Applications Of Machine Learning

Hello troubleshooters! From retailing section to finance to transportation to manufacturing to recommendation system machine learning can be seen everywhere.

Machine learning is the latest buzzword sweeping across the global business landscape. It’s captured the popular imagination, conjuring up visions of futuristic self-learning AI and robots. In industry, machine learning has paved the way for technological accomplishments and tools that would have been impossible a few years ago. From prediction engines to online TV live streaming, it powers the breakthrough innovations that support our modern lifestyles. And in today's article we are going to explore sections of life where we can easily spotify applied machine learning:

1. RETAILING

1.1 Stocking and Inventory

One of the key elements of running a successful business is the ability to streamline the stocking and inventory management process in a swift and automated manner. ML is offering retailers the chance to purchase online and offline data to predict inventory needs in real time, breaking down these factors based on different segments such as day of the week, season of the year and activity in a particular store. This information could be used to create a daily dashboard of suggested orders for a purchasing manager. Machine vision may also be used soon in the form of cameras that can detect the number of items of a particular product throughout the entire store just by looking at it.

1.2 Predicting Customer Behavior

The technology also has a positive role in analyzing customer data and predicting future behavior. Retailers can use this data to better understand the needs of their customers by examining the price range of their previous purchases, recommending items that they may be interested in. ML algorithms can generate suggestions for items that are complementary to items they are buying instead of simply pushing a hot item that is completely unrelated to what they are purchasing. Additionally, retailers can use add-on options for hygienic and other daily products that they may want to buy on a monthly or quarterly basis if they’re happy with the product.

1.3 Tracking Behavior for Marketing Purposes

ML can also be used to determine how well a product sells based on the position it’s in relative to the rest of the store. One way to predict how customers react to certain products is with cameras that detect the walking patterns and the direction customers face when walking down the store. These cameras could compile data that measure the interest of various products, which could be use to restructure store layouts. They could also be used to test new items or determine whether products with declining sales should be phased out.

1.4 Dynamic Pricing

Companies know that ensuring an item is priced accurately can make or break their business. ML now has the capability of offering dynamic pricing options, which means that the price of certain products change over time through an algorithm that considers a variety of pricing variables. These metrics could include the season of the year, as well as supply and demand. With this technology, retailers have more flexibility when generating the right price at the right time without losing sight of their main goals, including profit or revenue optimization. By learning the performance of a product over time, ML can easily adapt to changes in the market and improve a company’s ROI.

2. BANKING AND FINANCE

2.1 Financial Monitoring

Machine learning algorithms can be used to enhance network security significantly. Data scientists are always working on training systems to detect flags such as money laundering techniques, which can be prevented by financial monitoring. The future holds a high possibility of machine learning technologies powering the most advanced cybersecurity networks.

2.2 Making Investment Predictions

The fact that machine learning-enabled technologies give advanced market insights allows the fund managers to identify specific market changes much earlier as compared to the traditional investment models.

With renowned firms such as Bank of America, JPMorgan, and Morgan Stanley investing heavily in ML technologies to develop automated investment advisors, the disruption in the investment banking industry is quite evident.

2.3 Process Automation

Machine Learning powered solutions allow finance companies to completely replace manual work by automating repetitive tasks through intelligent process automation for enhanced business productivity. Chatbots, paperwork automation, and employee training gamification are some of the examples of process automation in finance using machine learning. This enables finance companies to improve their customer experience, reduce costs, and scale up their services.

Further, Machine Learning technology can easily access the data, interpret behaviors, follow and recognize the patterns. This could be readily used for customer support systems that can work similar to a real human and solve all of the customers’ unique queries.

An example of this is Wells Fargo using ML-driven chatbot through the Facebook Messenger to communicate with its users effectively. The chatbot helps customers get all the information they need regarding their accounts and passwords.

2.4 Secure Transactions

Machine Learning algorithms are excellent at detecting transactional frauds by analyzing millions of data points that tend to go unnoticed by humans. Further, ML also reduces the number of false rejections and helps improve the precision of real-time approvals. These models are generally built on the client’s behavior on the internet and transaction history.

Apart from spotting fraudulent behavior with high accuracy, ML-powered technology is also equipped to identify suspicious account behavior and prevent fraud in real-time instead of detecting them after the crime has already been committed.

