Building Machine Learning-based products offers insights that enable tech administrators to boost customer support, eliminate fraud, and anticipate the demand for services. However, choosing the right ML model based on the use case of a particular business process remains challenging for them. Several tech leaders have reported that ML models are hard to find and incorporate. Reliable practices have to be followed right from the start of the ML development lifecycle to ensure that the application has the capability for better production.
Understanding the use case and business objective before commencing the ML development is the first obvious stage. In many scenarios, ML projects are initiated without clearly defined use cases and goals. Such projects are set for failure as the ML solutions need clearly defined specifications, real-time applications, and metrics. That’s why, project managers have to be clear about the list of real-time use cases and success stories to measure success. Otherwise, they will waste a lot of time on chasing an impossible goal.
Proven Use Cases and Success Stories of Machine Learning Solutions
Fraud Detection for Safe Exchanges
Several leading financial enterprises invest greater amounts against every fraud as the retrieval cost case. Machine Learning possesses a range of use cases in this domain. The ML techniques are valuable in improving the security of the transactions by identifying fraud occurrences in advance. For instance, Credit card fraud detection is an established use case, where ML aids in refining transactional and financial security. Augmented with Python or R programming language, these detection solutions can easily predict fraudulent activities.
Credit card fraud detection solutions work in real-time to continually keep an eye on the possibility of fraud and produce alerts consequently. The built-in classification algorithms label the events as deceitful or alleged to overcome the chances of financial loss. CitiBank launches a credit card anomaly detection system (Feezai) for curbing risk management. This Machine Learning-powered detection system is used in over 80 nations. It has helped Citibank effectively track and control around 125,000 payments in a day while enhancing the security level up to 95%.
Cybersecurity Monitoring
At present, emails are one of the most common means for individual and professional interactions. These are also the main avenues to give shape to ransomware or other cybersecurity threats. Consider Phishing emails, they are meant for extracting sensitive data by tricking the mail users. Machine Learning solutions can be instrumental in the identification of cybersecurity attacks by performing real-time email tracking. With the incorporation of Natural Language Processing techniques, ML solutions can analyze email information and flag the risk of phishing attempts. Anomaly discovery using Machine Learning technologies is also helpful in organizing email monitoring. One of the real-world examples is SumatoSoft, a software organization in the US. It utilizes an ML-based email monitoring tool to avert phishing attacks, data breaches, and cyber-attacks. The tool leverages anomaly detection and NLP mechanisms to keep track of the cybersecurity problems entering through the mail.
Algorithmic Trading
Stock market dissimilarities rely on various aspects, with the preferences and sentiments of people being the vital factors for stock value calculation. CFOs in leading firms are using Machine Learning models and methods to organize sentiment analysis for stock market price evaluation. Reliable data sources, such as social media and web reviews are considered for executing sentiment analysis. The conjunction of Machine Learning along with cataloguing and clustering algorithms can categorize a stock into three groups as positive, negative, or neutral.
Charles Schwab, a leading trading firm in Chicago, uses the ML-based K Score model for analyzing user sentiment, price, and commendations. This helped them gain substantial popularity among hedge firms and quant funds in recent years. It is continually delivering 5500+ stock model portfolios in less than 23 hours.
Marketing Optimization
Machine Learning solutions are beneficial in marketing, and one such application of ML in marketing is user experience optimization. The prime concept of the process is to optimize the user acquisition cost to a particular conversion stage. The top-down methodology is one of the popular techniques in use. It takes customer objectives into account, such as buying patterns, appraising, and comparison with the market, and aligns the marketing touchpoints with the end-user objectives. This method, however, does not result in noteworthy revenue generation due to the lack of data-driven insights.
Data-centered techniques are becoming the new normal in organizing user experience optimizations. These are the bottom-up methodologies and comprehensively use intelligent algorithms and practices. ML algorithms govern all the user aspects and offer a score to each individual. This score denotes the customer lifetime value and acquisition costs, based on this marketers can personalize the services.
Recommendation Engines
The demand and reputation for the eCommerce sector are always evolving. Developing Recommendation engines using Machine Learning, Data Science, and AI technologies can deliver retail enterprises with a competitive edge. It can instantaneously evaluate the digital interactions of millions of consumers in real time and offer price, service, or product recommendations.
These engines also quickly define the user’s predilections and preferences based on the search duration, time expended on specific tabs, the number of snaps, etc. By hiring developers from a recognized Machine Learning services provider, retailers can build exclusive recommendation engines and process user interactions in real-time.
Collaborative ML Development – Role of Skilled Consultants
Though tech leaders know the capabilities of ML technology, deploying the models and realizing the expected benefits can be demanding. The problems of implementing any new technology come into play. Inadequate analysis and lack of workforce acceptance rank among the difficulties. This is where leaders should consider hiring Machine Learning consultants from a recognized offshore company. These mavens address an even broader set of problems (from epistemic uncertainty to ethics) and direct the leaders toward project success.
4 Ways Machine Learning Consultants Help Project Leaders
1. Accumulate Data for the Selected Use Case
Even though the use case is determined, lack of data availability is a crucial aspect that restricts tech leaders from implementing successful ML projects. Classifying areas of complexity and having relevant data to deploy Machine Learning solutions is the first step to achieving success. Machine Learning consultants can help in this regard and improve the data availability.
They start creating technical and business metrics based on the selected use case. The ML product can function better when there is an obvious objective and metrics to gauge those goals. To meet the business goal, the consultants thoroughly review existing processes and understand challenges. With the application of Deep Learning and other intelligent technologies, consultants can solve the challenges and store data outcomes for ML processing.
2. Setting the Right Infrastructure
Building a comprehensive infrastructure before commencing ML model development is a costly endeavor. That’s why, Machine Learning consulting services providers recommend project leaders to exploit the Cloud ecosystem initially. Cloud infrastructure is highly scalable, cost-effective, and requires low maintenance. Cloud platforms like GCP, AWS, and Microsoft Azure offer intuitive orchestration tools, containers, and agile architecture for ML development. Based on the ML project’s technical specifications, consultants recommend the best platform to be used.
3. Data Processing Is Essential
Data preparation is equally important to data gathering. For this, ML consultants apply feature engineering during data pre-processing and compare essential functionalities with available data. Data merging metrics are also implemented during the cooperative data analysis stage. Through the use of exploratory data analysis, ML consultants visualize gathered data, execute sanity checks, and confirm the authenticity. Once the data processing is completed, ML engineers incorporate continuous data transformations and data ingestions to amplify data analytics sources.
4. Model Selection and Development
Once infrastructure and data are prepared, it is time to select the right ML model to be incorporated. ML consultants conduct model selection depending on the robustness metrics. When the robustness of the selected model can’t be augmented to fulfill project standards, consultants look for a different ML model. Describing and generating usable test cases is another approach for model selection. When a pre-built model doesn’t suit project requirements, ML consultants build personalized models. Building exclusive models based on required specifications will help project leaders make their Machine Learning project resilient.
Closing Thoughts
These are only some of the important practices followed during ML development. With ongoing innovation and advancements, Machine Learning consultants leverage a number of methodologies to build exclusive solutions and improve the chances of project success. The consultants also help project managers make strategic decisions, increase productivity and profits, and stay ahead of the competition.