October 2019 was a big month! After the two exciting conference opportunities, ApacheCon Europe and Data Leaders Summit, in Berlin, I flew to Barcelona, Spain and joined IoT Solutions World Congress 2019 (IoTSWC).
As a data science engineer who has developed digital data-centric products, I learned a lot from the IoT conference to see a big picture of the industry mixing the physical and digital world.
For me, there were three particularly important findings:
- Everyone is suffering from the gap between PoC and its productization.
- Machine Learning (ML) is everywhere, and it's not THAT special anymore.
- Blockchain is becoming a key piece of modern IoT systems.
Biggest Challenge: PoC to Productization
PoC-ish initial implementation was done in a couple of months, but a project team is now spending 2-3 years to make the PoC implementation production-grade.
Sounds like such a situation is very likely to happen in real-world IoT projects.
Thus, some conceptual talks and panel sessions equally mentioned how to overcome the gap by building a solid team and effectively leveraging the resources & surrounding technologies. Across the entire conference including Expo, the audiences were actively looking for third-party solutions and partners to accelerate time to value.
I was surprised what my colleague, who has been working in the IoT industry for a long time, said — in fact, people have continuously discussed the same topic for 5-10 years. It means that, even though many disruptive technologies have arisen such as ML, Blockchain, AR/VR, and 5G, they still cannot resolve the essential challenge due to the difficulty of massive, reliable deployment of realistic IoT applications.
As many of us believe, IoT is an are of technology having huge potential impact. However, we need to bear in mind that building a real-world IoT solution is somewhat different from conventional software projects.
Commoditization of ML-Enabled IoT Products
I'm sure ML gets mature in the IoT field, since most talks I've heard naturally and seamlessly introduced the use of ML as a part of their entire story; nobody just introduces their "fancy" ML models, explains mathematical details, and states basic things like What is ML? and Why these technologies are so important.
It does make sense for me for the following reasons.
First, IoT sensor data can be super simple compared to the other complicated data types (e.g., web access log, customer attributes, video, audio), and it is relatively easy to analyze the data, build prediction models, and make the systems scalable. For instance, a temperature sensor gives a series of floating-point numbers, and the data does not contain any text or multimedia information. Hence, the numbers can be readily modeled even by a simple model such as linear regressor, and I guess that's the reason why ML is so widely used in the field of IoT.
Next, leveraging ML techniques is a great way to obtain meaningful outcome as fast as possible and accelerate time to market. ML helps IoT developers a lot to automate end-to-end data flow and quickly gain actionable insights, and it eventually makes the situation that I mentioned above (i.e., the gap between PoC and productization) better. The algorithm itself doesn't necessarily have to be state-of-the-art, and prediction simply makes the system less human independent.
Therefore, when you see something about IoT, ML should play a very fundamental role in their systems, and nowadays, the technology is not as new as the other trends like Blockchain.
Blockchain in IoT Systems
- IoT solution requires massive real-world deployment consisting of geographically distributed humans and devices.
- Solution providers realized the importance of data, and hence digitization is one of the most important goals in the IoT industry.
- GDPR has been executed in the EU, and data-intensive application needs to be careful about privacy tied to the individuals.
If you think about these facts, it is obvious that Blockchain is an attractive, key concept that ensures transparency and reliability of complex data flow.
To be honest, I didn't expect to see so many Blockchain talks at the conference; there was a special series of Blockchain sessions named Blockchain Solutions World.
It seems the supply chain management is one of the most important areas Blockchain works effectively. Thanks to the technology, products, and people involved in different phases of the supply chain become more transparent, and IoT application running on top of the chain ensures an outstanding degree of data visibility and reliability.
As far as I saw, the combination of IoT and Blockchain is still at a phase of exploration in contrast to ML. However, thanks to the IoTSWC sessions, I can now easily imagine the use of technology has become more and more common for the next few years.
IoTSWC is a great conference where you can efficiently learn industrial trends and its real-world use cases. In particular, for those who have recently joined the IoT field like me, it'd be worthy to spend your time and money for this opportunity.
As I described so far, keywords representing this year's IoT trends include "time to value," "digitization," and "Blockchain." Excited to see how this field grows from now on.
Finally, it should be noted that Barcelona was a nice city, especially in terms of food and beverages :)
- What Blockchain Brings to Real-World Applications
- Why a Data Science Engineer Becomes a Product Manager
- ApacheCon 2019 North America #ACNA19 & Europe #ACEU19
Author: Takuya Kitazawa
Takuya Kitazawa is a product developer, minimalistic traveler, ultralight hiker & runner, and craft beer enthusiast. Throughout my career, I have practically worked as a full-stack software engineer, OSS developer, technical evangelist, sales engineer, data scientist, machine learning engineer, and product manager.
Opinions are my own and do not represent the views of organizations I am/was belonging to.