AI News, Upcoming data conferences feature Insight Fellows and team members

Upcoming data conferences feature Insight Fellows and team members

On the heels of Strata and the Grace Hopper Celebration, there are three well-regarded conferences in the data community scheduled for next week.

Starting Monday, DataEngConf will feature data engineering and data science talks by industry experts (including Insight alumni and team members) for two days at Columbia University.

Her talk will focus on how her team uses Google Cloud to determine the optimal number of “single-copy” newspapers to send to each sales location.

The newspaper was an early adopter of the cloud platform, and Bauer said re-designing and deploying their single copy model has been one of her team’s longest-running projects.

Geneviève Smith, Insight’s Director of Product (and an alumna of the data science program), will talk about the lessons Insight has learned in creating a collaborative learning environment for our data science, health data science, data engineering, and artificial intelligence Fellows.

The data startup track also adds an extra benefit — ticket prices as low as $250, a fraction of the regular price.

For those interested in big data, two of our data engineers, Judit Lantos and David Drummond, will be running a Spark training session that will focus on solving real-world data problems faced by the industry.

10 Data Science, Machine Learning and AI Podcasts You Must Listen To

With the rapid pace at which technology is driving innovation in machine learning and artificial intelligence, it has become immensely important to keep pace with the ongoing trends in data science.

With the remarkable rise of the data science industry in recent years, enough podcasts have been created for us to geek out over.

This is Analytics Vidhya’s exclusive podcast series which features top leaders and practitioners in the data science and machine learning industry. In fact, the first three episodes are already available!

With episodes ranging from anywhere between 15 minutes to an hour, the Data Skeptic is a great way to introduce yourself to the world of Data Science podcasts.

The topics include interviews with data science practitioners to talk about real world data science challenges, simple academic concepts like feature selection, NLP, decision trees, among may others.

This can get technical and quite in-depth at times, but it’s still a great way of keeping up to date with what’s happening in the world of AI and Machine Learning.

Each episode features interviews with industry experts about data science and gives a holistic overview of a few techniques.

Average episode duration: 60 minutes Total Number of Episodes: 29 Area(s) of focus: Basic to intermediate data science concepts, listener Q&A , interviews with industry experts

The hosts, Ben Jaffe and Katie Malone, manage to break down complex data science problems and techniques into snippets of information that can be easily digested by the casual listener.

Average episode duration: 15 minutes Total Number of Episodes: 164 Area(s) of focus: Data science and machine learning concepts applied to real-world issues

Dubner explores a whole host of puzzling issues in the world but it gives the listener a good idea of how data science can be integrated into economics and other global issues.

Why Are Data Science Leaders Running for the Exit?

I've had several conversations recently with people I know in the data science space that always start out about business and then drift to the state of data science as a whole.

One theme constantly comes up in these conversations: There are a lot of people currently running data science teams at large organizations and the vast majority of them — I believe we are talking about 80-90%— want to leave their jobs.

So what is going on in larger organizations that is causing such a mass exodus?Having worked in and with many large organizations in a data science leadership capacity, I have a few theories.

They were never taught how to run a P&L, manage a team, or deal with people, competitive intel, market assessments, building a business case, etc.

If you’re not willing to give data science managers the training and support they need — or create an organizational structure that brings people with business backgrounds into data science strategy — what will happen is your budget will grow and your results will drop.

PhDs do great work, but the type of work that they excel in isn’t what most companies are asking them to do in this new age of enterprise data science.

The leader of a data science practice needs to focus on data governance, MDM, compliance, legal issues around the use of algorithms, and documentation just in case someone sues for wrongful use.

There are hiring issues and staffing problems to deal with, budget and funding to gain, P&L to run, business cases to build, market research to conduct, vendor meetings to hold, tech life-cycle management, the evangelizing of projects (both internal and external), and turning that work into data products that sell — all this while trying to ensure profit for the company.

When I build a recommendation engine, my cost per unit is pretty much zero, unlike a physical product which certainly has a per unit cost.

Smart Implementation of Machine Learning and AI in Data Analysis: 50 Examples, Use Cases and Insights on Leveraging AI and ML in Data Analytics

Now that more companies are mastering their use of analytics, they are delving deeper into their data to increase efficiency, gain a greater competitive advantage, and boost their bottom lines even more.

Specifically, companies in the customer engagement space utilize AI and machine learning to analyze conversations, both those that end in a sale and those that don’t, and to automatically identify the language that typically leads to a sale or that predicts when a sale will occur.

To help your company understand how machine learning and AI in data analysis can benefit your business, we have rounded up examples of smart implementation, insights from the experts, and business use cases to give you the information you need to start using these types of advanced data analysis yourself.

Please note, we have listed our 50 examples of smart implementation of machine learning and AI in data analysis in alphabetical order to simplify your search process;

@wittysparks WittySparks is a blog run by creative minds who practice in a host of fields and write about hot topics in digital marketing, content marketing, business, and technology, among other fields.

