'Seeing Meaning in the age of Big Data'
Probabilistic modeling and machine learning touches every industry. Extracting meaning from data allows better user interaction, finds patterns that would otherwise be obscured using traditional BI reporting, and leads to defensible decision making.
How can b2 b companies use ai to support their sales venkatvajradhar1
The applications of Artificial Intelligence (AI) are very diverse. Big players like Apple, Facebook, Google, or Samsung are not without reason to spend billions on new AI technologies.
AI continues to expand into different areas like healthcare, agriculture, scientific research and auditing.
AI is still only touching the surface when it comes to its application, especially if AI can work with time-series data.
Artificial Intelligence Best Practices: How AI Models Can Transform Legal and...Anna Kragie
The legal and corporate worlds are buying into the power of AI and machine learning. Now, many industries are becoming increasingly receptive to how to use better data classification methods and AI models to generate better business practices. A decade ago, the legal and corporate worlds needed more convincing about the power of AI and machine learning. Now, many industries — legal included — are becoming increasingly receptive to how to use better data classification methods and AI models to generate better business practices.
In the Dark? Understanding Big Data & AI: Talent Acquisition Strategies for 2018Yoh Staffing Solutions
Big Data and AI have changed the way companies acquire people. Is your organization one of them? Shed some light on this innovation with these valuable tips and gain a better understanding of the implications Big Data and AI can have on your talent acquisition strategy.
In a world where smart machines augment human work, board members and C-suite
executives need a working understanding of AI before they define how they will
implement it successfully within their organizations. Fingent provides technology
solutions that ensure that the technical implementation and strategic adoption of AI
builds toward their long-term goals.
How can b2 b companies use ai to support their sales venkatvajradhar1
The applications of Artificial Intelligence (AI) are very diverse. Big players like Apple, Facebook, Google, or Samsung are not without reason to spend billions on new AI technologies.
AI continues to expand into different areas like healthcare, agriculture, scientific research and auditing.
AI is still only touching the surface when it comes to its application, especially if AI can work with time-series data.
Artificial Intelligence Best Practices: How AI Models Can Transform Legal and...Anna Kragie
The legal and corporate worlds are buying into the power of AI and machine learning. Now, many industries are becoming increasingly receptive to how to use better data classification methods and AI models to generate better business practices. A decade ago, the legal and corporate worlds needed more convincing about the power of AI and machine learning. Now, many industries — legal included — are becoming increasingly receptive to how to use better data classification methods and AI models to generate better business practices.
In the Dark? Understanding Big Data & AI: Talent Acquisition Strategies for 2018Yoh Staffing Solutions
Big Data and AI have changed the way companies acquire people. Is your organization one of them? Shed some light on this innovation with these valuable tips and gain a better understanding of the implications Big Data and AI can have on your talent acquisition strategy.
In a world where smart machines augment human work, board members and C-suite
executives need a working understanding of AI before they define how they will
implement it successfully within their organizations. Fingent provides technology
solutions that ensure that the technical implementation and strategic adoption of AI
builds toward their long-term goals.
Artificial Intelligence has been around for almost 70 years, but only in recent years has it become a major disrupter for many industries due to the convergence of big data, processing power and cloud computing. This has led to the development of “deep learning”, which allows a type of computer intelligence that closely mimics human decision-making. In this paper, I take look at the evolution of Artificial Intelligence, along with two disparate industries: Retail and Real Estate. These industries have adopted AI at different speeds. Also, each industry has its own form of resistance and uses for the technology. My theory is that there are forms of technology resistance by major players in the real estate industry in combination with the long industry cycles that are causing slow adoption.
Building a High-Quality Machine Learning Model Using Google Cloud AutoML VisionBellakarina Solorzano
This capstone project sheds light on a new readily available generation of tools that are changing the marketing landscape. This study uses Google’s AutoML Vision interface to train a custom visual recognition model, while taking an in-depth look at how visual recognition technology works and how Artificial Intelligence, Machine Learning and Deep Learning are changing the way things get done across industries.
To conclude, this capstone project illustrates examples of the ways organizations across the world are getting the most out of these technologies and discusses the limitations and expectations of Artificial Intelligence and Machine Learning models going forward.
Analytical Storytelling: From Insight to ActionCognizant
Merging the ancient art of storytelling with digital-era data journalism, analytical storytelling makes data-based insights accessible and thus informs and guides skillful and effective decision-making.
