Posted by More than 300 attendees and 20 international speakers didn’t miss the second edition of 2ML: Madrid Machine Learning. #2ML18 gathered mostly decision makers and Machine Learning practitioners coming from six different countries: the US, Canada, China, Uruguay, Holland, Austria, and of course Spain, as the event was held in Madrid.
The opening remarks were given by Luis Martín, CEO at Barrabés.biz, who welcomed the audience to their new and impressive co-working space. After him, the two keynotes of the event provided the base of 2ML, analyzing the basic concepts of Machine Learning, both from a technical and business perspective.
The technical concepts came from the BigML CEO, Francisco Martín. With him, we learned what Machine Learning is, the basic ML workflow, kinds of algorithms, how ML is used to make predictions, and how to make better decisions in several domains. In other words, the complete ML process end-to-end. Explaining the differences between AI, ML and Deep Learning, among many other concepts, helped the audience understand the meaning of Machine Learning from a technical perspective. The second keynote was mastered by Ed Fernández, Co-Founder and partner at Naiss.io, who focused on the state of the art in Machine Learning from a business perspective. Ed Fernández presented the impact of ML in Venture Capital, the M&A trends in ML and AI, the enterprise AI scene, and finally the ML market adoption trends, evolution, and platformization of Machine Learning in the Enterprise.
After we established the basis of this 2-day event with the basic concepts of Machine Learning, we could delve into a multitude of real-world applications of ML. This block of examples took most of the agenda, as we believe it’s key to see how ML is already being applied in many organizations. The first example regarding how ML is used for entertainment was presented by BigML’s CIO, Poul Petersen, who introduced how BigML Deepnets predicted the Oscar Winners when we got 6 out of 6 right. Secondly, we heard Jose Ángel Alonso Cuerdo, Director at KPMG Data Analytics & AI, explain how they design sporting calendars using Machine Learning for the main sport leagues such as the NBA, the Australian Football League, the South Eastern Athletic Conference, and the Atlantic Coast Conference, among others.
We also saw an interesting example of how Machine Learning is being used to help lawyers get the NDA out of their way, presented by Arnoud Engelfriet, Founder at JuriBlox B.V. This topic was in fact one of the most popular talks, getting the attention of many attendees, especially during the first Q&A session. The last talk of the morning was a joint session provided by Jordi Palau, Supply Chain Director at Celsa Group, and Joel Montoy, Director at Aquiles Solutions. Both Jordi and Joel presented how Celsa Group together with Aquiles Solutions have been optimizing all the steps of the End-to-End Supply Chain process, where they plan, source, manufacture, and deliver steel.
After the lunch break, we learned how Machine Learning is used in emerging markets. David del Ser, Practice Director at Bankable Frontier Associates, presented the key role that ML plays for financial inclusion in a world where there are no bank accounts, poor people do not get any bank support for lack of information, and they cannot prove what they earn; however, they do have access to smartphone devices and this changes completely their situation when it comes to improve their businesses and lifestyles.
Another example of ML for social good was presented by Thor Muller, CIO at Off Grid Electic, the African startup that offers electric solutions using Solar energy in Rwanda and Tanzania and uses Machine Learning to predict whether their clients will churn at the end of the cycle.
To conclude this block, we found out how Frogtek applies ML to help “base-of-the-pyramid” Mexican micro-retailers to better control and grow their businesses, gaining in operational efficiency. Guillermo Caudevilla, Chief Technology Officer at Frogtek explained how the retailers register every transaction that takes place in their shops getting easy access to metrics and value-added services fueled by their own and other shopkeepers’ data. All this transactional data is also aggregated and fed into a business intelligence and marketing analytics system that Consumer Packaged Goods companies rely on for better visibility into a traditionally opaque sector.
After the second coffee break of the day we continued with real-world use cases applied in Marketing and Human Resources. For the former, Seamus Abshere, CTO at Faraday.io explained how they take customer data, combine it with a proprietary national database and Machine Learning templates to help other companies acquire, upsell, and retain more customers. This journey on how to make Machine Learning work for B2C revenue optimization was appealing for most attendees as it is an interest shared by many companies.
