Solution overview
The science of Artificial Intelligence (AI) uses machine learning algorithms to help computers learn from existing data in order to forecast future behaviours, outcomes and trends. Machine learning enables computers to act without being explicitly programmed, augmenting – rather than replacing – human capabilities.
ANEGIS build AI applications that intelligently sense, process and act on information, to automate business processes and increase speed and efficiency. Leveraging Microsoft AI service and infrastructure including Azure Machine Learning, Cognitive Services and Bot Framework, ANEGIS develop intelligent solutions for high value, complex enterprise scenarios.
Machine learning use cases
- Use the Microsoft machine learning platform to analyse Dynamics 365 for Sales data to automatically predict which products to recommend based on customer purchasing trends. Leverage the power of Microsoft Cognitive Services APIs, such as Text Analytics APIs to detect sentiment, key phrases, topics, and language from the text found in Dynamics 365 data.
- Combine Azure Bot Service with Cognitive Services Language Understanding to build powerful enterprise productivity bots. Streamline routine work activities by integrating external systems, such as Office 365 calendar and customer data stored in Dynamics 365.
- Optimise retail product assortment and space planning decisions at the local level. Develop complex predictive models including floor space, product substitutability, customer demographics and purchase habits.
Predictive maintenance with IoT
Predictive maintenance techniques are used to anticipate when an in-service machine will fail, so that maintenance can be planned in advance. Operational data from IoT sensors can be combined with other data sources, such as environmental conditions, to build predictive models.
The predictive strategy uses machine learning in a supervised learning process. This learning process requires data – the full life history of a series of devices – to train an AI model. The more complete the service life data, the more accurate the model. To learn to predict failures, the data must contain instances of those failures. The predictive maintenance strategy aims to replace equipment and parts on a just-in-time basis, avoiding unplanned failures and maximising service life.
Real-time analytics can be set up without having to manage complex infrastructure and software, making it easy to configure dashboards with live metrics such as a machine’s performance, operating conditions, behaviours and failure potential.
Challenges
The data we collect may contain hidden patterns and relationships.
Machine learning algorithms enable the discovery of these patterns, even if they are not immediately obvious.
In many fields, we need automated decision-making systems.
Machine learning allows for the creation of models that formulate decision rules based on data analysis.
Machines need to be flexible and capable of learning continuously.
Machine learning models can generalize and infer from new data, allowing them to assimilate new concepts.
Data is often incomplete, noisy, or requires refinement.
Machine learning algorithms can adapt to new information and generalize conclusions to larger datasets.
Machines need to be capable of real-time learning based on interactions with users or the environment.
Reinforcement learning allows machines to acquire knowledge through interaction with their surroundings.
Advantages
Machine learning allows for the automation of many tasks, leading to more efficient processes. For example, ML systems can analyze data related to production, logistics, or customer service, helping to optimize deliveries, resource planning, and inventory management.
ML systems can predict trends, such as in sales, demand, or customer behavior. This helps the company make more accurate strategic decisions.
ML enables the processing of vast amounts of information, such as in medicine (analyzing medical images), marketing (customer segmentation), or logistics (route optimization).
With ML algorithms, a company can better understand its customers' preferences and needs. This enables the delivery of personalized offers, product recommendations, and individual support.
In the financial sector or cybersecurity, ML can help identify anomalies, such as suspicious transactions or hacking attempts.
ML can optimize resource management, such as production planning, distribution, or service operations. This results in time and cost savings.