VMware Business Agility and the True Economics of Cloud ComputingVMware
New groundbreaking global survey findings demonstrate
the true value of cloud computing to the business. While it is understood in the industry that cloud computing provides clear cost benefits, CIOs are having difficulty getting a true fix on the business value that cloud might offer beyond cost reduction. These survey results reveal a direct link between cloud computing and business agility—how business outcomes are associated with agility, the role of IT for agile companies and the importance of cloud computing to business leaders.
Tim O’ Reilly, who initiated the most widely accepted specification for Web 2.0, said
“data will be the driving force of Web 2.0…”
Sanjay Mehta's article on MAIA Intelligence Blog
5 ways to enhance your business using ai venkat k - mediumusmsystem
Artificial intelligence (AI) is fast becoming a competitive tool in business. Companies have been discussing the pros and cons of AI in the past. From enhanced chatbots to customer service to data analytics to recommendations, deep learning and artificial intelligence are seen as an important tool for business leaders in their many forms.
10 WealthTech podcasts every wealth advisor should listen toIBM Analytics
Listen to this “Finance in Focus” podcast series to hear a cast of interesting experts discuss how the wealth management industry is adapting to new and emerging technologies that include robo-advisors, blockchain, analytics, and cognitive. Over the course of 10 episodes, hosts Rob Stanich and Alex Baghdjian are joined by wealth management experts to discuss behavior financing, DOL fiduciary rule, social media marketing, account aggregation, millennials, surveillance, and regulations.
The 10 Best Data Analytics And BI Platforms And Tools In 2020Bernard Marr
Data has become a vital asset to all companies, big or small, and across all sectors. In order to extract value from that data businesses need the right analytics or BI (Business Intelligence) tools to make it happen. Here we look at the top 10 analytics and BI tools available today.
Where Data and Story Meet - Building the Data Storytelling CapabilityRanda McMinn
Data is rapidly transforming the way companies are transacting and engaging with customers. Gone are the days of not having enough data, now we are being inundated with too much data and are struggling to find ways to make sense of it. As a business leader, especially in the roles of data science and marketing, your success is heavily reliant on making sense of data, so it is becoming imperative to build and nurture a great data storytelling capability.
In this piece, we explore the increasing demands in skillsets for the modern data scientist and marketer. Further, we explore the mindset of data scientists and whether or not that mindset differs from a group of analytics professionals who have been identified as great data storytellers. We also reveal different ways to build the data storytelling capability.
3 (Not So Obvious) Industries That Could Be Transformed By Computer VisionBernard Marr
Computer vision, or machine vision, is an important business trend that will transform many industries and many business processes. Here we look at three not so obvious examples to showcase that it is important for any industry.
5 ways to enhance your business using ai venkat k - mediumusmsystem
“Artificial intelligence (AI)” is fast becoming a competitive tool in business. Companies have been discussing the pros and cons of AI in the past. From enhanced chatbots to customer service to data analytics to recommendations, deep learning and artificial intelligence are seen as an important tool for business leaders in their many forms.
How can business professionals succeed in a future with AISemir Jahic
This is the slide deck of a talk given at UCL (University College London) in December 2017. The aim of the talk is to give a very simple overview of AI and its potential future impact on business professionals with a non-technical background.
Marketing and sales teams, today, rely on data. They use analytics to forecast and track performance. They require current, actionable company and contact data to find leads, target campaigns, prioritize and engage with prospects, and ultimately win deals. High performance companies work with data providers who deliver current external company and contact data into their CRM and Marketing Automation applications. The question is, how do you choose the right data provider?
Jeff Johnson, Research Engineer, Facebook at MLconf NYCMLconf
Hacking GPUs for Deep Learning: GPUs have revolutionized machine learning in recent years, and have made both massive and deep multi-layer neural networks feasible. However, misunderstandings on why they seem to be winning persist. Many of deep learning’s workloads are in fact “too small” for GPUs, and require significantly different approaches to take full advantage of their power. There are many differences between traditional high-performance computing workloads, long the domain of GPUs, and those used in deep learning. This talk will cover these issues by looking into various quirks of GPUs, how they are exploited (or not) in current model architectures, and how Facebook AI Research is approaching deep learning programming through our recent work.