According to a research, for almost every $1 lost to fraud, the recovery costs borne by financial institutions are close to $2.92.

One of the most successful applications of ML is credit card fraud detection. Banks are generally equipped with monitoring systems that are trained on historical payments data. Algorithm training, validation, and backtesting are based on vast datasets of credit card transaction data. ML-powered classification algorithms can easily label events as fraud versus non-fraud to stop fraudulent transactions in real-time.

2.5 Risk Management

Using machine learning techniques, banks and financial institutions can significantly lower the risk levels by analyzing a massive volume of data sources. Unlike the traditional methods which are usually limited to essential information such as credit score, ML can analyze significant volumes of personal information to reduce their risk.

Various insights gathered by machine learning technology also provide banking and financial services organizations with actionable intelligence to help them make subsequent decisions. An example of this could be machine learning programs tapping into different data sources for customers applying for loans and assigning risk scores to them. ML algorithms could then easily predict the customers who are at risk for defaulting on their loans to help companies rethink or adjust terms for each customer.

2.6 Algorithmic Trading

Machine Learning in trading is another excellent example of an effective use case in the finance industry. Algorithmic Trading (AT) has, in fact, become a dominant force in global financial markets.

ML-based solutions and models allow trading companies to make better trading decisions by closely monitoring the trade results and news in real-time to detect patterns that can enable stock prices to go up or down.

Machine learning algorithms can also analyze hundreds of data sources simultaneously, giving the traders a distinct advantage over the market average. Some of the other benefits of Algorithm Trading include –

-Increased accuracy and reduced chances of mistakes

-AT allows trades to be executed at the best possible prices

-Human errors are likely to be reduced substantially

-Enables the automatic and simultaneous checking of multiple market conditions

2.7 Financial Advisory

There are various budget management apps powered by machine learning, which can offer customers the benefit of highly specialized and targeted financial advice and guidance. Machine Learning algorithms not only allow customers to track their spending on a daily basis using these apps but also help them analyze this data to identify their spending patterns, followed by identifying the areas where they can save.

One of the other rapidly emerging trends in this context is Robo-advisors. Working like regular advisors, they specifically target investors with limited resources (individuals and small to medium-sized businesses) who wish to manage their funds. These ML-based Robo-advisors can apply traditional data processing techniques to create financial portfolios and solutions such as trading, investments, retirement plans, etc. for their users.

3. TRANSPORTATION

3.1 Automated Vehicles

One of the most noteworthy applications of Machine Learning technology is automated vehicles. The idea of self-driving vehicles was once simply a science fiction dream, has now become a viable reality. Even though individuals were doubtful of this innovation during its formative stages, driverless vehicles have just made their entrance into the transportation area.

We are all aware of Tesla’s self-driving vehicles. And now independent taxis have just begun working in Tokyo. For safety reasons, as of now, the driver needs to sit in the vehicle to take control whenever needed.

3.2 Self-driving trucks

Many logistics companies are including this technology in their fleets to benefit from it. Furthermore, with self-driving trucks becoming a popular idea, the maintenance costs will decrease by as much as 45 percent.

3.3 Traffic Management

Another transportation issue that individuals face consistently is congestion. Machine learning is currently set to settle this issue as well.

Sensors and cameras installed wherever on streets gather the details to analyze traffic conditions. This information is then uploaded to the cloud by gaining professional support from cloud consulting services, where traffic patterns are analyzed after information assessment. Important bits of information like traffic expectations can be gathered from information handling.

4. MANUFACTURING

4.1 Workplace Safety

One of the most adopted use cases has been leveraging artificial intelligence in workplace safety. The technology has led to long-term solutions associated with workplace safety events before they happen or speeding up post-incident root cause analysis (for example, trips, and falls). These solutions lead to healthier employees, a safer workplace, and continued operations on the factory floor.

4.2 Predictive & Preventative Maintenance

Machine learning enables predictive maintenance by predicting equipment failures before they occur, scheduling timely maintenance, and reducing unnecessary downtime. Manufacturers spend far too much time fixing breakdowns instead of allocating resources for planned maintenance. Machine learning algorithms can predict equipment failure with an accuracy of 92%, allowing businesses to plan their maintenance schedules more effectively, improving asset reliability and product quality. With the power of IoT devices, sensors, and machine learning algorithms, manufacturers can utilize many machine data points to predict breakdowns. Planned Maintenance schedules can be optimized before the predicted breakdown to keep machines in top-notch condition and the production floor running smoothly. Studies show that by deploying machine learning and predictive analytics, overall equipment efficiency increased from an industry average of 65% to 85%.

