Nowadays the term artificial intelligence (AI) has become synonymous with "technology of the future." Since 2012, when the neural networks trounced the ImageNet image classification challenge, machine learning has enabled extraordinary advances across diverse domains such as vision, translation, and speech recognition.
We have seen a widespread democratization of the knowledge needed to get started in AI. Cheap consumer hardware, easy access to datasets, and the prevalence of powerful open-source frameworks such as PyTorch and TensorFlow have significantly reduced the barrier to entry.
It has become clear that AI is going to transform the fabric of society in ways never seen before.
The electricity is in the air both within academia and industry. Attendance at AI conferences has increased at unbelievable rates, with some venues seeing 800% growth since 2012. Funding for AI startups has been growing at an average annual rate of 48% since 2010. In the US alone, the share of jobs in AI-related topics increased from 0.26% of total jobs posted in 2010 to 1.32% in October 2019, a nearly 500% increase in under a decade
This exponential growth has brought with it a tremendous global demand for AI talent across all disciplines including financial services, manufacturing, education, and agriculture.
The technology is ripe, the problems to solve are there, and the demand is high. All that is great. But one central question remains: for newcomers to the field today, what does it take to kickstart and ultimately succeed in an artificial intelligence career?
Artificial intelligence, as a field, is undergoing a maturation process. The artificial intelligence roles of today and the next 5-10 years will require not just machine learning chops, but also a deep and extensive set of practical skills.
Whether the title is data scientist or machine learning engineer, experts in AI jobs will need to be comfortable with:
1) Mathematical concepts such as statistics, probability theory, and linear algebra
2) Architecting data processing pipelines
3) Designing and implementing statistical models
4) Developing infrastructure for evaluating models and computing metrics
5) Rolling out systems in production where they can service web-scale customer traffic
The variety and versatility of skills required make AI practitioners of today analogous to full-stack web developers. And yet there is no tool available to help newcomers learn and master all of these skills.
That is why we are excited to announce Confetti. Drawing on a decade of experience in artificial intelligence and hundreds of hours of discussions with experts in the field, our platform is designed from the ground-up to prepare people for future success in artificial intelligence.
We are curating the largest bank of machine learning and data science questions out there, focusing specifically on both conceptual understanding and practical applications. Confetti will serve as the educational hub to access targeted resources and gain all the skills needed to not only excel in interviews but also in the careers that come after a job offer.
Over and above all we are designing Confetti with your success in mind. We want to help you become an expert practitioner, a master of data, modeling and engineering ready to tackle all flavors of problems with an AI skill set.
We're now at a turning point in the artificial intelligence roles of the future, and we are thrilled to share what we've been building. If you're interested in pursuing a career in machine learning or data science, get in touch!