Artificial Intelligence has been around for almost 70 years, but only in recent years has it become a major disrupter for many industries due to the convergence of big data, processing power and cloud computing. This has led to the development of “deep learning”, which allows a type of computer intelligence that closely mimics human decision-making. In this paper, I take look at the evolution of Artificial Intelligence, along with two disparate industries: Retail and Real Estate. These industries have adopted AI at different speeds. Also, each industry has its own form of resistance and uses for the technology. My theory is that there are forms of technology resistance by major players in the real estate industry in combination with the long industry cycles that are causing slow adoption.
Building a High-Quality Machine Learning Model Using Google Cloud AutoML VisionBellakarina Solorzano
This capstone project sheds light on a new readily available generation of tools that are changing the marketing landscape. This study uses Google’s AutoML Vision interface to train a custom visual recognition model, while taking an in-depth look at how visual recognition technology works and how Artificial Intelligence, Machine Learning and Deep Learning are changing the way things get done across industries.
To conclude, this capstone project illustrates examples of the ways organizations across the world are getting the most out of these technologies and discusses the limitations and expectations of Artificial Intelligence and Machine Learning models going forward.
Analytical Storytelling: From Insight to ActionCognizant
Merging the ancient art of storytelling with digital-era data journalism, analytical storytelling makes data-based insights accessible and thus informs and guides skillful and effective decision-making.
VMware Business Agility and the True Economics of Cloud ComputingVMware
New groundbreaking global survey findings demonstrate
the true value of cloud computing to the business. While it is understood in the industry that cloud computing provides clear cost benefits, CIOs are having difficulty getting a true fix on the business value that cloud might offer beyond cost reduction. These survey results reveal a direct link between cloud computing and business agility—how business outcomes are associated with agility, the role of IT for agile companies and the importance of cloud computing to business leaders.
Tim O’ Reilly, who initiated the most widely accepted specification for Web 2.0, said
“data will be the driving force of Web 2.0…”
Sanjay Mehta's article on MAIA Intelligence Blog
5 ways to enhance your business using ai venkat k - mediumusmsystem
Artificial intelligence (AI) is fast becoming a competitive tool in business. Companies have been discussing the pros and cons of AI in the past. From enhanced chatbots to customer service to data analytics to recommendations, deep learning and artificial intelligence are seen as an important tool for business leaders in their many forms.
10 WealthTech podcasts every wealth advisor should listen toIBM Analytics
Listen to this “Finance in Focus” podcast series to hear a cast of interesting experts discuss how the wealth management industry is adapting to new and emerging technologies that include robo-advisors, blockchain, analytics, and cognitive. Over the course of 10 episodes, hosts Rob Stanich and Alex Baghdjian are joined by wealth management experts to discuss behavior financing, DOL fiduciary rule, social media marketing, account aggregation, millennials, surveillance, and regulations.
The 10 Best Data Analytics And BI Platforms And Tools In 2020Bernard Marr
Data has become a vital asset to all companies, big or small, and across all sectors. In order to extract value from that data businesses need the right analytics or BI (Business Intelligence) tools to make it happen. Here we look at the top 10 analytics and BI tools available today.
Where Data and Story Meet - Building the Data Storytelling CapabilityRanda McMinn
Data is rapidly transforming the way companies are transacting and engaging with customers. Gone are the days of not having enough data, now we are being inundated with too much data and are struggling to find ways to make sense of it. As a business leader, especially in the roles of data science and marketing, your success is heavily reliant on making sense of data, so it is becoming imperative to build and nurture a great data storytelling capability.
In this piece, we explore the increasing demands in skillsets for the modern data scientist and marketer. Further, we explore the mindset of data scientists and whether or not that mindset differs from a group of analytics professionals who have been identified as great data storytellers. We also reveal different ways to build the data storytelling capability.
3 (Not So Obvious) Industries That Could Be Transformed By Computer VisionBernard Marr
Computer vision, or machine vision, is an important business trend that will transform many industries and many business processes. Here we look at three not so obvious examples to showcase that it is important for any industry.
5 ways to enhance your business using ai venkat k - mediumusmsystem
“Artificial intelligence (AI)” is fast becoming a competitive tool in business. Companies have been discussing the pros and cons of AI in the past. From enhanced chatbots to customer service to data analytics to recommendations, deep learning and artificial intelligence are seen as an important tool for business leaders in their many forms.
