Innovation in ML and Retail Part 2
Welcome to the 2nd part of the multi-part blog series by AICorespot, ‘Innovation in ML and Retail Part 2’. As always, AICorespot’s Editorial Team brings you the latest and greatest advancements in the world of emergent technologies and the rapidly evolving scene of Industry 4.0. As a tech blogger, based on my voluminous research, and interactions with industry experts, I’ve always said that the impact of Industry 4.0 is in many ways, comparable to the advent of the World Wide Web.
The idea of the internet revolutionized the world and made it virtually unrecognizable to people from generations prior. Regardless of age, what nobody can deny is the positive ways in which the internet has influenced our lives;
An Emergent Awakening
If you’d asked a person from 1960 that all these things would be possible in 1999, they’d have wagered you to be a mad person. But older generations are proven to be wrong, time and again, and this is no exception. The planet was a connected village by the year 1999, bravely trudging forward to take on the Y2K crisis, which it overcame. The naysayers and negative Nancy’s were proven wrong once again.
The point is – whenever there is a time of crisis, humanity bounces back with an inimitable vigor. All the while, so-called “prophets”, “godmen”, and slavish naysayers prescribe doom and gloom, whether it be the Kali Yuga, Judgment Day, or some other, equally ridiculous end of days prophecy. Technology is in many ways, the antithesis to hysteria, it is the acknowledgment of human power and responsibility, a reminder that we truly are in control, in every sense of the word. The hallmark of this human control is Industry 4.0.
No divine entities are protecting us, (or failing to protect us), from adverse situations, it’s all an issue of having the necessary tools to fight back in these times of adversity, whether it be the COVID-19 crisis or the Russia/Ukraine conflict. Before we start discussing plans to crowdfund Robocop, to keep our communities safe, we need to take baby steps. Rome was not built in a day. One day, tens of thousands of years in the future, galactic joyrides might be a reality, but not now. Let’s hold on to our horses, literally.
While this might appear to be somewhat of a tangent – it is anything but. Emergent technologies such as NLP, AI, ML, AR/VR, and Deep Learning are gaining massive ground in these pandemic times, both in terms of proliferation and development. Online-based retail has necessitated NLP development. Online-based retail is also massively reliant on AI and ML technologies to get the job done. It can be argued that Artificial Intelligence and Machine Learning lie at the backbone of Industry 4.0.
Newer emergent technologies are in the industry 4.0 mix is AR/VR, and Digital Twin. They’ve always been around in one form or another, but they’re currently experiencing an explosive boom period, and retail stands to gain from AR/VR implementations in its retail setup. While brick-and-mortar stores will never truly be gone from the scene, we’re heading towards a reality where we’ll be dealing with a hybrid business model of brick-and-mortar and web-based retail; emergent technologies will bring these two together in the most innovative and novel ways – in pole position to get the most utilization are AR/VR.
In this section of the blog article, we will speak about the latest and greatest innovative advancements in ML tech. Specifically, we will be focusing on nine unique trends and describe how the newest innovations in ML tech can confer your business with the edge in the market.
- No-Code ML
- ML Operationalization Management
- Full-stack DL
- Reinforcement Learning
- Unsupervised ML
- Generative Adversarial Networks (GANs)
- Few Shot, One Shot, and Zero-Shot ML
No-Code Machine Learning
A majority of ML is managed and set up with the help of computer coding, this is no more universal. No-code ML is a method of coding ML applications without needing to experience the protracted and tedious procedures of pre-processing, modeling, developing algorithms, gathering fresh data, deployment, retraining, change management, and a lot more. The following are some of the primary benefits of ‘No-Code ML’
- Swift implementation — With no coding required to be authored and no requirement for debugging, most of the time used will be on obtaining outcomes rather than development.
- Reduced expenditure — As automation eradicates the requirement for protracted development times, big data science teams are no more required.
- Ease of use / Simplicity — No-code ML is simpler to utilize owing to its simple drag and drop mechanisms.
No-Code ML utilizes drag and drops inputs to streamline the process into the following:
As this massively streamlines the ML process, the time investment required to become a specialist is not required. Even though this greatly increases accessibility, particularly to developers, it cannot be a replacement for more sophisticated and layered projects.
It can be relevant for simple data analysis predictive projects such as retail profit levels, dynamic pricing, and staff retention rates.
No-code algorithms are the ideal choice for small-sized enterprises that cannot keep up with maintaining an independent team of data scientists. Even though the use cases are restricted, no-code ML is an amazing option for the analysis of data and making forecasts over time with a massive amount of expertise or development.
In a scenario where IoT is becoming more and more commonplace, TinyML presents itself as a useful solution. While major scale ML applications are available, the usability is restricted. Small-scale apps are often required. It’s time-consuming for an online request to transmit data to a massive server for processing by an ML algorithm and then transmitted back. Rather, a more palatable strategy could be to utilize ML applications on Edge Devices.
By executing small-scale ML applications on IoT Edge Devices, we can accomplish reduced latency, reduced power expenditure, reduced bandwidth needed, and ensure end-user privacy. As the data isn’t required to be transmitted to a data processing facility, bandwidth, latency, and power expenditure are massively curtailed. There is also maintenance of privacy as the computations are made completely on a local basis.
This unique development has wide application:
- Predictive Maintenance for Industrial Facilities
- Healthcare Domain
- Agricultural Sector
Warehouses, which are an integral aspect of the retail sector, could benefit from predictive maintenance. Predictive maintenance is also integral to web-based retail, where being online 24x7x365 is a matter of criticality.
AutoML or Automated Machine Learning has put forth a new level of data processing for businesses across the industry. The retail space is heavily reliant on the power of data, and it has usually deployed conventional analytical strategies. The advent of AutoML tech has paved new pathways in how retail companies detect correlations and isolate anomalies in data. The following are a few use cases found at the intersection of AutoML and Retail.
- Cashier-less retail
- Stock Visibility
- Pricing Strategy
- Recommendation Engines
The retail domain makes extensive use of resources, and it is dependent mostly on human participation for effective operation. Automation can change the game, and this is where AutoML steps in.
AutoML utilizes your smartphones as trackers and detects your activities within the retail outlet. It can document which areas you went to first and which ones you went to after that, and which ones were the lowest priority. Facial identification tech could function in unison with ML to detect high spenders and provide personalized services. After you finish the purchase, AutoML, infused with AI, could debit the money from your bank account for the articles that you purchased without needing to wait in a queue to bill your items.
The retail space has minimal stock management solutions which inform a retailer just how much quantity remains in stock and how much of a specific article has been sold for the day. Now, stock management solutions are incapable of telling:
- How many are there in the system?
- How many are physically present in the warehouse?
- How much stock should there be of a specific item?
Retail outlets could outfit shop carts with cams that detect the items on the shelf as the client moves ahead adding items onto their cart. The store’s system could display adverts to capture the focus of the client towards a specific product that is being marketed. Stock management is set to be greatly streamlined with such technologies.
That brings us to the end of this 2nd part of the multi-part blog series, Innovation in ML and Retail. We will continue exploring other innovative use cases in addition to discussing other matters on the 3rd part. Stay tuned.