- Webinar: Deployment of Strategic AI in the Enterprise
We want to invite you to participate in the FREE ODSC Webinar! Date: September 11th Time: 10 am - 11 am PST To access this webinar, please register using the link below: https://attendee.gotowebinar.com/register/984495415926463501 The deployment of AI is truly transformational when it impacts the core business tasks and processes of the enterprise. For this reason, most organizations should only undertake AI initiatives with strategic impact potential. Experience shows that AI transformation programs achieve better results if organized in successive iterations of projects that implement high-value or even disruptive use cases. If managed properly, each project will create momentum and elements that will accumulate until critical mass is achieved. This iterative bottom-up approach is the most effective and realistic way of facing the daunting task of achieving the required AI proficiency within a reasonable time and cost. Contrary to this approach, too many AI transformation programs start with the creation of a corporate unit which is given a list of open mission statements such as “gather all our data and exploit it”, “identify and prioritize all the AI opportunities company-wide”, or “build the capabilities required to transform the business with AI”. The corporate AI unit is quickly flooded with too many requests and ideas and its efforts become diluted and immersed into power-fights. This also leads to AI tourism, since initiatives are launched to learn and experiment but have no continuity. It is very unlikely that this top-down approach achieves an acceptable fraction of the intended transformational goals even if provided with enough time and resources. This talk will discuss in detail why a task-force, bottom-up approach is the only suitable initial AI deployment option for most enterprises. The presentation will provide a set of guiding principles within a framework inspired by agile methods to manage and deliver effective strategic AI programs in realistic enterprise environments. Talk will be delivered by Fernando Nunez-Mendoza, a serial technology entrepreneur and disruptor, who is founder, chief executive officer, and chief technology officer of fonYou, a fast-growing international company born in Barcelona, Spain. fonYou’s mission is to build the mobile carrier of the future powered by AI. Before fonYou, he was a management consulting partner at Accenture and Diamond Cluster International helping global telecommunications, technology, and financial services firms embrace the internet and thrive in the brave new digital world. In his earlier career, Fernando worked for the European Space Agency and lectured and performed research in computer engineering and neural networks. Fernando holds, MSEE and Ph.D. degrees in Electrical and Computer Engineering from the Polytechnic University of Catalonia (Spain), was an invited Visiting Scholar at Purdue University and is alumni of Stanford University Graduate School of Business. ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/
- ODSC West 2019 Warm-Up: Machine Learning
We want to invite you to participate in the ODSC Webinar! During it you will get to know more about four sessions you can attend at our conference in San Franciso on Oct 29th - Nov 1st, 2019. Date: July 24th, 2019 Time: 1:00 pm - 2:30 pm PST To access this webinar, please register using the link below: https://attendee.gotowebinar.com/register/4037580819899150349 Sessions: 1 - Causal Inference & Machine Learning Speaker: Vinod Bakthavachalam, Data Scientist at Coursera Lots of data science problems, especially towards informing business and product strategy, involve understanding causal relationships. The standard way to measure these is through AB testing, but many times that is infeasible, requiring alternative techniques from the causal inference that are an essential component of any data scientist's toolkit. The talk will walk through these techniques, some applications, and recent work at the intersection of causal inference and machine learning to handle large data sets. 2 - Real-ish Time Predictive Analytics with Spark Structured Streaming Speaker: Scott J Haines, Principal Software Engineer at Twilio In 20 short minutes learn what becomes possible when you add Spark into your analytics pipeline. Learn how to effectivley solve common Data Engineering problems with compile-time guarenttes - like how to ingest, normalize, transform and join datasets in realtime. Learn how to add insights on top of your streaming data with simple filters and pre-trained models. 3 - Visualizing Complexity: Dimensionality Reduction and Network Science Speaker: Jane Adams, Data Visualization Artist at University of Vermont Complex Systems Center Working with mathematicians, data scientists, and domain experts at the University of Vermont Complex Systems Center, data visualization artist Jane Adams has developed strategies for prototyping exploratory graphs of high-dimensional data. In this 90-minute workshop, Adams shares some of these methods for data discovery and interaction, navigating a creative workflow from paper prototypes of visual hypotheses through web-based interactive slices, offering critical insight for clustering, interpolation, and feature engineering. 4 - Healthcare NLP with a doctor's bag of notes Speaker: Andrew Long, PhD, Data Scientist at Fresenius Medical Care Nausea, vomiting, and diarrhea are words you would not frequently find in a natural language processing (NLP) project for tweets or product reviews. However, these words are common in healthcare. In fact, many clinical signs and patient symptoms (e.g. shortness of breath, fever, or chest pain) are only present in free-text notes and are not captured with structured numerical data. As a result, it is important for healthcare data scientists to be able to extract insight from unstructured clinical notes in electronic medical records. In this hands-on workshop, the audience will have the opportunity to complete a Python NLP project with doctors’ discharge summaries to predict unplanned hospital readmission. The audience will learn how to prepare data for a machine learning project, preprocess text using a bag-of-words approach, train a few predictive models, evaluate the performance of the models, and strategize how to improve the models. The MIMIC III data set is used in this tutorial and requires requesting access in advance (an artificial dataset will be provided for those without access).
- When Data Goes Bad
Speaker: Eric Busboom - President at Civic Knowledge https://www.linkedin.com/in/ericbusboom/ Topic: When Data Goes Bad Schedule: 6:00pm - 6:30pm - ODSC Intro, Pizza & Refreshments 6:30pm - 7:20pm - Talk 7:20pm - 7:30pm - Q&A 7:30pm - 8:00pm - Networking Bio: Eric Busboom is an entrepreneur, technologist, and philanthropist, and is most interested in solving important social problems using data. His career has covered nonprofits, telecommunications, software engineering, healthcare, fashion and action sports. His current nonprofit project is the San Diego Regional Data Library, a data analysis organization that helps San Diego County nonprofits to be more effective by using data more efficiently. The Data Library is supported by his company, Civic Knowledge, which builds curated data collections for nonprofits, government, and journalists. Abstract: Data projects always start with high expectations, but real data rarely meet those expectations. The volume and variety of modern datasets can hide as many complex problems as interesting insights. This talk will explore examples of analysis-halting deficits in several public datasets and demonstrate graphical techniques to explore and address them. ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/ • NYC Immersive AI conference June 28 -29: https://odsc.com/nyc • West Conference Oct 29 - Nov 1: https://odsc.com/california • Europe Conference Nov 19 - 22: https://odsc.com/london