wide variety of tasks, including outlier detection, predictive analytics, event clustering, and anomaly detection. We’ll use real data to explore these techniques and explain the intuition behind the analytics.
Splunk is a powerful platform for understanding your data. The preview of the Machine Learning Toolkit and Showcase App extends Splunk with a rich suite of advanced analytics and machine learning algorithms, which are exposed via an API and demonstrated in a showcase. In this session, we'll present an overview of the app architecture and API and then show you how to use Splunk to easily perform a wide variety of tasks, including outlier detection, predictive analytics, event clustering, and anomaly detection. We’ll use real data to explore these techniques and explain the intuition behind the analytics.
Splunk is a powerful platform for understanding your data. The preview of the Machine Learning Toolkit and Showcase App extends Splunk with a rich suite of advanced analytics and machine learning algorithms, which are exposed via an API and demonstrated in a showcase. In this session, we'll present an overview of the app architecture and API and then show you how to use Splunk to easily perform a wide variety of tasks, including outlier detection, predictive analytics, event clustering, and anomaly detection. We’ll use real data to explore these techniques and explain the intuition behind the analytics.
Deception-Triggered Security Data Science to Detect Adversary MovementsSatnam Singh
Deception-triggered security data science is a novel paradigm where we marry deception and security data science together to enhance security incident analysis. We deploy deception sensors (consisting of multiple low and high honeypots) that can emulate at various stages, e.g., network, endpoint, application, data, and servers. Whenever an adversary tripped over these sensors, we initiate an alert and correlate with data from other hosts and users to construct an adversary trajectory graph. The graph enhances the capabilities of security analysts to get better insights about adversary movements and hosts that may also be compromised. This talk is a must for security data researchers, security analysts, and security practitioners. We will share some case studies from our deployments along with a live demo of our Splunk App.
Splunk is a powerful platform for understanding your data. The preview of the Machine Learning Toolkit and Showcase App extends Splunk with a rich suite of advanced analytics and machine learning algorithms, which are exposed via an API and demonstrated in a showcase. In this session, we'll present an overview of the app architecture and API and then show you how to use Splunk to easily perform a wide variety of tasks, including outlier detection, predictive analytics, event clustering, and anomaly detection. We’ll use real data to explore these techniques and explain the intuition behind the analytics.
.conf Go 2023 - Das passende Rezept für die digitale (Security) Revolution zu...Splunk
.conf Go 2023 presentation:
"Das passende Rezept für die digitale (Security) Revolution zur Telematik Infrastruktur 2.0 im Gesundheitswesen?"
Speaker: Stefan Stein -
Teamleiter CERT | gematik GmbH M.Eng. IT-Sicherheit & Forensik,
doctorate student at TH Brandenburg & Universität Dresden
.conf Go 2023 presentation:
De NOC a CSIRT
Speakers:
Daniel Reina - Country Head of Security Cellnex (España) & Global SOC Manager Cellnex
Samuel Noval - Global CSIRT Team Leader, Cellnex
Splunk - BMW connects business and IT with data driven operations SRE and O11ySplunk
BMW is defining the next level of mobility - digital interactions and technology are the backbone to continued success with its customers. Discover how an IT team is tackling the journey of business transformation at scale whilst maintaining (and showing the importance of) business and IT service availability. Learn how BMW introduced frameworks to connect business and IT, using real-time data to mitigate customer impact, as Michael and Mark share their experience in building operations for a resilient future.
Data foundations building success, at city scale – Imperial College LondonSplunk
Universities have more in common with modern cities than traditional places of learning. This mini city needs to empower its citizens to thrive and achieve their ambitions. Operationalising data is key to building critical services; from understanding complex IT estates for smarter decision-making to robust security and a more reliable, resilient student experience. Juan will share his experience in building data foundations for a resilient future whilst enabling digital transformation at Imperial College London.
Splunk: How Vodafone established Operational Analytics in a Hybrid Environmen...Splunk
Learn how Vodafone has provided end-to-end visibility across services by building an Operational Analytics Platform. In this session, you will hear how Stefan and his team manage legacy, on premise, hybrid and public cloud services, and how they are providing a platform for complex triage and debugging to tackle use cases across Vodafone’s extensive ecosystem.
.italo operates an Essential Service by connecting more than 100 million people annually across Italy with its super fast and secure railway. And CISO Enrico Maresca has been on a whirlwind journey of his own.
Formerly a Cyber Security Engineer, Enrico started at .italo as an IT Security Manager. One year later, he was promoted to CISO and tasked with building out – and significantly increasing the maturity level – of the SOC. The result was a huge step forward for .italo.
So how did he successfully achieve this ambitious ask? Join Enrico as he reveals the key insights and lessons learned in his SOC journey, including:
Top challenges faced in improving security posture
Key KPIs implemented in order to measure success
Strategies and approaches applied in the SOC
How MITRE ATT&CK and Splunk Enterprise Security were utilised
Next steps in their maturity journey ahead
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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.
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.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
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.
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3. Get the ML Toolkit & Showcase App
• Get the App! https://splunkbase.splunk.com/app/2890
• Leverages Python for Scientific Computing (PSC) add-on:
– Open-source Python data science ecosystem
– NumPy, SciPy, scitkit-learn, pandas, statsmodels
• Showcase use cases: Hard Drive Failure, Server Power consumption,
Server Response Time, Application Usage
• Standard algorithms out of the box:
– Supervised: Logistic Regression, SVM, Linear Regression, Random Forest
– Unsupervised: KMeans, DBSCAN, Spectral Clustering
• Implement one of 300+ algorithms by editing Python scripts
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4. Fit, Apply & Validate Models
• Machine Learning SPL – New grammar for doing ML in Splunk
• fit – fit models based on training data
– [training data] | fit LinearRegression costly_KPI
from feature1 feature2 feature3 into my_model
• apply – apply models on testing and production data
– [testing/production data] | apply my_model
• Validate Your Model (The Hard Part)
– Why hard? Because statistics is hard! Also: model error ≠ real world risk.
– Analyze residuals, mean-square error, goodness of fit, cross-validate, etc.
– Take Splunk’s Analytics & Data Science Education course
22. 22
5. Operationalize Your Models
• Remember the ML Process:
1. Get data
2. Explore data & fit models
3. Apply & validate models
4. Forecast KPIs
5. Surface incidents to Ops team
• Then operationalize: feed back Ops analysis to data inputs, repeat
• Be patient, this takes time, but lots of value will come out.
Operationalize
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SEPT 26-29, 2016
WALT DISNEY WORLD, ORLANDO
SWAN AND DOLPHIN RESORTS
• 5000+ IT & Business Professionals
• 3 days of technical content
• 165+ sessions
• 80+ Customer Speakers
• 35+ Apps in Splunk Apps Showcase
• 75+ Technology Partners
• 1:1 networking: Ask The Experts and Security
Experts, Birds of a Feather and Chalk Talks
• NEW hands-on labs!
• Expanded show floor, Dashboards Control
Room & Clinic, and MORE!
The 7th Annual Splunk Worldwide Users’ Conference
PLUS Splunk University
• Three days: Sept 24-26, 2016
• Get Splunk Certified for FREE!
• Get CPE credits for CISSP, CAP, SSCP
• Save thousands on Splunk education!
#splunkconf2016