The last two talks of the first day at 2ML were devoted to Human Resources. Firstly, David J. Marcus, Sr. VP of Special Projects at PandoLogic, shared how Machine Learning is revolutionizing the traditional recruitment procedures, which mainly used professional recruitment firms and advertisements in newspapers. Now, at PandoLogic, ML optimizes recruitment campaign spending in real-time by utilizing over 10 years’ worth of historical job performance data containing nearly 200 billion data attributes. The models work by establishing real-time predictive-performance benchmarks that drive when, where, and how each employer’s job is dynamically campaigned online.
The last speaker of the day was Patrick Coolen, Manager HR analytics at ABN AMRO, who shared the journey of the ABN AMRO analytics team in the past four years and how a big organization like this bank uses Machine Learning to discover interesting insights about their employees. This talk covered why all companies should do analytics in HR in the first place, how to convince senior management to apply ML techniques, how to set up an HR analytics function, and finally, how ABN AMRO uses ML in HR, providing actual examples as well as the practical takeaways of the 10 golden rules of HR analytics.
The second day of the 2ML event, May 9, started with a review of the main concepts shared during the first day. Santiago Márquez, CTO at Barrabés.biz, presented the talk to refresh basic concepts. Then, we continued with more real-world applications of Machine Learning, this time in the finance, investments, and telecom business sectors. Jorge Pascual, CEO at Anfix gave a detailed talk about how the Accounting industry must be reinvented, as by 2020 more than 80% of traditional financial services will be delivered by cross-functional teams that include Machine Learning. Instead of fearing the automation, Jorge Pascual focused on the positive side where ML will make accountants more efficient and productive.
Following the same path, Arturo Moreno, CEO at PreSeries, presented examples of how Machine Learning upgrades technology financing by enabling the data-powered processes, emphasizing how early-stage investment decisions can be better made with data. For instance, a growing number of investors are experimenting around data-driven strategies to early-stage investing, and the names of Social Capital, EQT, GV, or InReach Ventures, to name a few, are already showing results. Here is where PreSeries marks an important inflection point since it allows all investors to leverage the benefits that data and ML represent for the generation of insights. PreSeries believes that a data-centric culture at investing organizations will not only bring faster and better investment decisions, but will also allow investors to be helpful to startups in a much more productive manner thanks to the insights that the analysis of their data will bring.
The last two real-world use cases shown at #2ML18 were about ML used in Blockchain technology and in Telecom. For the former, again Santiago Márquez, CTO at Barrabés.biz introduced the synergies between Blockchain and Machine Learning, how to apply ML to Blockchain and the current status of this approach. The latter, provided by Francisco Martín Pignatelli, Group Head of Radio Product at Vodafone, presented the big challenge that traditional telecom systems are experiencing and cannot manage: the growth of data that telecom customers demand and generate. Pignatelli showcased why Machine Learning is the best option to address this challenge, since it allows networks to be predictive as opposed to reactive, which changes how technology has worked in Radio for the last 25 years.
The last, but by no means least, part of the event was the importance of adopting Machine Learning in all organizations across the entire corporate structure.
Francis Cepero, Head of Vertical Market Solutions at A1 Digital showed in a very interactive manner, why MLaaS Platforms are crucial to accelerate corporate Machine Learning training programs for data analysts and other professions relevant for decision making around data-centric business models. The whole audience had the chance to get to know each other by actively discussing the specific challenges that Francis Cepero was proposing throughout his presentation. This topic was concluded by Luis Martín, CEO at Barrabés.biz, who completed Francis’ point of view by providing the right steps that any company should follow to adopt ML, going from ideas to clear results.
The second edition of 2ML was concluded with a practical Machine Learning workshop provided by BigML’s CIO Poul Petersen, to put in practice the basic concepts learned during this two-day event. Poul Petersen showcased basic Machine Learning workflows and techniques that make ML easier than ever with MLaaS platforms like BigML. To see more details about the event, please check out here a few photos of this complete and fun event! There will be many more photos to be shared shortly as well as the presentations shared by the speakers. Stay tuned!
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