With the growing buzz around data science, many professionals want to learn how to become a data scientist—the role Harvard Business Review called the “sexiest job of the 21st century.” Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses.
Francesca and Jaya begin by outlining the typical skillset an exceptional data scientist needs. They then explore common applications of machine learning and artificial intelligence in different business verticals and explore why some companies are much more successful than others at driving analytics-based business transformation. Francesca and Jaya dive into a couple of specific use cases to demonstrate how machine learning and artificial intelligence can help drive business impact within an organization and how the right technology platform can boost employee productivity and help them innovate and iterate rapidly. You’ll learn why a modern cloud analytics environment that makes it easy to collect data, analyze, experiment, and quickly put things into production with a targeted set of customers is becoming a must-have for data-driven organizations and walk through a detailed use case, from how the data typically gets collected to data wrangling, building a model, tuning the model, and operationalizing the model for a business to use in their production environment.
Operationalize deep learning models for fraud detection with Azure Machine Le...Francesca Lazzeri, PhD
Recent advancements in computing technologies along with the increasing popularity of ecommerce platforms have radically amplified the risk of online fraud for financial services companies and their customers. Failing to properly recognize and prevent fraud results in billions of dollars of loss per year for the financial industry. This trend has urged companies to look into many popular artificial intelligence (AI) techniques, including deep learning for fraud detection. Deep learning can uncover patterns in tremendously large datasets and independently learn new concepts from raw data without extensive manual feature engineering. For this reason, deep learning has shown superior performance in domains such as object recognition and image classification.
Although, neural networks have been used for fraud detection for decades, recent advancements in computing technologies along with large volumes of data available today have dramatically improved the effectiveness of these techniques. Using a sample dataset that contains transactions made by credit cards in September 2013 by European cardholders, Francesca Lazzeri and Jaya Mathew explain how to build, deploy, and operationalize a deep learning model to identify and prevent fraud, using Azure Machine Learning Workbench to show the main steps in the operationalization process (from data ingestion to consumption) and the Keras deep learning library with Microsoft Cognitive Toolkit CNTK as the backend.
Credit card plays a very vital role in todays economy and the usage of credit cards has dramatically increased. Credit card has become one of the most common method of payment for both online and offline as well as for regular purchases of a common man. It is very necessary to distinguish fraudulent credit card transactions by the credit card organizations so their clients are not charged for the purchases that they didn’t make. Despite the fact that using credit card gives huge benefits when used responsibly carefully and however significant credit and financial damages could be caused by fraudulent activities as well. Numerous methods have been proposed to stop these fraudulent activities. The project illustrates the model of a dataset to predict fraud transactions using machine learning. The model then detects if it is a fraudulent or a genuine transaction. The model also analyses and pre processes the dataset along with deployment of multiple anomaly detection using algorithms such as Local forest outlier and Isolation forest. Nikitha Pradeep | Dr. A Rengarajan "Credit Card Fraud Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41289.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41289/credit-card-fraud-detection/nikitha-pradeep
Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how under sampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.
Online Payment Fraud Detection with Azure Machine LearningStefano Tempesta
Fraud detection is one of the earliest industrial applications of anomaly detection and machine learning. As part of the Azure Machine Learning offering, Microsoft provides a template that helps data scientists easily build and deploy an online transaction fraud detection solution. The template includes a collection of pre-configured machine learning modules, as well as custom R scripts, to enable an end-to-end solution.
This session presents best practices, design guidelines and a working implementation for building an online payment fraud detection mechanism in a SharePoint portal connected to a credit card payment gateway. The full source code of the solution is released as open source.
Operationalize deep learning models for fraud detection with Azure Machine Le...Francesca Lazzeri, PhD
Recent advancements in computing technologies along with the increasing popularity of ecommerce platforms have radically amplified the risk of online fraud for financial services companies and their customers. Failing to properly recognize and prevent fraud results in billions of dollars of loss per year for the financial industry. This trend has urged companies to look into many popular artificial intelligence (AI) techniques, including deep learning for fraud detection. Deep learning can uncover patterns in tremendously large datasets and independently learn new concepts from raw data without extensive manual feature engineering. For this reason, deep learning has shown superior performance in domains such as object recognition and image classification.
