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. 

The Future of Machine Learning

Technology is innovating and revolutionizing the world at a rapid pace with the application of Machine Learning. Machine learning (ML) and Artificial Intelligence (AI) might appear to be the same, but the reality is that ML is an application of AI that enables a system to automatically learn from data input. The functional capabilities of ML drive operational efficiency and capacity automation in various industries.

Technological Innovation for Convenience
Workforce handling is tedious and less productive; this is where Artificial Intelligence has lucratively overcome the age-old system of manual labor. With the world moving at such a fast pace, monitoring has become a constraint for most organizations; for this very reason, Artificial Intelligence and Machine Learning are used more as tools of convenience rather than just pieces of technology.

We have seen how accounting systems have replaced ledger books. At the same time, processes have been set up to align machines with organizational requirements effectively to balance everyone’s demands.

However, with the way Artificial Intelligence is advancing, it seems this technology is quickly going to change the way processes are functioning. Not only trends on social media will be affected, but even marketing will see a complete makeover through the use of Artificial Intelligence.

The Effect on Various Fields
When it comes to Artificial Intelligence, everybody wants a taste of it. From marketing experts and tech innovators to education sector decision-makers, Artificial Intelligence holds the capability to pave the path for a healthy future. Artificial Intelligence has been designed to provide utmost customer satisfaction. To derive maximum results from the nuances of AI customer-centric processes will need to align their business metrics to the logic of this latest technology.

As Big Data evolves, machine learning will continue to grow with it. Digital Marketers are wrapping their heads around Artificial Intelligence to produce the most efficient results by putting in minimal efforts. The entire algorithm and the build of Artificial Intelligence will be used to predict trends and analyze customers. These insights are aimed at helping marketers build patterns to drive organizational results. In the future, it seems like every basic customer need would be taken care of through fancy automation and robotic algorithms.

Healthcare Sector
The healthcare industry is one of the widely reckoned industries in the world today. Simply put, it has the maximum effect on today’s society. Through the use of Artificial Intelligence and Machine Learning, doctors are hoping to be able to prevent the deadliest of diseases, which even includes the likes of cancer and other life-shortening diseases.

Robots Assistants, intelligent prostheses, and other technological advancements are pushing the health care sector into a new frenzy, which will be earmarked towards progressing into a constantly evolving future.

Financial Sector
In the financial sector, it’s vital to ensure that companies can secure their operations by reducing risk and increasing their profits. Through the use of extensive Artificial Intelligence, companies can build elaborate predictive models, which can successfully mitigate the potential of on-boarding risky clients and processes; this can include signing on dangerous clients, taking on risky payments, or even signing up on hazardous loans.

No matter what might be the company’s requirement, Artificial Intelligence is a one-stop shop when it comes to preventing fraudulent activities in day to day operations – this, in turn, will lead to money savings possibilities, profit enhancement and risk reduction within every organizational vertical.

Robotics
We are steadily heading towards a future that will be marked complete with the rise of robotics and automation; this is not going to be restricted to the medical sector only; intelligent drones, manufacturing facilities, and other industries are also going to be benefited by the rise of robotics. Artificial Intelligence methodologies like Siri and Cortana have already seen the light of day – this is just the beginning. More and more companies are going to take these capabilities to a new level.

As more and more military operations begin to seek advantages from the likes of mechanized drones, it won’t be long before e-commerce companies like Amazon start to deliver their products through the use of drones. The potential is endless, and so are the possibilities. In the end, it is all about using technology in the right manner to ensure the appropriate benefits are driven in the right direction.

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