For a very long time, IT experts have been describing Machine Learning as the future or the next big thing. News flash — that future is here with us now. ML is no longer just an industry buzzword; it’s a strategic business investment already reshaping how organizations manage their day-to-day operations. Nearly half of all businesses globally are using this cutting-edge technology in one way or another, with many more planning to deploy it in the near future.
Machine Learning’s market size has been growing consistently for the last couple of decades. According to a recent Statista study, the industry will continue on this trajectory for the rest of this decade, reaching approximately $528.10 billion by 2030. As more executives discover the hidden potential of integrating ML into their processes, the demand for Machine Learning engineers will inevitably grow tremendously. For example, in a recent survey by the analytics company SAS, 63% of the responding business leaders said that they were experiencing the greatest skill shortages in AI and ML.
So, what’s the way forward? Where can you find competent ML engineers to help you stay ahead of the curve? What does the future of ML for businesses look like? Does your organization really need an ML engineer? If so, how do you identify qualified engineers for your unique business needs?
This article offers detailed answers to these and several other related questions. At the end of the read, you’ll understand why ML has recently been such a forefront topic and discover how to leverage it to gain a competitive advantage.
How Machine Learning is Disrupting Different Industries
Machine Learning offers a host of benefits to modern-day businesses — from big data analysis to automating and streamlining complex workflows, assisted predictions, intuitive chatbots, market predictions, and so on. Below are some of the industries that are already reaping big from this technology:
- Marketing
Source: Pexels
The marketing industry is one of the heaviest consumers of ML products. And reasonably so — it involves analyzing large data sets to understand customer behavior and trends. With the amount of data generated daily increasing by the minute, it’s crucial for businesses to find better ways to collect, store, sort, and analyze data faster and more efficiently. Relying on the old manual analysis techniques can be cumbersome and time-consuming. That’s where ML comes in; it can glean and analyze historical data to derive meaningful insights within milliseconds. This comes with several benefits, including:
- Decreasing labor costs by using ML models to automate routine tasks like data collection and sorting
- Enabling hyper-personalization by creating detailed customer profiles that businesses can use to develop personalized offerings at scale
- Enhancing customer engagements by delivering optimized content through natural language processing (NLP) models
- Better customer segmentation to help businesses predict customer lifetime values (CLVs) and funnel positions
- Cybersecurity
Research shows that cybercrime will cost the global economy approximately 10.5 trillion annually by 2025. Currently, a single data breach requires about $4.5 million to mitigate. Yes, you read that right — one cyber incident can dent your finances by a whopping $4.5 million!
With Gartner predicting that by the end of this year, about 45% of organizations will suffer the effects of supply chain attacks in one way or another, business executives are actively searching for better solutions to safeguard their systems against malicious cyber actors. One such solution, which has recently gained significant traction, is Machine Learning.
Integrating Ml into corporate networks can help CSOs analyze network system logs, email content, traffic, system logs, URLs, and user behavior patterns to detect anomalies that might be potential cybersecurity threats. You can also train ML models using different malicious software and deploy them to your systems to monitor file characteristics and code behavior for this malware. The best part is that, unlike traditional rule-based intrusion detection and prevention mechanisms, ML systems can track cyber actors or malicious software within your network, record their behavior changes, and improve your defense protocols automatically. This self-improving ability also enables them to detect new malware strains that conventional antivirus tools may overlook.
- Healthcare
Source: Pixabay
Another sector reaping big from Machine Learning is healthcare. A recent Morgan Stanley Research shows that about 94% of healthcare facilities use ML in their daily operations. Like in the other industries, healthcare practitioners primarily rely on ML algorithms to make sense of large chunks of medical data and unravel detailed insights that would have been impossible to identify manually. Doing so enables them to detect diseases in their early stages, automate prescriptions, and provide personalized care. With the advent of deep learning, pharmaceutical firms have also started using ML models to track pathogen behaviors and develop better drugs.
- Transportation
Source: Pixabay
The transportation industry has also recently recorded a tremendous increase in the use of Machine Learning. The most notable example is the invention of self-driving cars that use ML to collect and interpret data on their surroundings through cameras and other sensors to decide what actions to take. They can distinguish between vehicles, cyclists, pedestrians, and animals, interpret traffic lights, and recommend or automate lane changes, stops, and other navigation controls just as humans do. Some studies suggest that these cars are even more accurate than humans, with the potential to reduce collisions to only 1 per 1.3 million miles. Other sectors of transportation that ML is immensely revolutionizing include demand forecasting, route optimization, traffic management, accident prediction, and inventory management.
