Love it or hate it, you cannot ignore it. Yes, we are talking about artificial intelligence (AI) and with it, the magic of machine learning (ML). Since 2020, most businesses were compelled to improvise and adopt digital technologies to keep their noses above the water. CEOs and key stakeholders of many businesses were ready to ditch the age-old conventional way of doing things in favour of automated and data-driven business processes.
Since the pandemic, businesses of all sizes have prioritized digital transformation. Among innovations and technologies that have garnered popularity in the past few years, machine learning, a subset of AI has emerged as a clear winner in terms of adoption.
Right from mobile banking to recommending content on social media, machine learning has gradually become an integral part of our lives. Machine learning continues to put its foot forward as an unstoppable force with the ability to transform different businesses and industries.
Despite knowing the benefits of machine learning, many companies cannot adopt machine learning for many reasons. We understand the mere thought of integrating new systems and introducing new changes to a business can be a bit intimidating. However, in the long run, these changes provide plenty of benefits which we will discuss in the later stages of this article.
Let’s look at what machine learning is, why it is a good idea to adopt machine learning, and how companies can do so. You can also go through this interesting and insightful whitepaper which explains what the machine learning journey of a company looks like.
What is machine learning? How is it linked to AI?
As mentioned above, machine learning is a subset of AI. It enables machines to take into account past data to identify trends and make predictions with no major human intervention.
Machine learning relies on algorithms to identify patterns and develop in an iterative process, extracting valuable information from huge amounts of data. Instead of depending on any preconceived equation that might serve as a model, ML algorithms use computation techniques to learn directly from data.
While the concept of machine learning is not new—it was employed in World War II to crack the Enigma code—the ability to automatically perform intricate mathematical operations on the expanding quantities and diversity of data can be labelled as a recent innovation.
Nowadays, with the growth of big data, IoT, and ubiquitous computing, machine learning is crucial for addressing problems in a plethora of areas across different industries. Some of the key areas wherein machine learning models are used include:
- Computational finance
- Computer vision
- Computational biology
- Manufacturing, automotive, aerospace
- Natural language processing
AI in business is important - find out why
“Hey Alexa, turn on the lights”. This is a simple example of the social applications of AI. AI is also extensively used in smartphones, virtual assistants, home devices, and more. While these are the social applications of AI, enterprise-level applications of AI are very different. The use of AI in enterprises is much more complex and at a wider scale, consisting of a host of processes and systems within a business.
So the main question is, why should a business adopt AI?
- AI is largely used to automate several recurring tasks to save money, resources, and time.
- AI creates an additional intelligence layer for businesses in different forms including security systems or chatbots.
- AI plays a key role in significantly enhancing business intelligence solutions and evaluating big data via deep learning, machine learning, and predictive analysis.
- AI offers valuable and accurate insights by building a feedback loop for algorithms to learn on their own.
- AI leverages the power of data and equips businesses to adopt data-driven models for improved decision-making.
Important tips to embrace machine learning
There is no doubt that interest in machine learning has witnessed steady growth in recent years. Today, awareness around the benefits of machine learning has also grown across businesses of all sizes, improving demand for the same.
Despite these positive trends, the adoption of machine learning is still in the nascent phase. Despite a strong interest in machine learning and AI, many companies have not deployed machine learning across all operations. It has taken nearly two decades for machine learning models to gain mainstream acceptance worldwide. Wondering how to embrace machine learning for your business?
Read along to find out.
You can go through these practices that can aid businesses' transition from the exploration and evaluation stage to the implementation stage. These practices will also help you derive value from machine learning optimally.
Create new machine-learning roles in your company
If you are wondering how to introduce machine learning in your company, you can start by determining the different machine learning roles that can be introduced in the business. For instance, identify if your company can hire machine learning engineers or data ops specialists that primarily specialize in creating and deploying machine learning models.
Some organizations that have introduced new machine learning models in their processes have created vacancies for newer job titles. These include data engineers, deep-learning engineers, and data scientists.
If we were to go by the past trends, the consistent growth in the number of machine learning-specific roles worldwide is a key indicator of how popular the technology has become. Let's look at a simple example.
The role of a data scientist was first introduced in 2008 to describe someone who primarily worked on data projects. Today, over 50% of companies in the tech sector have data scientists in their ranks. Besides, data has revealed that organizations that are working toward introducing machine learning roles and hiring resources are likely to be in a much better position to navigate barriers and provide solutions.
Define success parameters for your machine learning projects
Companies that are just starting or have little experience primarily depend on product managers to pinpoint whether or not their machine learning project was a success. However, things work a bit differently in the biggest and most experienced organizations. In larger companies, data scientists define objectives and key priorities related to all major machine learning projects. Here, data scientists are responsible to evaluate whether a project was successful.
Respondents from very large and established companies utilize different success metrics, including machine learning metrics, business metrics, statistical metrics, and more to identify the success of their company.
There is an increasing demand for resources who have the right expertise and skills to handle machine learning projects. Their experience and knowledge are very critical to define success parameters and also creating improved business models that yield better results.
Model building and machine learning are different - treat them that way
As a business leader or key decision maker of a company, you may have heard of agile methodologies used during software development including Kanban, Scrum, Lean Development, and more. As machine learning models become increasingly popular among many organizations worldwide, many experts have pinpointed the pitfalls of deploying these strategies during machine learning model development.
At present, a fundamental mindset shift is very important. Besides, it is also crucial to understand that machine learning development and software development are worlds apart from each other.
Even today, the data community is developing tools to manage and streamline the different aspects of the development lifecycle. These include model deployment, operations, and monitoring. Even though the best practices and tools are sprouting up consistently, it is safe to say that we are in the nascent stage of model lifecycle management.
Create a model-building checklist
Companies need to evolve in the way they operate under evolving regulations and privacy laws. We are seeing a lot of small to large-sized companies venture into the machine-learning sphere. While the newer companies stumble across several unexpected and unforeseen regulatory and operational barriers, the experienced companies have worked around these obstacles.
With years of experience under their belt, established companies adhere to a robust model-building checklist. Such checklists define the required standard companies need to maintain to ensure optimal transparency and data privacy. Data and transparency checks have become very critical as companies need to comply with the latest laws and regulations protecting them. For example, the General Data Protection Regulation (GDPR) in Europe.
The GDPR requires "privacy-by-design," or including data protection from the beginning of the design process rather than as an afterthought. As a result, more businesses will need to include privacy in their machine learning checklist. For most companies that are just getting started, the emergence of tools and techniques for privacy-preserving analytics and ML aligns with the new legislation.
Act now before it's too late, machine learning is the way forward
It is no hidden secret that machine learning has immense potential. Industry experts have even deemed it the "technology of the future". Thus, we are seeing more and more organizations adopt machine learning to improve decision-making and gain a competitive edge in the crowded market landscape.
However, integrating machine learning models in a company's preset processes is not a walk in the park. It requires strategic planning, in-depth analysis, and a methodical approach. We hope the points included in this article will help companies kick-start their machine-learning journey.
To learn more about an organization's ideal machine learning journey, read this insightful and thought-provoking whitepaper.
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