2017 witnessed the meteoric rise of Artificial Intelligence and Machine Learning. From large platform vendors to early-stage startups, AI and ML have become the key focus areas. VCs poured billions of dollars in funding AI-related startups. Platform companies increased their R&D budget to accelerate research in AI & ML domains. The number of online courses offering self-paced learning has hit the roof. Finally, there is no single industry vertical that's not impacted by AI.
Though it has become a clich√?¬©, 'democratizing machine learning' has taken off in 2017. Amazon, Apple, IBM, Google, Facebook and Microsoft are competing with each other to make ML accessible to developers. The availability of tools and frameworks doubled in just one year. 2017 also saw the beginning of AI infusion in business applications.
With the hype at its peak, what's in store for AI and ML in 2018?
Here are three key trends for 2018 that will take AI and ML to the next level.
DevOps for Data Science
A data scientist is defined as an individual who is better in statistics than an average programmer and a better programmer than an average statistician. Data scientists squarely focus on finding hidden patterns in data sets. They apply proven statistical models to modern data sets to solve business problems.
Though data scientists deal with Python, R and Julia to create machine learning models, they are not equipped to deal with the infrastructure and environment required for developing and deploying ML models. During the development phase, ML models will be moved back and forth between local development environments and cloud-based training environments where GPU-based VMs are used for scale. Data scientists need a simple mechanism to perform the roundtrip between local environment and cloud-based environment