- Software development
As software developers often say, the hardest part about developing software programs is not coding; it’s meeting the project’s requirements. Machine Learning can help resolve this challenge by enabling developers to analyze organizations’ data, understand their target audiences’ points, and create befitting software solutions. Developers can also use it to analyze existing codebases, identify patterns and potential optimizations, and auto-generate context-aware code suggestions. Popular code editors like Sublime Text and Visual Studio Code are already allowing programmers to integrate ML-powered tools kike Tabnine and Kite to offer personalized code suggestions.
What Does The Future Hold For Machine Learning?
One thing is for sure — the integration of ML into business processes will increase tremendously over the next couple of years. IBM research shows that 42% of companies are seriously considering adopting AI in the future. With the increased adoption and continuous research in this field, the following trends will inevitably shape the future of Machine Learning:
- Quantum Computing: While quantum computers are still at the development and testing stage, they’ve shown promising potential to help ML experts analyze large data sets and identify complex patterns that classical computers often overlook. Upon completion, they’ll enable ML models to run several multi-stage operations simultaneously, saving time and enhancing accuracy.
- Big Model Creation: Like quantum computing, this invention will enable ML programs to perform several tasks simultaneously. It makes it easier for developers to train models according to different project specifications, even if they don’t understand the underlying framework’s relevant applications.
- Distributed ML Portability: The continued adoption of cloud storage and offsite databases increases the need for organizations to facilitate flexibility in accessing and using data sets. As a result, we’ll likely see the rise of distributed ML portability very soon, enabling ML engineers to integrate their previous work into new systems instead of starting from scratch. In doing so, engineers will save time and effort during toolkit shifts.
- No-Code Development: Machine Learning currently requires a strong coding background and considerable industry experience. However, as open-source frameworks like Caffe, TensorFlow, and Torch gain traction, ML will likely get simpler and more accessible to non-technical staff.
Will Machine Learning AI Replace Humans?
One of the greatest concerns for ML skeptics is its perceived ability to replace humans and interrupt the job market. However, nothing could be further from the truth. While Machine Learning, and AI in general, is designed to replace human labor by automating routine, manual tasks, it cannot replace humans fully. Businesses will still need human input. Here’s why:
- ML lacks emotional intelligence — While artificial neural networks (ANS) and deep learning algorithms can train ML models to mimic human intelligence, these systems cannot replicate human emotional intelligence. That’s because computers cannot develop empathy or understand the intricacies of human experiences and connections. Therefore, they cannot connect to customers as intimately as human employees.
- ML systems need engineers to work: They are developed and managed by humans. While the whole concept is that they should improve on their own over time, humans still need to monitor and verify the accuracy of their output.
- ML models rely on the data humans feed them: They cannot derive insights beyond the data at their disposal. Comparatively, humans can creatively think beyond the box and generate innovative Ideas.
Contrary to popular belief, Machine Learning will actually create more jobs. The 2020 World Economic Forum Report shows that while AI will replace approximately 85 million jobs by 2025, it will also create 97 million other jobs within the same period.
Why Every Business Needs an ML Engineer
We understand that these are difficult times for most businesses. Just when we were almost recovering from the impacts of the COVID-19 pandemic, the Russia-Ukraine war came up, disrupting global supply chains and cash flows. Every CFO is looking for new ways to cut costs and eliminate unnecessary spending. Therefore, adding an extra paycheck to your wage bill by hiring ML engineers may not seem like the best financial decision.
Well, before you make up your mind about hiring ML engineers, you might want to read this section. ML engineers can actually help relieve your budgetary strains and give you the competitive edge you need to weather these stormy days.
Here’s how:
- Enabling automation: ML engineers can help you automate routine processes like customer service, attendance tracking, procurement, invoicing, payroll processing, data analytics, and backup restoration. Doing so can help you reduce your spending on manual labor, allowing you to channel these funds to other business projects.
- Better marketing: In these tough economic times, retaining your existing customers and converting as many prospects as possible is the best way out. A competent ML engineer can help you create market models to monitor and analyze customer behaviors, understand their preferences and pain points, and create more personalized campaigns with higher conversion potentials.
- Supporting faster decision-making: We cannot overstate the need for speed in today’s fast-paced, dynamic business world. An ML engineer will help you deploy models to analyze vast data sets in real time and identify market opportunities ahead of your competitors. They can also help you model future market trends and develop proactive solutions before other industry players.
Crucial Skills For ML Engineers
Now that you understand why you need an ML engineer, let’s look at some key qualifications you should consider when screening potential candidates:
Proficiency in programming languages
The majority of an ML engineer’s job involves writing codes to develop, deploy, test, and finetune ML models. Therefore, these professionals require a firm grasp of basic programming languages such as Python, Java, JavaScript, SQL, C, and C++. While most ML specialists start by learning Python (because it’s easy to learn and has numerous use cases), the exact language required depends on your project specifications. For example, when hiring an ML engineer to handle resource-intensive tasks requiring high computational performance, you should look for a C++ expert. Similarly, if you want to deploy ML models to large-scale, distributed systems like production environments, you should hire Java experts.
