This article will share my experience working closely on AI ML projects that used Python data science applied explicitly to the food manufacturing industry and processing businesses.
Have you ever thought of how your python data science experience could help organizations build advanced Artificial Intelligence (AI) and machine learning models that support smart food manufacturing processes and restaurant businesses? We have made some remarkable innovations in recent years from food production to processing to now delivery using robots and drones.
But first, why do we see only Python?
Why not any other programming language? I know of at least ten other programming languages that data scientists can co-build and co-developed to streamline a machine learning project.
Well, the answer is simple and very generic. Every data science professional knows the reason behind using Python for their ML projects. Python is available open-source, and its general-purpose nature allows it to be applied to solve complex daily life challenges through ideal coding benchmarks. That’s why we find every other ML company using Python data science to accentuate their capabilities. To name a few, we have Google, NASA, Spotify, Netflix, Uber, Zomato, and Samsung – all use Python at one of the other projects.
In a very high-profile AI Machine Learning conference held in Bangalore back in 2018, I heard this: “Smart technologies bring Smart changes. When smart changes happen to the industries related to food, water, and shelter, we can expect smart technologies to be more trustworthy and reliable in their true sense than just a set of shiny marketing objects seeking glorification.” That truly opened up my thoughts about the role data science could play in the fields of agri-food, retail F&B, and sustainable energy, all coming together to solve the most pressing issues related to global poverty levels, malnutrition, and food wastage.
Let’s evaluate the various ways the food industry currently uses Machine Learning Python applications to improve product quality and achieve efficiency.
Food Market Analysis
A good mentor or a data science trainer will tell you everything involving food industry standards. It’s a challenging yet productive time to embark on a data science journey within the food industry.
Did you know the world wastes trillions of tons of edible food every year?
In Germany alone, 12 million tons of food end in the dustbin every year. In India, it’s close to 100 million! Now, imagine if a system was available to the food industry that could predict this volume of ‘wastage’ and somehow alerted the “culprits” against food wastage. What would have happened? Not a single family in the sub-Saharan region would ever go hungry for one day’s meal. Never…
Ten million people die of starvation-related problems in the world. So, food market analysis becomes a big responsibility for not just commercial food delivery outlets like restaurants but also for domestic consumers and government agencies to ensure food products can be somehow salvaged, re-packed, and repurposed for use with efficient plant-based systems.
Optimized Food production
AI, primarily developed on Python platforms, has been gaining prominence in the last few years for its ability to minimize overproduction and avoiding waste at the plant levels itself. There are various reasons for wastage at the production site. One of them is related to non-optimized raw material controls and fluctuations in quality requirements. In addition, the local norms often lead to wastage of food products if they fail to meet the needs. Tolerance levels are tight here. Python-based AI ML systems can help food-producing units optimize their scales at various levels of the supply chain – farm, factory, sale to a supermarket, and finally at consumer’s dining table.
The whole in-dine versus take away economy standards shifted because of data science.
Due to the rise of customer engagement across all channels and the phenomenal growth of social media as a mass media channel, we can expect AI ML to play a critical role in the transformation of the business. The food industry is among the top adoption centers of data science to understand customer behavior, mobile app engagements, chatbot interactions, and sales forecasting. All leading food businesses, including KFC, McDonald’s, Subway, Starbucks, and others, have large business analyst teams that mine data with data science teams and analyze, monitor, and deduce how consumers would behave in a given situation menu card online versus physical experiences.