Although, neural networks have been used for fraud detection for decades, recent advancements in computing technologies along with large volumes of data available today have dramatically improved the effectiveness of these techniques. Using a sample dataset that contains transactions made by credit cards in September 2013 by European cardholders, Francesca Lazzeri and Jaya Mathew explain how to build, deploy, and operationalize a deep learning model to identify and prevent fraud, using Azure Machine Learning Workbench to show the main steps in the operationalization process (from data ingestion to consumption) and the Keras deep learning library with Microsoft Cognitive Toolkit CNTK as the backend.
Credit card plays a very vital role in todays economy and the usage of credit cards has dramatically increased. Credit card has become one of the most common method of payment for both online and offline as well as for regular purchases of a common man. It is very necessary to distinguish fraudulent credit card transactions by the credit card organizations so their clients are not charged for the purchases that they didn’t make. Despite the fact that using credit card gives huge benefits when used responsibly carefully and however significant credit and financial damages could be caused by fraudulent activities as well. Numerous methods have been proposed to stop these fraudulent activities. The project illustrates the model of a dataset to predict fraud transactions using machine learning. The model then detects if it is a fraudulent or a genuine transaction. The model also analyses and pre processes the dataset along with deployment of multiple anomaly detection using algorithms such as Local forest outlier and Isolation forest. Nikitha Pradeep | Dr. A Rengarajan "Credit Card Fraud Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41289.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41289/credit-card-fraud-detection/nikitha-pradeep
Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how under sampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.
Online Payment Fraud Detection with Azure Machine LearningStefano Tempesta
Fraud detection is one of the earliest industrial applications of anomaly detection and machine learning. As part of the Azure Machine Learning offering, Microsoft provides a template that helps data scientists easily build and deploy an online transaction fraud detection solution. The template includes a collection of pre-configured machine learning modules, as well as custom R scripts, to enable an end-to-end solution.
This session presents best practices, design guidelines and a working implementation for building an online payment fraud detection mechanism in a SharePoint portal connected to a credit card payment gateway. The full source code of the solution is released as open source.
A Study on Credit Card Fraud Detection using Machine Learningijtsrd
Due to the high level of growth in each number of transactions done using credit card has led to high rise in fraudulent activities. Fraud is one of the major issues related to credit card business, since each individual do more of offline or online purchase of product via internet there is need to developed a secured approach of detecting if the credit card been used is a fraudulent transaction or not. Pattern involves in the fraud detection has to be re analyze to change from reactive approach to a proactive approach. In this paper, our objectives are to detect at least 95 of fraudulent activities using machine learning to deployed anomaly detection system such as logistic regression, k nearest neighbor and support vector machine algorithm. Ajayi Kemi Patience | Dr. Lakshmi J. V. N "A Study on Credit Card Fraud Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30688.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30688/a-study-on-credit-card-fraud-detection-using-machine-learning/ajayi-kemi-patience
Folks, recently I was invited by re-work to be a speaker at the Deep Learning in Finance Summit held in Singapore. First of all, I wanted to thank the folks @ rework for organizing this fantastic event and inviting many talented speakers from the industry and academia. The entire 2 days agenda was a great platform to learn more about the latest happening in this area.
Regarding my presentation- The topic was “ Deep Learning & Fraud Detection in Fintech Lending”. Some of the key points that were covered during this presentation are-
Types of fintech
Key drivers for fraud in fintech lending
Common fraud modus operandi ( MOs) in fintech lending
Why deep learning for fraud detection
Sample deep learning application areas in fraud detection-
Anomaly detection using Autoencoder/ Replicator Neural Network
Social network analysis ( SNA)
Demo of Multi Layer Perceptron ( MLP) deep learning classifier built using Python, Tensorflow and Keras along with vital statistical parameters such as accuracy, logloss, precision, recall, fscore etc.
I am attaching the full presentation here. Do share your thoughts…
Happy reading.
Cheers!
-RP
This is a talk given to bankers at CCX Forum where I share how Machine Learning products can be built for retail banking sector, what are the challenges and how can they be overcome.
Artificial Intelligence: a driver of innovation in the Banking Sector - The Italian case
Marco Rotoloni (Head of the research team on banking operations, ABI Lab)
Consumers are looking for more than just banking and machine learning helps banks deliver that.
