Machine Learning – Art of Learning from the Past

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Machine Learning – Art of Learning from the Past

Machine Learning (ML) is a specialised core area within Artificial Intelligence (AI). It is the ability of machines to enable them to gain intelligence and learn on their own without exclusive programming. This allows computers to self-learn based on experience, much like the humans would do.

 

The era of ML began with the identification of data patterns that could be isolated and mapped to certain behaviours and hence lead to a specific set of actions. Based on the existing data examples, machine learning algorithms can empower computers to make better and informed decisions in the future.

Since the new computing power and technologies have enhanced abilities to manage huge volumes of data, machine learning learns from this data at high speeds. Of course, it requires training efforts and time, along with a rich set of experiences and examples.

Some examples

Manufacturing companies, for instance, can use enterprise mobility in areas such as field operations, asset management, warehouse management, shop floor etc. Healthcare industry can benefit from features like remote patient consultation, doctor car from remote in areas like rural areas, expert advice from a distance etc. Hotel & hospitality sector can provide users the ability to make their bookings, supply chain can streamline its transfer and handling processes, after-sales service industries can use mobility to improve their on-road technicians and home delivery services, and financial businesses all can improve their customer services as well as reduce risks by enabling mobility.

Cutting-edge benefits of Machine Learning

Machine learning backed AI, along with cognitive technologies, is a rich combination that can yield far-reaching benefits and insights. Some of these are:

  • Personalised customer experience
  • Lowered costs, enhanced productivity
  • Improving customer retention and loyalty
  • Enhanced business processes, productivity, and efficiency
  • Automating workflows like finance
  • Hiring the right set of people via software assistance
  • Fraud detection
  • Streamlining supply chains
  • Predictive maintenance

How can various industries benefit from ML?

Various industries have started to use ML and AI in a variety of ways. For instance, healthcare sector can empower itself in diagnosis, prescription writing, treatment and even medical procedures with ML adoption. This can result incomputer’s ability for self-diagnosis without human intervention based on existing patterns and symptom matching. The massive reference samples and case history data samples could span multiple databases and computers can compare these at swift speeds.  Robotic arms are already operating like human surgeons. Predictive medicine is also coming of age. Bots are being trained to become virtual assistants for patients during and after hospitalization.

Another interesting case is that of the education sector. Tech-enabled classrooms are empowering personalised self-paced learning for different students in the same class. ML can facilitate early detection of learning disabilities. AI and ML can be used to auto-generate tests based on data from existing question banks. Machines can identify the gaps between a student’s skills and expected levels and chart out individualistic curricula to improve the performance as needed, even driving the delivery of the curricula for a more receptive and engaging learning experience.

Financial businesses are a strong playing field for ML.  Banks can use advanced analytics to make machines learn about credit risk and replace a loan officer top approve and reject loans. Robot-assistant can learn about investment options, study an individual’s portfolio, understand the risk appetite, and propose investment options accordingly. Stock and share markets are using ML to make better predictions.

The transportation industry is also very excited about the prospects of AI and ML. Apart from the self-driving cars and self-managed drones, there are driver assistance systems as well. Autopilot is the manifestation of such an intelligent and self-learning system.  Such systems can self-park, switch lanes, suggest alternate routes, provide a 360-degree virtual view to ‘see’ in all directions around the vehicle etc.

Other industries like oil and gas industry, water management, solar industry, manufacturing and many other sectors which have plants and fields, can use ML to allow self-monitoring and self-correcting services. Even functions like marketing can use ML in innovative ways to run target campaigns, learning customer behaviours, and identify the ones to engage at the right time. ML enables systems to learn from past engagements. Bots can service customers 24 /7, getting trained to become better with time.

Exclusive ML journey unique to every organization

Microsoft’s Azure platform provides a holistic platform to host and plug-in apps and capabilities of ML into a business ecosystem. Its versatility allows seamless integration with existing software systems within an organization. Intelligent customizationis possible via Machine Learning Studio, which is a powerful environment that can be used to develop tailor-made solutions for any business. Microsoft was an early starter in the field of ML, even before it became a commercially viable technology, as it had been using it for its core services like Bing search, Xbox Kinect, Skype, and Xbox games console. Its entire suite of products has the capability to include ML that can integrate taring existing in-house models as well as augment these functionalities.

Of course, to exploit these wonderful opportunities for your organization, you will need the assistance of ML experts who can bring the cloud-based Azure ML service to you.

We, at Techminds , have a rich experience that covers businesses across a gamut of sectors, simplifying the process does our clients to build their own ML models. Contact us for a powerful inclusion if ML for getting the competitive edge for your business with timely and cost-effective investments.

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