Currently, the big buzz about big data is probably apt with the number of technologies and tools available to build products and services. Uber, Google, Microsoft, and now Apple are implementing AI to their core business operations to provide real-time AI services in their ecosystem.
I personally believe once due to this success of big data companies, the hype behind AI has blown out of proportions. All businesses are keen to implement some sort of AI technologies to automate processes or at least keen to test the litmus paper.
Andrew Ng, one of the leading pioneers of AI has suggested that the next big innovation in AI will come in the non-software industries, where there might be a huge potential for the market ( estimated 13 Trillion). Advances in AI is also paving way for small data, open frameworks, and auto-ML ( which might eliminate data scientists in a way)
I have always been passionate about statistics and AI since highschool, and this led me to take up Masters course in Data Science and then subsequently take up a role in a real estate agency (SME). While I was able to deliver high-quality analytics by merging and mixing a lot of excel and CSV's, it quickly became apparent that this is not scalable to run in the future. While I become good to clean and visualize data, I am barely being able to touch the AI waters, as we needed a better data pipeline system to deliver the results week in and week out.
Real Estate agencies do not have many data scientists or even analysts( non in Australia), so there is no reference at the moment. Though software services are promising such AI features, I personally do not believe AI in one system can be effective. Our agency uses 17 different systems with data sources being in different formats and sizes. However, I believe there is a huge opportunity for the business to merge concatenate all these sources and deliver much faster information to clients. Real estate has a large sum of data and a huge opportunity as housing is probably the bedrock of the Australian Economy. Here is one process flow to achieve AI, which I picked up during a talk.
I joined team data science to learn data engineering as a discipline. Earlier, I tried studying data engineering using online certification courses, but I believe they aren't helpful if you do not do a project or practice with the tools. Though I was going to write a post sometime back regarding my journey when I started the course( almost 4 weeks back), I believe my situation was unique. I already had huge amounts of datasets and problems, but I needed a path to follow. Thanks to Andreas, I found that path, and the right amount of workload to learn the skills.
Will update you more on my project soon.