There is a wave of ready-to-use AI/machine learning tools and more launches every day. Access to do-it-yourself tools allows all kinds of businesses to experiment and start leveraging their data. This, of course, is great for industry and business. However, as we discussed earlier, without quality data and ongoing support, this can be problematic for DIYers. Companies want to run their own program, but they rarely have the power to organize and process training datasets. This can sometimes result in small or insufficient datasets and ultimately bad models. This is where a good data support partner can provide both perspective and scalable support to help lead from behind.
There's an old adage among researchers: the more questions you ask, the more you realize you need answers. As companies seek to build Color Correction Service increasingly complex machine learning programs, they will continue to find that the datasets they had and used to get started just aren't enough. AI logic will continue to grow. The more mature the industry, the greater the knowledge of the data we don't have. While this isn't unique to AI or machine learning, I think we're at a point in history where people are reassessing their perception of their business, their customers, and their community. The assumptions and expectations that were the backbone of existing products, programs and strategies are all being reassessed. Now is the time for companies to look at existing and future AI and machine learning tools with fresh and inclusive eyes. It used to be optional, but now it's expected and companies that don't scale will be left behind by consumers who have irreversibly raised their expectations.