How can business professionals succeed in a future with AISemir Jahic
This is the slide deck of a talk given at UCL (University College London) in December 2017. The aim of the talk is to give a very simple overview of AI and its potential future impact on business professionals with a non-technical background.
Marketing and sales teams, today, rely on data. They use analytics to forecast and track performance. They require current, actionable company and contact data to find leads, target campaigns, prioritize and engage with prospects, and ultimately win deals. High performance companies work with data providers who deliver current external company and contact data into their CRM and Marketing Automation applications. The question is, how do you choose the right data provider?
Jeff Johnson, Research Engineer, Facebook at MLconf NYCMLconf
Hacking GPUs for Deep Learning: GPUs have revolutionized machine learning in recent years, and have made both massive and deep multi-layer neural networks feasible. However, misunderstandings on why they seem to be winning persist. Many of deep learning’s workloads are in fact “too small” for GPUs, and require significantly different approaches to take full advantage of their power. There are many differences between traditional high-performance computing workloads, long the domain of GPUs, and those used in deep learning. This talk will cover these issues by looking into various quirks of GPUs, how they are exploited (or not) in current model architectures, and how Facebook AI Research is approaching deep learning programming through our recent work.
Soumith Chintala, Artificial Intelligence Research Engineer, Facebook at MLco...MLconf
Predicting the Future Using Deep Adversarial Networks: Learning With No Labeled Data: Labeling data to solve a certain task can be expensive, slow and does not scale. If unsupervised learning works, then one can have very little labelled data to help a machine solve a particular task. Most traditional unsupervised learning methods such as PCA and K-means clustering do not work well for complicated data distributions, making them useless for a lot of tasks. In this talk, I’ll go over recent advances in a technique for unsupervised learning called Generative Adversarial networks, which can learn to generate very complicated data distributions such as images and videos. These trained adversarial networks are then used to solve new tasks with very little labeled data, making them an attractive class of algorithms for many domains where there is limited labeled data but unlimited unlabeled data.
Hussein Mehanna, Engineering Director, ML Core - Facebook at MLconf ATL 2016MLconf
Applying Deep Learning at Facebook Scale: Facebook leverages Deep Learning for various applications including event prediction, machine translation, natural language understanding and computer vision at a very large scale. There are more than a billion users logging on to Facebook every daily generating thousands of posts per second and uploading more than a billion images and videos every day. This talk will explain how Facebook scaled Deep Learning inference for realtime applications with latency budgets in the milliseconds.
April 2016 HUG: CaffeOnSpark: Distributed Deep Learning on Spark ClustersYahoo Developer Network
Deep learning is a critical capability for gaining intelligence from datasets. Many existing frameworks require a separated cluster for deep learning, and multiple programs have to be created for a typical machine learning pipeline. The separated clusters require large datasets to be transferred between clusters, and introduce unwanted system complexity and latency for end-to-end learning.
Yahoo introduced CaffeOnSpark to alleviate those pain points and bring deep learning onto Hadoop and Spark clusters. By combining salient features from deep learning framework Caffe and big-data framework Apache Spark, CaffeOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. The framework is complementary to non-deep learning libraries MLlib and Spark SQL, and its data-frame style API provides Spark applications with an easy mechanism to invoke deep learning over distributed datasets. Its server-to-server direct communication (Ethernet or InfiniBand) achieves faster learning and eliminates scalability bottleneck.
Recently, we have released CaffeOnSpark at github.com/yahoo/CaffeOnSpark under Apache 2.0 License. In this talk, we will provide a technical overview of CaffeOnSpark, its API and deployment on a private cloud or public cloud (AWS EC2). A demo of IPython notebook will also be given to demonstrate how CaffeOnSpark will work with other Spark packages (ex. MLlib).
Speakers:
Andy Feng is a VP Architecture at Yahoo, leading the architecture and design of big data and machine learning initiatives. He has architected major platforms for personalization, ads serving, NoSQL, and cloud infrastructure.
Jun Shi is a Principal Engineer at Yahoo who specializes in machine learning platforms and large-scale machine learning algorithms. Prior to Yahoo, he was designing wireless communication chips at Broadcom, Qualcomm and Intel.
Mridul Jain is Senior Principal at Yahoo, focusing on machine learning and big data platforms (especially realtime processing). He has worked on trending algorithms for search, unstructured content extraction, realtime processing for central monitoring platform, and is the co-author of Pig on Storm.
