Is Machine Learning the Solution to Your Business Problem?

The term Machine Learning (ML) is defined as ‘giving computers the ability to learn without being explicitly programmed’ (this definition is attributed to Arthur Samuel)Another way to think of this is that the computer gains intelligence by identifying patterns and data sets on its own, improving output accuracy over time as more data sets are examined. Since ML can be a challenging solution to implement, we’ve put together some foundational steps to assess the feasibility of building an ML solution for your organization: 

1. Identify the problem TYPE 

Start by distinguishing between automation problems and learning problems. Machine learning can help automate your processes, but not all automation problems require learning.

Automation: Implementing automation without learning is appropriate when the problem is relatively straightforward. These are the kinds of tasks where you have a clear, predefined sequence of steps currently being executed by a human, but that could conceivably be transitioned to a machine.

Machine Learning: For the second type of problem, standard automation is not enough – it requires learning from data. Machine learning, at its core, is a set of statistical methods meant to find patterns of predictability in datasets. These methods are great at determining how certain features of the data are related to the outcomes you are interested in.

2. Determine if you have the right data

The data might come from you, or an external provider. In the latter case, make sure to ask enough questions to get a good feel for the data’s scope and whether it is likely to be a good fit for your problem. consider your ability to collect it, its source, the required format, where it is stored, but also the human factor. Both executives and employees involved in the process need to understand its value and why taking care of its quality is important. 

3. Evalute Data Quality and Current State

Is the data you have usable as-is, or does it require manual human manipulation before introducing into the learning environment? A solid dataset is one of the most important requirements for building a successful machine learning model. Machine learning models that make predictions to answer their questions usually need labeled training data. For example, a model built to learn how to determine borrower due dates to improve accurate reporting needs a starting point from which to build an accurate ML solution. Labeled training datasets can be tricky to obtain and often require creativity and human labor to create them manually before any ML can happen.

4. Assess Your Resources

Do you have the right resources to maintain your ML solution? Once you have an appropriate question and a rich training dataset in hand, you’ll need people with experience in data science to create your models. Lots of work goes into figuring out the best combination of features, algorithms, and success metrics needed to make an accurate model. This can be time-consuming and requires consistent maintenance over time.

5. Confirm Feasibility of ML Project

With the four previous steps for assessing whether or not ML is right for your organization, consider the responses. Is the question appropriate for building an ML business solution? Is the data available, or at least attainable? Does the data need hours of human labor? Do you have the right skilled team members to carry out the project? And finally, is it worth it – meaning, will the solution have a large impact, financially and socially? 

It’s important to consider these key questions when assessing whether or not Machine Learning is the right solution for your organization’s needs. Connect with our ML experts today to schedule your free assessment. 

Understand How Artificial Intelligence and Machine Learning Can Enhance Your Business

Understand How Artificial Intelligence and Machine Learning Can Enhance Your Business

Automation is the key to success; every company is expanding on this domain’s expertise, as organizations take on a more global approach. Given the problems of decision making, learning, and the need for adaptability when understanding data, data scientists introduced the concept of Machine Learning within the realm of Artificial Intelligence. These practices have been able to bring about a radical change in modern business efficiency.

Artificial Intelligence is commonly a platform which performs tasks intelligently, without incurring the need for human intervention. On the other hand, Machine Learning is an exclusive part of the Artificial Intelligence world, which encapsulates the know-how and the logic behind making the concept of Artificial Intelligence a real success story. Through the use of Machine Learning, machines can be taught to work more sensibly, thereby allowing them to recognize different patterns and understand new circumstances with ease.

Machine Learning has come to be used extensively, especially when it comes to providing analytical solutions to the world of consumers and technology. Through large systems of data, Machine Learning has been able to drive solutions, which help create a more data-driven approach towards solving problems.

How Artificial Intelligence is Changing Enterprise Applications

Corporate enterprises are showing a growing interest in the field of Artificial Intelligence and Machine Learning. From IBM’s Watson to Google’s DeepMind to AWS’s multiple Artificial Intelligence services, there is a lot of activity happening in the market these days.

Other features of Machine Learning include the likes of Deep Learning, computer vision and natural language processing (NLP). With all these innovations languages in place, computers can enhance their functionalities, including pattern recognition, forecasts, and analytical decision-making.

By incorporating Artificial Intelligence and Machine Learning techniques in day to day functions, large enterprises can automate everyday tasks and enhance their overall efficiency in the long run.

Here are some ways in which Machine Learning techniques are helping enterprises enhance their efficiency:

Improving Fraud Detection: Fraud detection has become the need of the hour, as more and more companies are investing heavily in these new capabilities. With more companies falling prey to fraudulent practices, there is an imminent need to be ahead in the game of fraud detection. With Artificial Intelligence and Machine Learning in place, companies and organizations can extensively direct their resources towards enriching their fraud prevention activities, to help isolate potential fraud activities.

Loss Prediction and Profit Maximization: When it comes to deriving insights from heaps of data, there is nothing better than Machine Learning to prevent loss prediction and maximize profits. The stronger the techniques, the more foolproof the loss prediction methodologies would become in the long run.

Personalized Banking: In this era of digitization, everything is automated. For this reason, banks often seek to deliver customized, top notch, personalized experiences to their customers to keep loyalty intact. By leveraging their data, banks can aim to unearth customer needs and fulfill them with the utmost precision and dedication.

Robotic Financial Advisors: Portfolio management has become the talk of the town these days, especially since robotic financial advisors have stepped into the game. Clients can benefit immensely by this advancement, since the right opportunities are mapped with their portfolio needs and demands. Robotic applications are easy to merge with services such as Alexa and Cortana, allowing banks to provide exceptional service to their customers. Through this integration, financial institutions can hope to acquire new customers and also offer more individualized services to existing customers.

Next-Era Digital Traveling: Through the use of recommendation engines, travelers can experience the new recommendations for their travel aspirations. Organizations can play a role by allowing customers to converse with chatbots, which are created through the use of Artificial Intelligence and Machine Learning. As predicted by Gartner, by the year 2020, 25% of all customer service operations will rely on virtual assistant technology to make their business ends meet.

Detailed Maintenance: Through the help of predictive maintenance, industries like aviation, transportation, and manufacturing are expecting to be able to provide the best customer service in the market. Through the use of predictive models, such industries can accurately forecast prices and predict their losses, thereby, reducing any redundancies in the future.

With digitization paving the path of the future, there is a bright scope for companies and organizations which are investing heavily in these new age technologies of Machine Learning and Artificial Intelligence. Third party consulting services such as Idexcel are ready to help companies looking to take their first step with industry leading consulting and cloud-advisory services.

As we progress through the years, what should be interesting to note are the changes we will get to see in the various industries, as every sector aims to provide exceptional customer service to their customers in multiple ways.

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