Machine learning has developed to the point in which it moved from being a very experimental piece of technology to being blueprinted in many business sectors, ranging from software development to eCommerce and more. Machine learning is quite a broad subject and it's hard (at its current stage of development) to dissect it in detail, given the reasonably powerful investments which have been done from both a business and technological perspective on this very matter. With this being said, picturing the state of machine learning in 2019 is still possible. Let's analyze what are the strengths and weakness of what could be described as a technological phenomenon that has been reshaping the market in the past 4 years.
The Power Of Data
Machine Learning in 2019 is mainly associated with data: from data warehousing protocols (mainly applied to complex architectures like AWS and related) to data science, the connection of Python tools and data gathering is tangible and pretty strong, also, from a business perspective. In 2018 the figure of the "data scientist" was the most requested within the technological job market, surpassing software developers, web developers and niche-targeted developers.
Data is extremely important in today's business era: tailoring a specific piece of content, from a marketing perspective, by following determined data guidelines is a highly competitive procedure which determines the success of a variety of departments.
A Very Startup-Related Matter
In the past 4 years, 75% of the world's technological development has come from startups, who amounted for billions of dollars in terms of investments and general technological awareness. When it comes to machine learning, specifically, startups have been extremely successful in combining automated features to tools and architectures which weren't automated at all: with a big example being Alibaba's recent "automated warehouse", completely outsourced in terms of software to a small machine learning development team in China, it's safe to say that pretty much everything machine learning-related is in the hands of startups worldwide.
Mobile: The Future Of Automated Technology
What's known today as "the race for automation" is being embraced by a vast variety of sectors, and mobile is one of them. To reference, in the UK, some app developers have recently opened their gates to in-house Python developers with the ultimate goal of developing automated features that are responsive, intelligent and ahead of times to overcome their competitors both from a building perspective and from, most importantly, a business one. Mobile traffic last year peaked at over 58%, so it's quite easy to understand why many will look to implement a variety of automated features in their development plans, whether if ML-based or not.
Once again, outlining the current state of machine learning is very complex, given the number of variables which are currently into place when talking either development and business in general/investments most importantly. It's safe to say that startups will dominate the ML market in the next couple of years, as they are building the foundation in regards to the future of ML and automated features in general. Mobile ML will become an "industry standard" with smartphones becoming more and more powerful every year, from a hardware perspective and, finally, data science will become a standard procedure even in small and medium-sized businesses.
Paul Matthews is a Manchester-based business and tech writer who writes in order to better inform business owners on how to run a successful business. You can usually find him at the local library or browsing Forbes' latest pieces.