Effectiveness and Efficiency Recognise the Value of AI & ML for Organisations...Flexsin
Learn about AI & ML importance for businesses. Implement them with Flexsin's AI development services & consulting for efficiency, engagement, and insights.
https://www.flexsin.com/artificial-intelligence/
Smart Data Webinar: Transforming Industries with Artificial Intelligence (AI)...DATAVERSITY
The state of the art and practice for AI and Machine Learning (ML) has matured rapidly in the past few years, making it an ideal time to take a look at what works and what doesn’t.
In this webinar, we will present an overview of AI-infused applications in two industries:
Manufacturing
Retail
Participants will learn to look for characteristics of business processes and of data that make them well - or ill - suited to AI-augmentation or automation.
Empowering the financial institutions with machine learning9 series
The finance sector has seen tremendous growth in the last few years with the adoption of Machine Learning algorithms. The main reason for such growth is the rise in affordable computing prowess for streamlining operations, optimizing portfolios, and underwriting loans.
10 Amazing Benefits of Machine Learning You Should Be Aware Of!Kavika Roy
https://www.datatobiz.com/blog/advantages-of-machine-learning/
ML aims to derive meaningful information from an immense amount of raw data. If implemented correctly, ML can act as a remedy to a variety of problems of market challenges and anticipate complicated consumer behaviors. We’ve already seen some of the significant technology companies coming up with their Cloud Machine Learning solutions, such as Google, Amazon, Microsoft, etc. Here are some of the critical ways ML can support your company:
20 Useful Applications of AI Machine Learning in Your Business ProcessesKashish Trivedi
The fear of robots taking over our lives has been a prevalent concern, with over 70% of the U.S. population expressing apprehension, as highlighted by a 2017 Pew Research study. However, while the emergence of a Skynet-like scenario remains uncertain, it's evident that technology, particularly artificial intelligence (AI), is poised to revolutionize various aspects of our daily tasks, freeing us from repetitive and dehumanizing job elements rather than rendering us obsolete. With AI being a strategic priority for 84% of businesses, its implementation has shown remarkable efficiency enhancements, such as boosting sales team productivity by over 50%. The accessibility of AI tools has expanded significantly, enabling practically anyone to leverage its benefits. In this discourse, we'll explore 20 diverse real-world applications of AI, ranging from healthcare and finance to entertainment and government, illustrating its pervasive impact on modern society.
20 Useful Applications of AI Machine Learning in Your Business ProcessesKashish Trivedi
A 2017 study from Pew Research found that more than 70% of the U.S. is scared that robots are going to take over our lives. And, while we can’t perfectly predict the emergence of a Skynet singularity, we can say with some certainty that technology is set to take over the repetitive, dehumanizing elements of our jobs instead of putting us out of work. Artificial intelligence (AI) is a strategic priority for 84% of businesses, and in some cases has been used to improve sales team efficiency by over 50%. Even I’ve used AI in the past to generate hundreds of relevant hashtags for social media posts at the click of a button. It was once the stuff of utopian science fiction and huge enterprises, but now practically anyone can take advantage. For this post, we will dive into 20 different applications of AI in the real world.
Regulating Generative AI - LLMOps pipelines with TransparencyDebmalya Biswas
The growing adoption of Gen AI, esp. LLMs, has re-ignited the discussion around AI Regulations — to ensure that AI/ML systems are responsibly trained and deployed. Unfortunately, this effort is complicated by multiple governmental organizations and regulatory bodies releasing their own guidelines and policies with little to no agreement on the definition of terms.
Rather than trying to understand and regulate all types of AI, we recommend a different (and practical) approach in this talk based on AI Transparency —
to transparently outline the capabilities of the AI system based on its training methodology and set realistic expectations with respect to what it can (and cannot) do.
We outline LLMOps architecture patterns and show how the proposed approach can be integrated at different stages of the LLMOps pipeline capturing the model's capabilities. In addition, the AI system provider also specifies scenarios where (they believe that) the system can make mistakes, and recommends a ‘safe’ approach with guardrails for those scenarios.
Real World Use Cases of Data Annotation in Machine Learning.pptxAndrew Leo
The new-gen technologies like Artificial Intelligence and Machine Learning have the potential to achieve enormous feats today. In fact, you cannot imagine your daily life without the use of AI and ML. It is because several components of our reality bear the significant use of these next-gen tech marvels in some way or the other.