In this marketing strategy article, Dan Shewan shares 10 examples of companies using machine learning in innovative ways, including image curation at scale, improved content discovery, and to leverage chatbots.

In his ThoughtSpot article, chief data Evangelist Doug Bordonaro explains that you don’t really need to understand machine learning, artificial intelligence, and deep learning to take advantage of them for your business.

Three key details we like from 5 Ways Machine Learning Can Make Your BI Better: @Medium @NathanBenaich In his Medium article, investor and technologist Nathan Benaich uses his expertise in AI and emerging technology to encourage readers to delve into machine learning.

Three key details we like from 6 Areas of AI and Machine Learning to Watch Closely: @TechEmergence Professionals who want to know exactly how AI impacts their industry look to TechEmergence because they provide original market research and media on AI.

In his TechEmergence article, Joao-Pierre Ruth explores six examples of AI platform providers that are holistic solutions for better business intelligence and analytics automation.

Their AI Business Use Cases share detailed glimpses into how machine learning makes it possible to automate common data workflow, detect objects by image, and understand text.

They refer to AI and machine learning as “the most important general-purpose technology of our era,” because the machine continually improves its performance without humans needing to explain how to accomplish all its tasks.

Now, they are shaking up the banking world by focusing on customer behavior and analytics with its in-house startup, Advanced Analytics, which features leading-edge AI and machine learning technology.

Subramanian reminds readers that machine learning provides enterprises with the framework, insights, and algorithms needed to ensure better predictive ability.

Three key details we like from Enterprises Approach to Machine Learning: @mitsmr MIT Sloan Management Review leads the way for academic researchers, business executives, and other influencers and thought leaders about advances in management practice, especially those shaped by technology.

Three key details we like from How 11 CIOs are Using Machine Learning to Boost Innovation: @HarvardBiz After Erik Brynjolfsson and Andrew McAfee published their HBR article arguing AI and machine learning will become “general-purpose technologies,” HBR senior editor Walter Frick sat down with Hilary Mason, the founder of Fast Forward Labs, to discuss how companies can put these technologies into practice and how to take advantage of them.

Three key details we like from How AI Fits into Your Data Science Team: @IEGroup InnovationEnterprise is the leading global voice in enterprise innovation, providing access to cutting-edge content across nine distinct channels.

In their perspectives report, that provide a comprehensive overview of AI and machine learning and examine how smart apps are impacting small businesses and the implications of the technology on small businesses.

In fact, the Accenture Artificial Intelligence Report predicts that AI may cause annual economic growth rates to double and boost productivity by nearly 40% by 2035.

Three key details we like from How Healthcare Can Prep for Artificial Intelligence, Machine Learning: @MaruitTech Maruit Techlabs is a professional team delivering end-to-end software solutions related to chatbots, mobile platforms, application development, and web analytics.

Their machine learning article explores how the technology boosts predictive analytics, yet only 60% of business leaders who cite growth as a key source of value from analytics have predictive analytics capabilities.

Lukas Biewald’s TechCrunch article asserts that machine learning is forcing massive changes in company operations and explores how businesses use machine learning every day.

Three key details we like from How to Use Machine Learning in Business: @BernardMarr Barnard Marr, bestselling author, keynote speaker, strategic performance consultant, and analytics, KPI, and Big Data expert, shares how Walmart uses machine learning, AI, IoT, and Big Data to improve performance.

Three key details we like from How Walmart Is Using Machine Learning AI, IoT and Big Data to Boost Retail Performance: @edgylabsdotcom Edgy Labs is comprised of a group of technologists and successful tech entrepreneurs who specialize in growth hacking, SEO, artificial intelligence, virtual reality, augmented reality, and the Internet of Things.

Three key details we like from How You Use Machine Learning Everyday and Business Will, Too: @Deloitte Deloitte is a global network of member firms that helps clients achieve their goals, solve complex problems, and make meaningful progress.

Here, Philipp Gerbert, Martin Reeves, Sebastian Steinhäuser, and Patrick Ruwolt share the findings of a report BCG conducted with MIT Sloan Management Review to determine exactly how businesses use AI and establish a baseline to help companies compare their efforts and goals with the technology and to offer guidance for future initiatives Three key details we like from Is Your Business Ready for Artificial Intelligence?: @BioStorage Denodo senior product marketing manager Saptarshi Sengupta wrote this article BioStorage Technologies to examine the ways in which machine learning and AI impact medical research.

They also share this article by Scott Hackl, global head of sales for Finacle at EdgeVerve, which presents his argument that banks and credit unions should use Ai and the power of advanced analytics in order to become agile and remain relevant.

She also addresses the ways in which the advanced technology can work for small businesses and investigates several services and products that make AI and machine learning accessible for those businesses.

In this article, they explore deep learning and machine learning and the ways in which Gartner predicts deep learning will be a critical component of demand, fraud, and failure predictions by 2019.

Three key details we like from Why AI, Machine Learning and Big Data Really Matter to B2B Companies: @salesforceiq SalesforceIQ delivers relationship intelligence technology to help companies save time and close more deals via smarter selling and better relationships.

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