4.3 Supply Chain Optimization

Today’s supply chains are super complex networks to manage, with thousands of parts and hundreds of locations. AI is becoming a necessary tool to get products from production to customer promptly. The manufacturing industry requires extensive logistics capabilities to run the entire production process. Machine learning-based solutions can automate several logistics-related tasks, boosting efficiencies and reducing costs. It is estimated that the average US business loses $171,340 each year due to manual, time-consuming tasks such as logistics and production-related paperwork. These routine tasks can be automated using machine learning and save thousands of man-hours annually. Machine learning algorithms can also be used to streamline resource management.

4.4 Predictive Yield

Yield prediction conversations always come up when AI in manufacturing is being discussed. The ROI on having a high accuracy prediction AI model is limitless. Predicting yield can better prepare supply chain and inventory management for future component needs. Knowing if yield will be lower than expected can alert production management to increase production time to meet demand needs. Yield prediction is a data-heavy complex problem that will require AI to solve.

4.5 Energy Management

AI can help the often-overlooked area of energy management. Most engineers don’t have the time to analyze the cost of factory energy consumption. Having an AI look into the energy consumption of a production operation can significantly reduce operations costs. In addition, reduced costs can allocate more funding for process improvement resources, leading to higher yield and quality. Data is the new bacon, and AI is taking it to new heights.

5. CONSUMER INTERNET

Big business companies use recommender system algorithms to provide a more customized and engaging user experience. As a result, recommenders boost retention and sales, helping companies reach their business goals.

5.1 YouTube

Uses a sophisticated two-stage recommender system. Their candidate generation network provides broad personalization via collaborative filtering. A small subset (hundreds) of videos from a large corpus is retrieved.

Events from the user's YouTube activity history act as input as they are considered to be highly relevant to the user. The similarity between users is noted and expressed as IDs of video watches, search query tokens, demographics, etc.

5.2 Netflix

Tracks what is being watched and how users interact with the platform. Ongoing A/B tests and measuring long-term satisfaction metrics via users' responses to changes in the recommendations are run.

The recommendations served by the video streaming service strive to go beyond just idea validation based on historical data or rating prediction – instead, personalized ranking, page generation, search, image selection, messaging, etc., is taken into consideration.

5.3 Facebook

Cannot use the traditional ML approach because of the sheer mass of data that is being processed – so sampling is not an option. A state-of-the-art deep learning open source recommendation model (DLRM) is used in combination with Facebook's open-source PyTorch and Caffe2 frameworks.

Collaborative filtering and predictive analytics-based approaches help provide recommendations based on the likes and interactions from people who have similar tastes. As a result, users discover the most relevant items through granular pieces of content that are recommended.

5.4 Instagram

Powers its Instagram Explore recommendations section with the help of ongoing research done by the Facebook AI team. It has created a domain-specific language optimized for retrieving candidates in recommender systems called IGQL.

Using Facebooks' nearest neighbor retrieval engine, FAISS, Instagram can serve its users a customized recommended content feed that doesn't base the recommendation on filtering all the mass of content available on the platform. Instead, the recommendations are based and served according to the session's recommendations and similar accounts' interactions.

5.5 TikTok

Has the main content feed called "For You", and it is fully customized for each individual user. Liked and shared videos, followed accounts, posted comments, created/uploaded content – all this determines the user's "indicator of interest" – time watched.

The machine learning recommendation system also factors in video information like the captions, sounds, and hashtags associated with the "liked" content, as well as device type, account settings, language preferences, country, geolocation, etc.

TikTok’s recommender system is optimized for performance, so the results are less precise than on other described platforms. Content from accounts with more followers is boosted on the platform.

So yeah that's all for the today's article do consider checking out previous articles where I've explained machine learning from the scratch. Upcoming article of the 100days of machine learning we would be exploring MACHINE LEARNIG DEVELOPMENT LIFE CYCLE which would let us allow to know the throughout development life cycle technicalities.

Read till here thanks a bunch:)