There are some startups, such as Deepmind and OpenAI, that have had research as an absolute core part of their organization from the getgo and have become successful in their own ways (e.g. getting acquired by Google or pivoting towards a more commercial framework by offering massive AI models such as GPT-3 behind paid APIs). However, trying to emulate these is not a good idea if you’re a startup pitching to investors. …
There’s a megatrend underpinning the advent of the data age: the rise of the data engineer, the fastest-growing job in tech right now. We believe that data engineers are the unsung heroes and the change agents in a decade-long process that will revolutionize data.
We decided to gather some of the most significant moments from the past two decades that shaped the rise of the data engineer in an infographic illustrating the 2000–2020 timeline.
You can access the high-resolution version of the timeline here.
In a survey by O’Reilly from 2019, 26% of the respondents with a mature machine learning practice stated poor data quality as the nr 1 bottleneck holding them back from further AI/ML adoption.
The accelerated digitalization and our ever-increasing appetite for and generation of data fuelled a lot of development in the Data + ML landscape in 2020. As companies have started to reap the benefits of the last few years’ predictive analytics and ML initiatives, they clearly show a healthy appetite for more in 2021. “Can we process more data, faster and cheaper? How do we deploy more ML models in production? Should we do more in real-time?” … the list goes on. We’ve experienced an amazing evolution in the data infrastructure space during the past few years. Data-driven organizations have moved…
Early-stage tech investor. Preaching about the realities and possibilities of ML and Data. Former Google. Current Validio, J12 Ventures and Mumintroll.