Machine learning contributes to areas such as credit decisions, risk management, personalized customer experiences, fraud detection, automation and much more.
This PDF will address the following points:
1. An overview of the banking sector and its importance in the economy
2. The top 5 banks in the US benefiting from the power of machine learning
3. The areas in banking where Machine Learning is applied
This second machine age has seen the rise of artificial intelligence (AI), or “intelligence” that is not the result of
human cogitation. It is now ubiquitous in many commercial products, from search engines to virtual assistants. aI is the result of exponential growth in computing power, memory capacity, cloud computing, distributed and parallel processing, open-source solutions, and global connectivity of both people
and machines. The massive amounts and the speed at which structured and unstructured (e.g., text, audio, video, sensor) data is being generated has made a necessity of speedily processing and generating meaningful, actionable insights from it.
Fraud detection is a popular application of Machine Learning. But is not that obvious and not that common as it seems. I'll tell how QuantUp implemented it for WARTA insurance company (a subsidiary of Talanx International AG).
The models developed gave between 10% and 30% of reduction of losses. The project was not a simple one because of the complex process of handling claims and using really rich dataset. The tools applied were R (modeling) and DataWalk (data peparation). You will learn what is important in development of such solutions in general, what was difficult in this particular project, and how to overcome possible difficulties in similar projects.
Ai - Artificial Intelligence predictions-2018-report - PWCRick Bouter
Here’s some actionable advice on artificial intelligence (AI), that you can
use today: If someone says they know exactly what AI will look like and
do in 10 years, smile politely, then change the subject or walk away.
Future of artificial intelligence in the banking sectorusmsystems
The banking sector is becoming an active adapter of artificial intelligence — exploring and implementing this technology in new ways. The penetration of artificial intelligence in the banking sector had been unnoticed and sluggish until the advent of the era of internet banking.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Fraud Analytics with Machine Learning and Big Data Engineering for TelecomSudarson Roy Pratihar
Presentation of a successful project executed on telecom fraud analytics @ 3rd International conference for businees analytics and intelligence, Indian Institute of Management Bangalore
A Study on Credit Card Fraud Detection using Machine Learningijtsrd
Due to the high level of growth in each number of transactions done using credit card has led to high rise in fraudulent activities. Fraud is one of the major issues related to credit card business, since each individual do more of offline or online purchase of product via internet there is need to developed a secured approach of detecting if the credit card been used is a fraudulent transaction or not. Pattern involves in the fraud detection has to be re analyze to change from reactive approach to a proactive approach. In this paper, our objectives are to detect at least 95 of fraudulent activities using machine learning to deployed anomaly detection system such as logistic regression, k nearest neighbor and support vector machine algorithm. Ajayi Kemi Patience | Dr. Lakshmi J. V. N "A Study on Credit Card Fraud Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30688.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30688/a-study-on-credit-card-fraud-detection-using-machine-learning/ajayi-kemi-patience
Folks, recently I was invited by re-work to be a speaker at the Deep Learning in Finance Summit held in Singapore. First of all, I wanted to thank the folks @ rework for organizing this fantastic event and inviting many talented speakers from the industry and academia. The entire 2 days agenda was a great platform to learn more about the latest happening in this area.
Regarding my presentation- The topic was “ Deep Learning & Fraud Detection in Fintech Lending”. Some of the key points that were covered during this presentation are-
Types of fintech
Key drivers for fraud in fintech lending
Common fraud modus operandi ( MOs) in fintech lending
Why deep learning for fraud detection
Sample deep learning application areas in fraud detection-
Anomaly detection using Autoencoder/ Replicator Neural Network
Social network analysis ( SNA)
Demo of Multi Layer Perceptron ( MLP) deep learning classifier built using Python, Tensorflow and Keras along with vital statistical parameters such as accuracy, logloss, precision, recall, fscore etc.
I am attaching the full presentation here. Do share your thoughts…
Happy reading.
Cheers!
-RP
This is a talk given to bankers at CCX Forum where I share how Machine Learning products can be built for retail banking sector, what are the challenges and how can they be overcome.
Artificial Intelligence: a driver of innovation in the Banking Sector - The Italian case
Marco Rotoloni (Head of the research team on banking operations, ABI Lab)
Consumers are looking for more than just banking and machine learning helps banks deliver that.
