While most of the attention for how artificial intelligence (AI), machine learning and big data can impact companies is focused on the business to consumer (B2C) space, business to business (B2B) companies need to pay attention or they risk their future success. Customers have the same expectations for a simple and easy buying experience whether it's a B2C or B2B interaction. So, if you're in the B2B space, I hope your organization is beginning to explore and plan for, if not already implemented, big data and machine learning to your operations. Since the buying cycle for B2B is usually significantly longer and more complex than B2C (Gartner found there is an average of 5.4 people on corporate buying teams), I would argue that it's even more important for B2Bs to use machines in any way possible to quickly get to know customers and respond to decision-makers with the objective of closing sales as efficiently as possible. According to Salesforce's 2016 Connected Customer report, by 2020, 57% of business buyers will depend on companies to anticipate their needs and if they don't, business buyers will have no problem switching brands.
Note:- By end of this weekend, will write a blog post "Tutorial on Tab:- A Linux Shell Utility" 2. Deep Learning Tutorial:- From Perceptron to Deep Networks:- - In this tutorial, author is introducing the reader to the key concepts and algorithms behind deep learning, begging with the simplest unit of composition and building to the concepts of machine learning in Java 3. Faster Apache Pig with Apache Tez - Apache Pig 0.14.0 released on Nov 20th, 2014; And the good news is Tez if now one of the execution engine.
To get the most out of their data, successful companies are not focusing on queries and data lakes, they are actively integrating analytics into their operations with a data-first application development approach. Real-time adjustments to improve revenues, reduce costs, or mitigate risk rely on applications that minimize latency on a variety of data sources. In his session at @BigDataExpo, Jack Norris, Senior Vice President, Data and Applications at MapR Technologies, reviewed best practices to show how companies develop, deploy, and dynamically update these applications and how this data-first approach is fundamentally different from traditional applications. He covered examples of how leading companies have identified ways to simplify data streams in a publish-and-subscribe framework (for example, how focusing on a stream of electronic medical records simplified the deployment of real-time applications for hospitals, clinics, and insurance companies). He also detailed how a data-first approach can lead to rapid deployment of additional real-time applications as well as centralize and simplify many data management and administration tasks.
With each passing month, we see more and more car companies taking a deep dive into artificial intelligence and autonomous systems, as well as studying big data that comes with developing autonomous systems for use in city environments. They do this either by partnering with existing companies or absorbing them, or through loose investments with tech sharing agreements. Audi is starting to train their own employees in-house under the new "data.camp" Despite advances in education and the inclusion of information technology in the most syllabuses around the world, there is still a great number of people in the current workforce that don't quite understand the basics of it. This is especially true in Germany where vocational training means most employees have very narrow ranges of expertise, but with new car development requiring integration with the cloud and such, employees need to understand what they're going to be dealing with.
RavenPack's prestigious annual event has experienced growing interest, with attendance exceeding 260 buy-side professionals. Word on the street is RavenPack's research symposium is a "must attend event" for quantitative investors and financial professionals that are serious about Big Data. An excellent set of senior finance professionals shared their latest research and experience with big data and machine learning.
At home, it helps power personalized shopping apps, suggests personalized entertainment experiences, manages and monitors self-driving cars, supports virtual assistants, and improves navigation. At the office, it helps businesses develop the next best offer, recruit top-notch candidates, detect fraud, automate supply chains, and boost data center efficiency. Yet, corporate finance leaders are asking deeper questions which require more advanced analytics systems. "Why can you ask your mobile phone for directions to find the nearest restaurant but you can't ask your system how revenues are trending in Italy?" Oracle Vice President of Product Strategy for Big Data Analytics, Rich Clayton said during a recent Financial Executives International (FEI) webcast. "Why is that your systems don't understand your processes?
Job Qualifications We are looking for candidates whose teaching and research interests are related to marketing analytics, summarized by one or multiple of the following keywords, amongst statistical and machine learning algorithms, (rule-based/hybrid) ensembles, predictive modeling, R, Python, SAS, Spark, (NO/)SQL, web analytics, web scraping, social media analytics, data mining, recommendation tools, process mining, social network analytics, fraud detection, text mining, visual analytics, and/or big data analysis tools. Applicants should possess a PhD and be able to provide evidence of publications (and/or demonstrate the potential to publish) in reputable academic journals. The candidate will contribute to the IÉSEG Excellence Center for Marketing Analytics and shares his/her expertise within the MSc. in Big Data Analytics for Business. He/she also needs to provide evidence of strong teaching skills and/or professional experience. Applicants should be completely fluent in English as all courses will be taught in this language.
I'd like to add a bit to this discussion, if I may... Since ML people come from many places, but chiefly statistics (incl. The former prefer "stream-of-consciousness" work, where they get ideas and test them, play around for a bit until they get something that works well for them... and then keep the notebook in that format, usually due to lack of time/resources for this "finished" project. This generally makes it hard for other people to easily wrap their heads around what you've done by themselves (ie without a walk-through). This philosophy is more or less the basis of the R language and Jupyter-style notebooks: easy for experimentation and results are immediate. I use this approach when exploring new tasks.
First started in 2015, the Open Data Science Conference (better known as the ODSC) has now blossomed into a one of the top ten Data Science Conferences in a year (by various compilations). This year, the ODSC West was held at the Hyatt Regency San Francisco Airport, from November 2 to 4. Sheamus McGovern, the ODSC Chair and his team pulled in business executives and engineers to a packed and sold-out event. An event of this magnitude comes together with a ton of planning and working together. I am, attempting here, to give you a snapshot tour of what I experienced. The tech-focused piled into eleven premium training sessions that kicked off in parallel to the Accelerate AI event which was the new one-day format for the business folks.
Imagine if something not designed with you or anyone like you in mind was the driving force of how regular interactions permeate your life. Imagine it controls what products are marketed to you, how you can use certain consumer products (or not), influences your interactions with law enforcement, and even determines your health care diagnoses and medical decisions. There are problems brewing at the core of artificial intelligence and machine learning (ML). AI algorithms are essentially opinions embedded in code. AI can create, formalize, or exacerbate biases by not including diverse perspectives during ideation, testing, and implementation.