Data analysis and visualization
The most common business application of Machine Learning is data analysis. In fact, most of the other use cases rely on ML’s ability to process and analyze huge chunks of data simultaneously. Therefore, while ML engineers are not necessarily data analysts, they often require a basic understanding of data mining, cleaning, analysis, and interpretation. They should also know how to train models to not only generate valuable insights from data but also visualize them into easy-to-navigate dashboards. As a result, before hiring an ML engineer, you should find out if they have considerable experience experience in wrangling and building dashboards.
Edge computing
Initially, Machine Learning involved mining data from source systems and routing it to organizational databases before gleaning and feeding it to ML systems. When cloud platforms like AWS, Azure, and Google Cloud Platform emerged, they enabled businesses to integrate ML algorithms into their environments and analyze data in real-time. However, they’re still prone to latency issues and can be affected by network downtimes. These challenges inspired ML specialists to develop yet another nifty invention — edge ML learning.
As the name aptly suggests, edge ML learning involves integrating ML algorithms into source systems, enabling organizations to analyze data as soon as it’s generated. It addresses the need for speed by allowing businesses to draw insights from the most recent data without first transporting it to in-house databases or the Cloud.
When screening ML engineers, assess their understanding of the edge architecture and experience in developing and deploying edge applications. Also, ask them to demonstrate their expertise in edge security principles and familiarity with managing edge systems.
Version control
Another crucial skill you should look for when hiring ML engineers is version control. These professionals’ jobs don’t end after developing and deploying ML models. They also need to continually update these models to address emerging problems and prevent disagreements. While doing so, they require version control expertise to track the changes made and retrace previous versions if necessary.
Besides the above hard skills, ML engineers also require the following soft skills:
- Problem-solving skills
- Effective communication
- Prioritization
- Continuous learning
- Teamwork
- Critical thinking
- Adaptability
- Time management
How To Find Competent ML Engineers
With the global demand for ML engineers at an all-time high, finding and attracting competent engineers is increasingly becoming challenging. Things can get even murkier if you have a relatively lean IT budget. However, with the following tips, you can increase your chances of securing your ideal candidates:
- Write compelling job descriptions: Identify your unique ML needs and craft a detailed job post outlining all the soft and hard skills you’re looking for. Studies show that more than half of job seekers rely on the qualities of job descriptions when deciding whether to apply to different positions.
- Leverage job boards: You can also find competent ML engineers by publishing your job posts on online job boards. While these platforms may lack customization options to help you narrow down to engineers that meet your unique needs, they can be a good starting point if you don’t mind doing the detailed screening manually.
- Build candidate databases: Have you ever received a strong resume, but were forced to drop the candidate because they didn’t meet particular project specifications? Instead of throwing that resume away, how about you reserve it for future consideration? Doing so can help you build comprehensive candidate databases of competent ML engineers that might be useful later.
- Use social media: If you’re having trouble getting ML engineers from your locality, you can use your social media platforms to inform potential candidates of available openings. Research shows that 54% of job seekers use social media for job search, and approximately 70% of managers have hired success through social media platforms.
Make The Smart Choice: Hire Pre-Vetted LATAM ML Engineers Through DevEngine
Hiring ML engineers is not a one-day activity. It’s a comprehensive process that takes several days and sometimes weeks. Fortunately, DevEngine can save you from this hassle by connecting you to pre-vetted ML engineers from LATAM.
Why ML Engineers From LATAM?
First, the region is home to several highly qualified ML engineers, thanks to continuous investment in tech-related programs. For instance, local universities in Mexico alone churn out over 605,000 software engineering graduates yearly. The best part is that, unlike in other parts of the US and Canada, the demand for ML engineers in Latin America is relatively lower — the rest of the world just learned of the region’s potential.
If you’re not sold on LATAM’s vast, untapped talent pool, you’ll definitely love the cost-saving opportunities that the region offers. Compared to Canada and the US, where ML engineers earn approximately $106,000 and $160,000 yearly, LATAM engineers only ask for an average of $46,923 annually. You can surely benefit from this reduced rate.
Transparent Hiring With DevEngine
When you hire through us, we can handle everything from screening to onboarding and staff management. Also, we offer upfront pricing plans, so you don’t have to worry about hidden or additional costs.
Unlike most staff augmentation companies, we only work with a few clients at a time. This approach enables us to give every customer dedicated attention and personalized services. Above all, we treat each client as an individual entity with unique business needs. Before recommending ML engineers, we’ll first analyze your business to understand your work cultures, pain points, and operational models and deliver personalized hiring solutions.
Make the smart choice — hire competent ML engineers with LATAM. Contact Us now for permanent placements, staff augmentation, or building remote MLengineering.teams.