The Age of Machine Learning As Code Has Arrived
The 2021 edition of the State of AI Report came out last week. So did the Kaggle State of Machine Learning and Data Science Survey. There's much to be learned and discussed in these reports, and a couple of takeaways caught my attention.
"AI is increasingly being applied to mission critical infrastructure like national electric grids and automated supermarket warehousing calculations during pandemics. However, there are questions about whether the maturity of the industry has caught up with the enormity of its growing deployment."
There's no denying that Machine Learning-powered applications are reaching into every corner of IT. But what does that mean for companies and organizations? How do we build rock-solid Machine Learning workflows? Should we all hire 100 Data Scientists ? Or 100 DevOps engineers?
"Transformers have emerged as a general purpose architecture for ML. Not just for Natural Language Processing, but also Speech, Computer Vision or even protein structure prediction."
Old timers have learned the hard way that there is no silver bullet in IT. Yet, the Transformer architecture is indeed very efficient on a wide variety of Machine Learning tasks. But how can we all keep up with the frantic pace of innovation in Machine Learning? Do we really need expert skills to leverage these state of the art models? Or is there a shorter path to creating business value in less time?
Well, here's what I think.
The 2021 edition of the State of AI Report came out last week. So did the Kaggle State of Machine Learning and Data Science Survey. There's much to be learned and discussed in these reports, and a couple of takeaways caught my attention.
"AI is increasingly being applied to mission critical infrastructure like national electric grids and automated supermarket warehousing calculations during pandemics. However, there are questions about whether the maturity of the industry has caught up with the enormity of its growing deployment."
There's no denying that Machine Learning-powered applications are reaching into every corner of IT. But what does that mean for companies and organizations? How do we build rock-solid Machine Learning workflows? Should we all hire 100 Data Scientists ? Or 100 DevOps engineers?
"Transformers have emerged as a general purpose architecture for ML. Not just for Natural Language Processing, but also Speech, Computer Vision or even protein structure prediction."
Old timers have learned the hard way that there is no silver bullet in IT. Yet, the Transformer architecture is indeed very efficient on a wide variety of Machine Learning tasks. But how can we all keep up with the frantic pace of innovation in Machine Learning? Do we really need expert skills to leverage these state of the art models? Or is there a shorter path to creating business value in less time?
Well, here's what I think.