Trends of ML adoption in software development
Before we delve into the workings of the MLOps method and workflow, it is beneficial to understand the big picture and trends as to where and how MLOps is disrupting the world. As many applications are becoming AI-centric, software development is evolving to facilitate ML. ML will increasingly become part of software development, mainly due to the following reasons:
- Investments: In 2019, investments in global private AI clocked over $70 billion, with start-up investments related to AI over $37 billion, M&A $34 billion, IPOs $5 billion, and minority stake valued at around $2 billion. The forecast for AI globally shows fast growth in market value as AI reached $9.5 billion in 2018 and is anticipated to reach a market value of $118 billion by 2025. It has been assessed that growth in economic activity resulting from AI until 2030 will be of high value and significance. Currently, the US attracts ~50% of global VC funding, China ~39%, and 11% goes to Europe.
- Big data: Data is exponentially growing in volume, velocity, veracity, and variety. For instance, observations suggest data growing in volume at 61% per annum in Europe, and it is anticipated that four times more data will be created by 2025 than exists today. Data is a requisite raw material for developing AI.
- Infrastructural developments and adoption: Moore's law has been closely tracked and observed to have been realized prior to 2012. Post-2012, compute has been doubling every 3.4 months.
- Increasing research and development: AI research has been prospering in quality and quantity. A prominent growth of 300% is observed in the volume of peer-reviewed AI papers from 1998 to 2018, summing up to 9% of published conference papers and 3% of peer-reviewed journal publications.
- Industry: Based on a surveyed report, 47% of large companies have reported having adopted AI in at least one function or business unit. In 2019, it went up to 58% and is expected to increase.
Information
These points have been sourced from policy and investment recommendations for trustworthy AI – European commission (https://ec.europa.eu/digital-single-market/en/news/policy-and-investment-recommendations-trustworthy-artificial-intelligence) and AI Index 2019 (https://hai.stanford.edu/research/ai-index-2019).
All these developments indicate a strong push toward the industrialization of AI, and this is possible by bridging industry and research. MLOps will play a key role in the industrialization of AI. If you invest in learning this method, it will give you a headstart in your company or team and you could be a catalyst for operationalizing ML and industrializing AI.
So far, we have learned about some challenges and developments in IT, software development, and AI. Next, we will delve into understanding MLOps conceptually and learn in detail about a generic MLOps workflow that can be used commonly for any use case. These fundamentals will help you get a firm grasp of MLOps.