Machine learning contributes to areas such as credit decisions, risk management, personalized customer experiences, fraud detection, automation and much more.
This PDF will address the following points:
1. An overview of the banking sector and its importance in the economy
2. The top 5 banks in the US benefiting from the power of machine learning
3. The areas in banking where Machine Learning is applied
This second machine age has seen the rise of artificial intelligence (AI), or “intelligence” that is not the result of
human cogitation. It is now ubiquitous in many commercial products, from search engines to virtual assistants. aI is the result of exponential growth in computing power, memory capacity, cloud computing, distributed and parallel processing, open-source solutions, and global connectivity of both people
and machines. The massive amounts and the speed at which structured and unstructured (e.g., text, audio, video, sensor) data is being generated has made a necessity of speedily processing and generating meaningful, actionable insights from it.
Fraud detection is a popular application of Machine Learning. But is not that obvious and not that common as it seems. I'll tell how QuantUp implemented it for WARTA insurance company (a subsidiary of Talanx International AG).
The models developed gave between 10% and 30% of reduction of losses. The project was not a simple one because of the complex process of handling claims and using really rich dataset. The tools applied were R (modeling) and DataWalk (data peparation). You will learn what is important in development of such solutions in general, what was difficult in this particular project, and how to overcome possible difficulties in similar projects.
Ai - Artificial Intelligence predictions-2018-report - PWCRick Bouter
Here’s some actionable advice on artificial intelligence (AI), that you can
use today: If someone says they know exactly what AI will look like and
do in 10 years, smile politely, then change the subject or walk away.
Future of artificial intelligence in the banking sectorusmsystems
The banking sector is becoming an active adapter of artificial intelligence — exploring and implementing this technology in new ways. The penetration of artificial intelligence in the banking sector had been unnoticed and sluggish until the advent of the era of internet banking.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Fraud Analytics with Machine Learning and Big Data Engineering for TelecomSudarson Roy Pratihar
Presentation of a successful project executed on telecom fraud analytics @ 3rd International conference for businees analytics and intelligence, Indian Institute of Management Bangalore
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
What is Data analytics? How is data analytics a better career option?Aspire Techsoft Academy
Are you looking for the Best Data analytics Training Institute in Pune Aspire Techsoft offers you the best SAS Data Analytics Certification Training in Pune with Certified expert faculties.
Mixed Methods Research in the Age of Big Data: A Primer for UX ResearchersUXPA International
What does UX research entail in what some are calling the “Age of Data Science?” Most would agree that some level of collaboration is needed -- Data Science results feeding UX Research and vice versa -- but can this be more meaningful than simply attending each other’s readouts?
In this session, you’ll hear some practical, approachable tips for qualitative UX Researchers to play a larger role in Big Data discussions. Stats expertise not required! These tips will help you break through the lexicon barriers between UX Research and Data Science, and provide a framework for collaboration that can lead to even more impactful research.
UXPA 2016: Mixed Methods Research in the Age of Big DataZachary Sam Zaiss
UX professionals have a long history of blending quantitative and qualitative research to better understand the customer experience. As Data Science has emerged as a discipline (with an increasing amount of hype), it's all too easy to engage only during results time, sharing information but working independently. At UXPA 2016, I made the case for deeper collaboration between UX professionals and Data Scientists during research and analysis time, for the sake of better Design outcomes for all.
Data Analytics has become a crucial part of the IT industry, as businesses strive to extract meaningful insights from the massive amounts of data they generate. APTRON's Data Analytics Training in Gurgaon is designed to equip learners with the knowledge and skills required to become proficient in the field.
Data Science has become one of the most demanded jobs of the 21st century. It has become a buzzword that almost everyone talks about these days. But what is Data Science? In this article, we will demystify Data Science, the role of a Data Scientist and have a look at the tools required to master Data Science.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
Though the term machine learning has become very visible in
the popular press over the past few years—making it appear to be the newest shiny object—the technology has actually been
in use for decades. In fact, machine learning algorithms such as decision trees are already in use by many organizations for predictive analytics.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Twitter Sentiment Analysis in 10 Minutes using Machine LearningSkyl.ai
About the webinar:
Social media is one of the richest sources of data for brands. According to Domo's 'Data never sleeps' report, every single minute 456,000 tweets are posted on Twitter, 46,740 photos are uploaded on Instagram and 510,000 comments & 293,000 statuses are updated on Facebook.
