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 Rise of Machine Learning-as-a-Service

The Rise of Machine Learning-as-a-Service
Machine learning and artificial intelligence have become two of the most important words to reckon; their use has become imperative in almost all industries of today. With the introduction of machine-learning-as-a-service, data science has become a focus of the masses within a short span of time. While machine learning is a part of artificial intelligence, it is widely regarded as the process through which self-iterating algorithms are run to analyze vast spectrums of data, in the absence of around-the-clock the clock human supervision.

As machine learning takes precedence over other technologies, machine learning-as-a-service (MLaaS) has come up to meet the growing demands of data-driven industries. MLaaS is a set of services which are offered to companies so that they can access and obtain the benefits of Machine Learning without having to hire a data scientist to do the necessary footwork. As cloud technology is gaining momentum, more companies are outsourcing their data needs to be able to benefit from the advantages of MLaaS.

Why is Machine Learning so Important?
As discussed above, Machine Learning is all about running algorithms to achieve desired data-driven conclusions. Such models, which are equipped with the knowledge of machine learning, are adept at forecasting trends, creating real-time analyses, and performing accurate predictions based on user data. Given its adaptive nature, machine learning can grow from past mistakes and outcomes, which ordinarily help drive future positive results.

No matter the realm, machine learning can do it all. From fraud detection, to price optimization, to crime prevention, there is no end to the capabilities of this advanced technology. For companies looking at optimizing their day to day services, MLaaS is the best data optimization solution. MLaaS is offered as a Cloud-based service and consists of automatic learning tools, which learn as they go. These options can be used in the Cloud, or even in a more hybrid fashion, as per the need of the hour.

The Current Situation of MLaaS Implementation
If you think that MLaaS options are new entrants in the market, you cannot be further from the truth. The technology is not new; Microsoft, Amazon, Google Cloud and IBM have already been providing customized services to their customers. These tech conglomerates offer an excellent platform to their customers; wherein organizations can then create their personalized machine learning algorithms without having to get into the know-how of the technology.

While a majority of big, financially sound companies are making the most use of these platforms, the trend certainly seems to be changing rapidly. With the entry of new MLaaS companies, even small and medium-sized companies are raking the benefits from machine learning services. Since qualified data scientists are scarce, more companies are beginning to traverse on this path to make their data ends meet.

Benefits of MLaaS for Companies and Organizations
Just like SaaS (Software as a Service), MLaaS is hosted by a vendor, which means outsourcing is going to help you reduce your expenses drastically. Since many organizations do not have the infrastructure or the funds to host their own data storage servers, they seek the help of MLaaS vendors to do their bidding. Storing vast amounts of data can be a costly affair especially for small and medium-sized businesses (SMBs). What can be better than bringing in vendor managed platforms for data management, and letting them do all the data-driven algorithms? For this very reason, many MLaaS companies offer scalable technology, which can help SMBs pick and choose as per their requirements.

With so much riding high on this technology, there is a lot of scope for machine learning to progress within the near future. The capacity for expansion is limitless, which means companies are becoming more competitive in the market. MLaaS helps small and medium-sized business improve their technology, enhance their services, and lower their overall operational costs.

As the list of benefits increases so does the need for these platforms in everyday functioning within these companies. As more businesses begin to seek the services of machine learning, there is an inherent scope of expansion, as companies start to look for greater benefits from their machine learning partners.

Also Read

Understand How Artificial Intelligence and Machine Learning Can Enhance Your Business
How Your Small Business can Benefit from Machine Learning
The Future of Data Science Lays within Cloud-Based Machine Learning and Artificial Intelligence
Machine Learning’s Impact on Cloud Computing