Know More Info: https://www.damcogroup.com/data-support-for-ai-ml
#dataannotationservices
#annotationinmachinelearning
#dataannotationinmachinelearning
#damcosolutions
Data Annotation in Machine Learning – Key Challenges and How to Overcome ThemAndrew Leo
Explore the complexities of data annotation for Machine Learning on Damco’s insightful page. Delve into the key challenges faced in this crucial process and uncover effective solutions. Our formal guide provides a comprehensive understanding, aiding businesses in refining their Machine Learning models. Stay informed and stay ahead in the dynamic realm of technology.
The growing adoption of Gen AI, esp. LLMs, has re-ignited the discussion around AI Regulations — to ensure that AI/ML systems are responsibly trained and deployed. Unfortunately, this effort is complicated by multiple governmental organizations and regulatory bodies releasing their own guidelines and policies with little to no agreement on the definition of terms.
In this talk, we will provide an overview explaining the key Responsible AI aspects: Explainability, Bias, and Accountability. We will then outline the Gen AI usage patterns and show how the three aspects can be integrated at different stages of the LLMOps (MLOps for LLM) pipeline. We summarize the learnings in the form of Gen AI design patterns that can be readily applied to enterprise use-cases.
25 Tips On How a Perfect AI Strategy Can Help Your BusinessKavika Roy
https://www.datatobiz.com/blog/perfect-ai-strategy-can-help-your-business/
A comprehensive artificial intelligence business strategy can boost business and make the enterprise an industry leader. Let’s look at the round-up of pro tips shared by leaders in the AI industry.
AI for enterprises Redefining industry standards.pdfChristopherTHyatt
"AI for Enterprises revolutionizes business landscapes, offering unparalleled efficiency, data-driven decision-making, and personalized customer experiences. From automation to advanced analytics, this transformative technology empowers organizations to streamline operations, enhance productivity, and stay ahead in the competitive digital era. Embrace the future of business with AI for Enterprises and unlock a realm of innovation, strategic insights, and sustainable growth."
Evolution of AI ML Solutions - A Review of Past and Future Impact.pdfChristine Shepherd
Need to incorporate technologies that drive unparalleled advancements? If yes, leveraging AI and Machine Learning services helps enterprises to streamline operations and also usher in a new era of possibilities and societal benefits. Whether it's designing novel solutions, creating intelligent products, or optimizing workflows, AI and ML serve as catalysts for innovation, propelling enterprises into the forefront of their respective industries.
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
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
1. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Visualizing Inference
Seeing Meaning in the Age of Big Data
Alex Morrise
October 9, 2014
2. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
1 Big Data meets Machine Learning
2 Machine Learning Pros and Cons
What is Business Value of ML?
ML vs. BI
ML disclaimer
Personalization
3 Visualizing Inference
ML Infancy
Cases Studies
4 Summary
3. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
“Machine Learning is the antidote to having to
write down billions of business rules”
Machine Learning Uses Include:
User Personalization
4. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
“Machine Learning is the antidote to having to
write down billions of business rules”
Machine Learning Uses Include:
User Personalization
Finding Predictors to a given Objective (Revenue,
Churn, Volatility, Moods, Sentiment, Market Cap,
Stocks, Biomedical, Risk Assessment, etc)
5. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
“Machine Learning is the antidote to having to
write down billions of business rules”
Machine Learning Uses Include:
User Personalization
Finding Predictors to a given Objective (Revenue,
Churn, Volatility, Moods, Sentiment, Market Cap,
Stocks, Biomedical, Risk Assessment, etc)
Fraud Detection (Purchases, Identity Thief, Health
Insurance, Governments)
6. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
“Machine Learning is the antidote to having to
write down billions of business rules”
Machine Learning Uses Include:
User Personalization
Finding Predictors to a given Objective (Revenue,
Churn, Volatility, Moods, Sentiment, Market Cap,
Stocks, Biomedical, Risk Assessment, etc)
Fraud Detection (Purchases, Identity Thief, Health
Insurance, Governments)
Optimizing Decision Flows (Shipping, Markets,
Robotics, Database Migrations, Team resource
allocation and management, etc...)
7. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
“Machine Learning is the antidote to having to
write down billions of business rules”
Machine Learning Uses Include:
User Personalization
Finding Predictors to a given Objective (Revenue,
Churn, Volatility, Moods, Sentiment, Market Cap,
Stocks, Biomedical, Risk Assessment, etc)
Fraud Detection (Purchases, Identity Thief, Health
Insurance, Governments)
Optimizing Decision Flows (Shipping, Markets,
Robotics, Database Migrations, Team resource
allocation and management, etc...)
Key Take Away
Every Industry is Touched by Machine Learning
8. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
What is the Business value of Machine
Learning and Data Science?
Companies are being inundated with data:
User Behavioral Data/Click Stream (purchases, views,
engagement, interests)
9. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
What is the Business value of Machine
Learning and Data Science?
Companies are being inundated with data:
User Behavioral Data/Click Stream (purchases, views,
engagement, interests)
Sensor Data (Internet of Things, smart meters,
construction, management)
10. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
What is the Business value of Machine
Learning and Data Science?
Companies are being inundated with data:
User Behavioral Data/Click Stream (purchases, views,
engagement, interests)
Sensor Data (Internet of Things, smart meters,
construction, management)
B2B (SaaS management tools across all industries,
Salesforce, etc)
11. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
What is the Business value of Machine
Learning and Data Science?
Companies are being inundated with data:
User Behavioral Data/Click Stream (purchases, views,
engagement, interests)
Sensor Data (Internet of Things, smart meters,
construction, management)
B2B (SaaS management tools across all industries,
Salesforce, etc)
B2C (Uber, Netflix, google)
12. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
What is the Business value of Machine
Learning and Data Science?
Companies are being inundated with data. They all have
one thing in common.
They want a way to capitalize on the real meaning behind
the data.
Bayesian methods allow the discovery of the latent
properties in data, while assessing our confidence in
the models certainty/ignorance.
Hypothesis testing, parameter estimation, confidence
intervals, etc..
13. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
What is the Business value of Machine
Learning and Data Science?
Why do businesses need Machine Learning/Data Science?
They have plenty of data to train expert systems
14. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
What is the Business value of Machine
Learning and Data Science?
Why do businesses need Machine Learning/Data Science?
They have plenty of data to train expert systems
Traditional BI may not be able to find the correct
patterns
15. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
What is the Business value of Machine
Learning and Data Science?
Why do businesses need Machine Learning/Data Science?
They have plenty of data to train expert systems
Traditional BI may not be able to find the correct
patterns
Shifting focus from traditional 20th century business
objectives, companies need to convert their value
propositions into technological currency.
16. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
What is the Business value of Machine
Learning and Data Science?
Why do businesses need Machine Learning/Data Science?
They have plenty of data to train expert systems
Traditional BI may not be able to find the correct
patterns
Shifting focus from traditional 20th century business
objectives, companies need to convert their value
propositions into technological currency.
Users & Businesses are sophisticated and want
intelligence in their applications
17. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
What is the Business Value of BI?
Traditional BI tools and methods (Tableaux, Splunk, etc), are
amazing, sophisticated and potentially misleading
Example: Splitting demographic data into seemingly
good piles and running aggregating reporting over
those splits.
18. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
What is the Business Value of BI?
Traditional BI tools and methods (Tableaux, Splunk, etc), are
amazing, sophisticated and potentially misleading
Example: Splitting demographic data into seemingly
good piles and running aggregating reporting over
those splits.
Wonderful if you want to know what women ages 24-26
are purchasing in Los Angeles this month.
19. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
What is the Business Value of BI?
Traditional BI tools and methods (Tableaux, Splunk, etc), are
amazing, sophisticated and potentially misleading
Example: Splitting demographic data into seemingly
good piles and running aggregating reporting over
those splits.
Wonderful if you want to know what women ages 24-26
are purchasing in Los Angeles this month.
What about Behavior?
20. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Businesses want Automatic Action
Finding Behavior in a Automatic Actionable Way
Finding Behavior requires leveraging the power of
machine learning to tease out the meaning behind the
observations in a crowd sourced way.
21. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Businesses want Automatic Action
Finding Behavior in a Automatic Actionable Way
Finding Behavior requires leveraging the power of
machine learning to tease out the meaning behind the
observations in a crowd sourced way.
Bayesian methods such as Factorizations, Hierarchical
Clustering, and other Topic models, extract meaning
from the data.
22. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Businesses want Automatic Action
Finding Behavior in a Automatic Actionable Way
Finding Behavior requires leveraging the power of
machine learning to tease out the meaning behind the
observations in a crowd sourced way.
Bayesian methods such as Factorizations, Hierarchical
Clustering, and other Topic models, extract meaning
from the data.
Once model is fit, observations of Behavior connect to
Actions in the system (API), yielding an automatic
intelligent way to process information and transactions.
23. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Disclaimer: Value of ML?
Let’s be Honest:
ML can also lead to misleading results when used in the
wrong hands:
Running a Decision Tree on demographic data could
likely split the population by M/F right off the bat.
24. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Disclaimer: Value of ML?
Let’s be Honest:
ML can also lead to misleading results when used in the
wrong hands:
Running a Decision Tree on demographic data could
likely split the population by M/F right off the bat.
This split will lead to misleading results as it tries to
explaining the objective.
25. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Disclaimer: Value of ML?
Let’s be Honest:
ML can also lead to misleading results when used in the
wrong hands:
Running a Decision Tree on demographic data could
likely split the population by M/F right off the bat.
This split will lead to misleading results as it tries to
explaining the objective.
Know your ML tool belt, practice makes perfect, and
treat the job as science (tests, validation, parameter
search, research, etc).
26. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Disclaimer: Value of ML?
Let’s be Honest:
ML can also lead to misleading results when used in the
wrong hands:
Running a Decision Tree on demographic data could
likely split the population by M/F right off the bat.
This split will lead to misleading results as it tries to
explaining the objective.
Know your ML tool belt, practice makes perfect, and
treat the job as science (tests, validation, parameter
search, research, etc).
Answer: Use a Random Forest instead.
27. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
ML is BI
Example: The Retail Vertical
BI reporting can be good for detecting aggregate trends
but fails to personalize.
28. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
ML is BI
Example: The Retail Vertical
BI reporting can be good for detecting aggregate trends
but fails to personalize.
Personalization can find aggregate trends and solve the
question,
“What are the 3 shoes you are highly likely to engage
and ultimately purchase, given your (sparse) purchase
history, time of year, demographic information, etc”
29. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Intermission
We’ve stated use cases for ML
Business will capitalize on including ML in their stack.
Let’s move on to see how we see the meaning behind
the data?
30. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Machine Learning in it’s Infancy
31. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Visualizing Inference
To see the latent properties in your data, construct a Graph
G as follows
Form your data matrix M
32. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Visualizing Inference
To see the latent properties in your data, construct a Graph
G as follows
Form your data matrix M
Factor it using your favorite algorithm, M = WH
33. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Visualizing Inference
To see the latent properties in your data, construct a Graph
G as follows
Form your data matrix M
Factor it using your favorite algorithm, M = WH
Cluster in W and Ht
34. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Visualizing Inference
To see the latent properties in your data, construct a Graph
G as follows
Form your data matrix M
Factor it using your favorite algorithm, M = WH
Cluster in W and Ht
Use the cluster assignments (or some similarity metric
on factors) to make graph G.
35. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
8tracks.com, the Best Music Service on Planet
Earth
36. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
8tracks.com
37. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
8tracks.com
38. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
8tracks.com
39. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Boomtrain.com
Boomtrain.com uses machine learning to inform decisions.
Boomtrain offers an end to end solution using, in part, a real
time novel view into the user base of a given company. By
learning
Users Proclivity to a set of Topics
User Archetypes
Users Derived Meta-Properties
Boomtrain.com exposes this knowledge in an actionable
framework, allowing clients to drive engagement and
retention.
40. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Boomtrain.com
41. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Quid.com (Assessing metrics in Technology and
Innovation)
IdleGames.com (Behavior as Predictor of Demographic
and Monetization)
BeatsMusic.com (Contextualized Music
Recommendation – Understanding the Heart of the
Music)
42. Visualizing
Inference
Alex Morrise
Outline
Big Data
meets
Machine
Learning
Machine
Learning Pros
and Cons
What is Business
Value of ML?
ML vs. BI
ML disclaimer
Personalization
Visualizing
Inference
ML Infancy
Cases Studies
Summary
Machine Learning is the Future
We are just at the onset of a radical transformation in the
way we do, and see, everything
Every business is transforming into a technology
company
They all need intelligence powering their core offerings
Finding better ways to see the meaning behind the data
will drive each of those offerings.