This data contains valuable information like product feedback or reviews and information that can be used to better understand users or find valuable insights. However, traditional ways struggle to analyze the unstructured data and this is where sentiment analysis using machine learning comes to the rescue!, Machine learning can help to understand the text and extract the sentiment using Natural Language Processing. Sentiment analysis can be applied in a range of business applications like - social media channel analysis, 360-degree customer insights, user reviews, competitive analysis, and many more.
What you will learn
- How businesses are leveraging sentiment analysis to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: How to build a twitter sentiment analysis model
Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor flow, IBM watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science role. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python,Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Similar to A day in the life of a data scientist in an AI company (20)
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
3. What is AI and Why is it so important?
@frlazzeri @mathew_jaya@frlazzeri @mathew_jaya
4. Computer Vision Audio Processing
Natural Language
Processing
Knowledge
Representation
Machine Learning Expert Systems
AI
Technologies
Illustrative
Solutions
…
Virtual
Agents
Identity
Analytics
Cognitive
Robotics
Speech
Analytics
Recommendation
Systems
Data
Visualization
Emerging AI technologies
Computer vision and audio processing, for
example are able to actively perceive the world
around them by acquiring and processing images,
sounds and speech. The use of facial recognition
at border control kiosks is one practical example of
how it can improve productivity.
Sense
Natural language processing and inference
engines can enable AI systems to analyse and
understand the information collected. This
technology is used to power the language
translation feature of search engine results
Comprehend
An AI system can take action through technologies
such as expert systems and inference engines or
undertake actions in the physical world. Auto-pilot
features and assisted-braking capabilities in cars
are examples of this
Act
What is AI? – To sense, comprehend and act
@frlazzeri @mathew_jaya@frlazzeri @mathew_jaya
Accenture: Why artificial intelligence is the future of growth, April 2016
9. Data is
connected
Data is
accurate
A lot of
data
Example: Predict
whether
component X will
fail in the next Y
days
Example: Identifiers
at the level you are
predicting, relevant
data collected &
feature engineering
using domain
knowledge
Example: Will be
difficult to predict
failure accurately
with few examples
Example: Failures are
really failures, human
labels on root causes
Example: Machine
information
linkable to usage
information
@frlazzeri @mathew_jaya@frlazzeri @mathew_jaya
10. Business
scenario
Key
decision
Data Science
question
Energy forecasting Should I buy or sell
energy contracts?
What will be the long/short-term
demand for energy in a region?
Customer churn Which customers should I
prioritize to reduce churn?
What is probability of churn within
X days for each customer?
Personalized marketing What product should I offer first? What is the probability
that customer will purchase
each product?
Product feedback Which service/product
needs attention?
What is social media sentiment
for each service/product?
@frlazzeri @mathew_jaya@frlazzeri @mathew_jaya
11. Defining Performance Metrics
@frlazzeri @mathew_jaya@frlazzeri @mathew_jaya
Correlation
with the Data
Science
Metric
Establish a
Baseline
Quantify the
Metric Value
Improvement
Translate into a
Quantifiable
Business
Metric
Establish a
Qualitative
Objective
Example: Reduce
user churn
Example: Reduce the
fraction of users with
4-week inactivity
Example: Statistically
significant A/B test is
a clean way. If this is
difficult, compare the
values of the metric
before and after the
solution
Example: Reduce
the fraction of
users with 4-week
inactivity by 20%
Example: Current
fraction of users
with 4-week
inactivity = 60%
12. Understanding the ML workflow &
the Team Data Science Process
@frlazzeri @mathew_jaya@frlazzeri @mathew_jaya
17. Sophisticated pretrained models
To simplify solution development
Azure
Databricks
Machine Learning
VMs
Popular frameworks
To build advanced deep learning solutions TensorFlow KerasPytorch Onnx
Azure
Machine Learning
LanguageSpeech
…
Azure
Search
Vision
On-premises Cloud Edge
Productive services
To empower data science and development teams
Powerful infrastructure
To accelerate deep learning
Flexible deployment
To deploy and manage models on intelligent cloud and edge
Cognitive Services
@frlazzeri @mathew_jaya
