Machine learning and artificial intelligence are both driving significant developments in the automotive industry. Machine learning in automotive is primed to take off dramatically, which means companies in this industry will need help operationalizing machine learning initiatives. However, that can be a big challenge because machine learning in automotive industry circles has not quite taken off yet.
AI ML in automotive industry circles needs a lot more maturity, as there are only a few companies like Tesla doing a lot of work in this field. There will need to be many other companies joining the ranks of Tesla to be successful. However, there are certain things companies can do right now to make the process of getting ready for a future with machine learning at the center more quickly. The first thing they need is to understand MLOps and how utilizing this new way of doing things in the industry can save them time and money.
Autonomous and Always Connected Cars
Technology will enable a car in the future that is semi-autonomous and always connected. That will require the car to navigate along the road by itself. However, companies will need to understand the art and science of operationalizing machine learning to get that done. Machine learning in automotive industry circles will have to get much better.
AI ML in automotive industry circles currently isn’t good enough for that, but it is slowly changing with plenty of technologies being slowly integrated into what companies are doing. The foundation of that is more processing power being integrated into the vehicle. That will allow the vehicle to do more data processing locally, but it also presents more issues that need to be talked about. If operationalizing machine learning is to be successful, it will require car companies fundamentally rethink how they do business in some ways.
Data Governance and Locality
The big issue at play here is with data governance and doing things locally. Machine learning in automotive requires powerful graphics processors to be integrated into the vehicle. The graphics processor will be required to achieve autonomous driving at any level. However, operationalizing machine learning becomes quite difficult because you need data that will be streamed across the airwaves.
It is difficult to have data close to the source where it is processed because of the security implications. A car is not a secure server in a data center somewhere. It is something that can be hacked easily if you have physical control. If you want to make things happen faster, you will have to move data close up the car, which is why you need to think about how to do data governance smartly and securely.
5G and a Mobile Future
Another issue at play is 5G and a Mobile future we are working with. 5G is quite fast, which means you will be able to operationalize machine learning in automotive quite easily. However, AI ML in automotive industry circles will need to change because they require new thinking about security and using that processing power more efficiently. However, the big news is that machine learning in automotive industry circles has brightened things up, and it will make the world a better place for the automobile.