We have entered into the last month of the year that marked the entrance into the AI industrialisation decade in which the majority of companies are expected to harvest the fruits of pilot projects and see them into development.
The start of the AI industrialisation decade was unfortunately met with an unprecedented and catastrophic global pandemic that made us question the trustworthiness of the AI systems we were so proud of. The uncertain reality we have been living in 9 months already, exhibited the faults of our models and algorithms that proved so successful in controlled organisational conditions.
And yet, we were once again reassured by the predicaments that warped our lives, that data will provide the right answers even in times of uncertainty. As we started working remotely and making sure business processes run smoothly from a distance, it became clear we must abandon gut feeling and adopt a data-driven approach. Companies that had made successful inroads with data had a leg up on those who had been reserved towards it.
Nevertheless, we are thankful that organisations advanced in the data realm have selflessly and openly shared their success stories and inspirational use cases with us. At Hyperight, we are always devoted to giving the floor to organisations eager to unveil their stories at our event platforms.
In this regard, we’d like to mark 2020 by doing a recap series covering the stories of organisations who have spearheaded their data, AI and ML transformations, that were published this year, and left a distinct impression in the field.
These are some of the companies that excelled in the art of innovating through data, AI and ML – all of them success stories from which we can learn real and valuable lessons.
How Klarna personalised the checkout experience with data and ML
Based on the presentations of an engineer and a data scientist, we learn how Klarna is able to simplify their online payment and checkout experience for both customers and merchants and act as a bridge between consumers and e-commerce websites.
Erik Zeitler, Former Lead Engineer at Klarna and Deepa Krishnamurthy, Data Scientist at Klarna, brought us closer to the data architecture, and machine learning models that help them understand customer paying behaviour and enable their smooth checkout experience.
How Aldo Group reinvented its business processes relying on data
In the fast-paced retail world, data is the blood of accurate and informed decision-making. Considering that retailers gather huge amounts of customer data, from various channels, it’s of great importance to have sound data management and governance practice in place in order to successfully reinvent your business.
A great example of how to do it right came from David Dadoun, previously Senior Director Business Intelligence and Data Governance at the Aldo Group, in a talk on the topic how the Aldo Group leveraged data to achieve operational excellence.
David Dadoun shared insight on 3 key data-driven projects within the Aldo Group which helped to achieve operational excellence, anchored in a sound data governance framework which they had previously established.
“No good AI, analytics, BI, machine learning project is going to work if it’s just a technology-driven initiative. It needs to be addressing a business opportunity or solving a business problem”, David stressed.
Why and where The LEGO Group deploys machine learning models
Perfect proof that a brand can conquer the global market and the hearts of its customers with the help of data and AI is the LEGO Group. LEGO Group’s story was told by Francesc Joan Riera, Applied Machine Learning Engineer, Data Engineering at The LEGO Group.
Francesc demonstrated how they train, maintain and update deployed machine learning models for LEGO’s Moderation Service at the Data Innovation Summit. Beforehand, he did an interview in which he opened up about his beginnings with machine learning, how he found his place in the machine learning world and how The LEGO Group does the cool stuff with ML. More specifically, he described that they had been investing in improving the customers’ digital experience by means of AI techniques to provide better and safer play experiences for children all around the globe, coupled with an overview of the LEGO Group products that use/or are based on machine learning techniques.
Innovation in times of a pandemic: How to leverage the Corona effect
A recap of 2020 can’t go without an article on COVID-19. In this case, the Corona butterfly effect on companies’ AI efforts, as well as positive outlooks in how companies could leverage the pandemic to innovate in times of need.
The article summarises a virtual roundtable on the topic The Corona Effect – Evolution through Crisis. Impacts on Data, AI & Digitalization, moderated by Goran Cvetanovski, Founder & CEO of Hyperight, in which we could hear Henrik Göthberg, Founder at Dairdux; Patrick Couch, Business Developer AI & IoT at IBM; Diego Galar, Professor at the LuleĂĄ University of Technology and Anders Arpteg, Head of Research at Peltarion & Steering Member of AI Innovation of Sweden share their views, concerns and proposals.
How to know when you’ve succeeded with digitalisation and AI: Stena Line
The account on Stena Line’s digitalisation builds on the event from 2018 when they had introduced the first AI-assisted vessel in an effort to lower fuel consumption and CO2 emissions.
