Adoption of Machine Learning is growing significantly in business. More and more, the integration of Machine Learning is becoming an integral part of a digital transformation processes that businesses are looking to undergo. The advances in technology and the accessibility of Machine Learning capabilities, like for example TensorFlow or Cloud Services such as Google Cloud AI and operational tooling including Talend have helped combat the skills required to embrace Machine Learning concepts and accelerate delivery of solutions. Previously a discipline associated with scientists in white lab coats, Machine Learning is now becoming an increasingly mainstream activity. So instead of white lab coats you are more likely to find designer jeans and wearable devices associated with today’s emerging army of machine learning developers.
The main reasons for this are the advances in availability and costs of data storage and compute, coupled with more accessibility to Machine Learning capabilities. As a result, this has created the ‘perfect storm’ for organisations to explore how to exploit this discipline of Data Science. The nirvana of enhancing customer experience, accelerating compute intensive processes and enabling new initiatives all drive the adoption of it. However, Machine Learning is still fundamentally about statistical modelling using data (often very large sets of data) – so data is still key.
Many vendors are racing to provide inclusive platforms for embracing and hence democratising Machine Learning which incorporates tutorials, examples and tooling to help manage an end to end enablement experience for developers. Data Science and the disciplines around Machine Learning are in high demand in many organisations which is driving this momentum at the developer level to educate and enable so that alignment with company opportunities or goals can be verified and enacted.
We are also starting to see some innovative applications of Machine Learning in our customer base as organisations start to operationalise solutions as a result of the lowering of many barriers to its adoption. From predicting shopping cart abandonment in e-commerce websites through suggesting next best action (NBA) in gaming and betting platforms, to forecasting supply chain needs based on additional dimensions like weather and key events, our customers are exploring how Machine Learning initiatives can help enhance the user and customer experience, maximise sales conversions and enhance profitability.
One example of this is a global pharmaceutical company, Bayer Cropscience AG that used Machine Learning to find a solution for farmers. Weeds that damage crops have been a problem for farmers since farming began. A proper solution is to apply a narrow spectrum herbicide that effectively kills the exact species of weed in the field while having as few undesirable side effects as possible. But in order to do that, farmers first need to accurately identify the weeds in their fields. By using Talend Real-time Big Data, the company was able to develop a new application that farmers can download for free. The app uses Machine Learning and Artificial Intelligence to match photos of weeds in the company’s database with weed photos farmers send in. Accessible all over the world, the photo database resides on a private cloud stored on AWS. It gives the grower the opportunity to more precisely predict the impact of his or her actions such as, choice of seed variety, application rate of crop protection products, or harvest timing. The result is a more efficient way of farming that increases yield and allows farmers to be more environmentally aware of their actions.
Potential to reinvent
This is just one example of how Machine Learning can transform a business, by enabling success more easily and efficiently than traditional coding-centric approaches. Through enabling Business, Data Science and IT functions to more easily collaborate on Machine Learning projects, Talend eases operationalisation and hence democratisation of Machine Learning initiatives. Due to its open source, standards-based architecture, Machine Learning models can be more easily deployed to business applications and bridge the skills gap that typically exists between data scientists and IT developers.
As adoption and accessibility to this technology increases, Machine Learning will continue to support more and more advanced use cases to help organisations drive new innovations and enhanced customer experiences. Many people now start to talk about Cognitive Computing as the nirvana of Machine Learning whereby systems are able to learn at scale, reason with purpose and interact with humans more naturally. By mimicking the human brain and the way people process and reason information through thought, experience, and the senses, Cognitive Learning promises to help deliver high end applications of Machine Learning such as computer vision and recognition (generally, not of just trained object types), truly intelligent chat-bots, versatile handwriting recognition and more.
Rapid advances in hardware manufacturing is helping to provide the compute power required for such cognitive applications to be available in dedicated chips which help optimise processing and reduce the hardware footprint typically required to support such applications.
It’s an exciting time to be involved in not just the technology industry, but any sector that stands to be disrupted by innovation, and in particular by Machine Learning and Artificial Intelligence. We’ve learned recently that the UK Government is set to inject £115 million funding into AI University Courses, in a move to keep pace with the U.S. and China. AI and therefore Machine Learning is poised to be the most significant technology for a generation but it’s widely acknowledged that there aren’t the skills in place to reap the full benefits. The skills gap is nothing new, but it does continue to evolve as new technologies become more sophisticated and it is something that will always be at the top of the agenda and have to be tackled as the workforce becomes increasingly digitally focused.
For all of the reasons discussed here, it’s clear that Machine Learning has the potential to reinvent a myriad of business processes, and we are seeing some of those applications now. However, as the technology evolves, applications will become more sophisticated and drive businesses forward in a way that previously could not have been imagined. I for one am truly excited to see how Machine Learning adoption develops and is able to affect change in the enterprise.
Darren Brunt, Pre-sales Manager for Northern Europe, Talend
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