“Study the past if you would define the future.” Confucius
by Chris Sachs, on Jun 29, 2017 4:02:28 PM
There are lots of IOT startups out there. Too many. Silicon Valley hasn’t changed – if you throw enough mud at the wall, some has to stick, right? For example, there are over 400 IoT software platform vendors competing for developers to adopt their frameworks to develop cool new IOT apps. Good luck with that, folks. Just ask Pivotal (the Cloud Foundry folk) how hard it has been to build a community of developers focused on its platform. Salesforce faced a similar battle when it acquired Heroku. And then there are the big guys, who effectively suck the remaining oxygen from the room: AWS, Microsoft, Google, and perhaps Apple. Fighting for developers is a losing strategy for a startup. It’s a one-way street that ends in the oft-repeated fight to the bottom: “If we open source it, they will come”. Sadly, once the tech has been open sourced, if it is any good it will simply be offered as a service by one of the big clouds, or adopted by competitors with more funding.
Then there are the containerization/orchestration plays. Rather than compete for app developers, these folks compete for the future app lifecycle managers – the “Ops” folk in “DevOps”. Let’s be generous and call this the “Docker/Kubernetes/Mesos ecosystem” – unfortunately few of these companies are able to pay their bills. Here again open source dominates, for sound reasons: customers don’t want to bet on a proprietary framework for app lifecycle management, and there are many great reasons for all players to invest developing transferrable skill-sets for the DevOps-ers building the next generation of cloud-native apps.
But is my firm belief that in the Industrial IOT domain at least, neither micro-services based app development nor DevOps can transform the industry to deliver value to customers. Applying these (powerful) concepts from cloud-native Apps and cloud based IT to IIOT projects will not make them successful. In fact, they are perhaps more frequently responsible for the frequent failure of IIOT projects. The Ops people who are relevant in IIOT are Operations Technology (OT) Pros, struggling to keep automated production systems running, and who are unfamiliar with new-fangled DevOps concepts.
There’s another category of Silicon Valley “innovation” that is unlikely to deliver value, namely IOT Gateways/IoT Routers. Silicon Valley has many of these multi-protocol network widgets that aim to simplify getting your IOT data to the cloud. Just as the inhabitants of the mythical tower of babel could speak lots of languages but not much made sense, the concept of an IOT Gateway is a cop-out. A device that can talk lots of protocols might be able to talk to your sensors, grab the data and transmit it to the cloud. What then? Big-data and complex app development and ops problems. IOT gateways just make delivering value from the data someone else’s problem. Back to square one.
The paradigm has changed, again
App development and DevOps will struggle to deliver value at the rate at which opportunity arises because there quite simply aren’t enough technologists with the required skill sets to do the job. But rapid advances in machine learning technology can help:
- Learn the insights automatically, to short-cut app development
- Learn about the deployment context to automate ops
Swim is committed to transforming business outcomes in the IIOT domain using machine learning at the edge – right where the data originates. Machine learning at the edge is key to delivering value quickly and repeatedly in the IIOT domain.
Machine Learning (ML) can help – and we at Swim are confident that it can transform the success of pilots and large production deployments. It’s a big claim, but our confidence is rooted in our continued success in customer engagements. Specifically,
- Successful adoption of IIOT solutions is not an app development problem, it’s a data problem. By learning automatically on even opaque, brownfield data, Swim can shortcut the application development problem by delivering high level insights automatically – just by learning on the data. And lest you worry about the additional complexity of needing domain experts to train machine learning models, we can avoid that too: Swim edge learning is self-training. Its algorithms continually check their hypotheses against real world data, training and adjusting for over fitting as needed. This avoids the need to label training data and avoids any need for machine learning expertise in the OT environment.
- Machine Learning at the edge is cheap! Swim can learn as much on a device that costs a few hundred dollars at the edge, as a solution costing thousands of dollars per month, in the cloud.
- Machine Learning can simplify adoption by using learning to automate operations, administration, maintenance and security. Swim uses learning to self-configure and manage, reducing IT/OT cost, training and complexity.
Learn more about how SWIM is able to help you gain actionable, real-time business insights from your IIOT data using machine learning at the edge.