One year later in 2019, Amer Mohammed who was at the moment Head of Digital innovations at Stena Line, recounted what is needed to succeed in your digital transformation initiatives and make your organisation AI and data-driven. The key is, as Amer suggested, looking back to digitalisation actually means and what the end-game is.
“Digitalisation is not analytics, it is not a data lake, it is not moving to the cloud, and it’s not hiring a Head of Digital,” stated Amer. “Digitalisation is the journey to become autonomous,” affirms Amer. The end game of digitalisation is to become autonomous; everything else you do from technology, people and resources are just means of getting there.
Saving lives from train accidents with machine learning and IoT
Machine learning has a proven track record of advancing our lives, whether it is automating tasks and processes, gaining valuable insights out of massive quantities of data and enabling us to take the most effective data-driven actions.
But machine learning and IoT also play a crucial role in saving lives from train accidents, as well as identifying and preventing suicides on train tracks. Victoria Chudinov, Former Data Scientist at DSB Digital Labs, shared from a project to prevent suicidal behaviour and accidental collisions on the railway track enabled by machine learning and IoT.
In her interview, she gave us a sneak peek into the DSB Digital Labs project, and the range of solutions from simple sensor-triggered devices, to complex machine learning-driven solutions that detect and prevent suicidal behaviour.
How Uber delivers exceptional customer experience with ML
Brands around the globe, big or small, are giving it all they got, going over and beyond for their customers. They know that their business is as successful as their least satisfied customer.
When your business is serving customers in 6000 cities, in 64 countries and millions of riders, you have to make sure you live and breath customer satisfaction. Uber, the global tech leader in ride-sharing, can teach us a thing or two with how they manage to keep all their riders and drivers happy.
Ritesh Agrawal, Former Tech Lead Manager at Uber and Anando Sen, Product Lead for Experimentation and AI/ML Platforms at Uber, showcased how they leverage machine learning to work to delight their customers. They focused on one of the crucial applications of AI and machine learning at Uber — detecting and resolving user experience incidents in order to make sure their app is up-and-running and reliable at all times.
Vehicles as data platforms combining human and sensory behaviours
Humans and machines are more and more interconnected in every aspect of life. We are witnessing the development of eco-systems for which we need both human experience and data to optimise and make more efficient use of. This is the example of General Motors’ cutting-edge vehicle intelligence platform which transforms vehicles into actual data platforms with vehicle data that can be imagined as a combination of human and sensory behaviours.
Meltem Ballan, Data Science Fellow at General Motors, captured this subject in her talk at the Data Innovation Summit titled Vehicles as data platforms: Telemetry meets vehicle identity.
In her interview, Meltem offered her sincere view, starting with her personal story, her relationship with AI, ML, NLP and deep learning on one hand and cognitive and behavioural neuroscience on the other, telemetry, data privacy and ethics, as well as what we can expect in the future for vehicle data platforms.
AI in the energy sector: How neural networks can improve plant operations
Although AI is in the early stages of implementation in the energy sector, promising AI projects are being developed in plants that are already showing significant results. The energy industry has recognised the potential of AI to transform and improve energy systems.
One such energy company that put trust in AI is Uniper, a global energy supply company based in Düsseldorf, Germany. Tobias Mathur, Head of AI Operations at Uniper, talked to us about Uniper’s breakthrough work with AI and neural networks, as well as AI maturity in the energy sector.
Stimulate ML Development using Feature Store
Jayesh R. Patel, Sr. Data Engineer at Rockstar Games, presented on the topic Scalable Big Data Modeling at the Data Innovation Summit. As part of his participation, Jayesh contributed his own insightful article on ML application development using Feature Store.
As more and more enterprises are jumping on the ML boat to gain a competitive edge, it would be fruitful to share some of the best practices to consider while strategising ML vision. ML journey may not be smooth as pictured in theory. A variety of data sources are accessible in enterprise big data lake. However, ML application development can be time and resource-intensive. Data scientists and ML developers still spend most of their time in data cleansing, feature extractions, and modelling for ML applications repeatedly.
To ignite ML Development and conquer these challenges, one of the effective ways is to centralise ML features and serve whoever needs them in a unified way. Sharing cleaned and processed ML features will not only save time but also leads to better predictions. That is the central idea of ML Feature Store.
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These are just some of the amazing case studies by forward-oriented companies that have successfully innovated through data, AI and ML. See more of our 2020 archive here.Â
The next recap will be focusing on predictive maintenance and analytics and organisations that excelled in this area. Until the next time!
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