With the rapidly growing popularity of machine learning (ML), companies are racing to build up their capabilities. Some organizations opt to hire a machine learning engineer in-house. Others prefer outsourcing to an established ML leader with a machine learning team to reinforce their business processes.
When used well, machine learning delivers innovative results that consistently support business objectives.
Since its inception in the 1990s, machine learning has gone from a niche scientific interest to an in-demand industry resource. Its applications range from finance and medicine to nanotechnology and beyond.
The fundamental technology of machines has evolved rapidly, and thousands of scientific articles on ML have been published over the last few years. Big data is getting bigger by the day. Businesses that once struggled to manage large datasets now have the appropriate tools, with intelligent data analytics able to provide an excellent ROI. It’s just a matter of finding a suitably skilled workforce.
Current Landscape in Machine Learning: For Businesses & Engineers
The use of machine learning in the industry is on the rise, and the importance of data is continuously growing. Thanks to the advancements this technology brings, companies are ramping up their investments in ML. In the case of a machine learning engineer, now is a great time to be in the job market.
Uses for machine learning, AI, and other innovations are proliferating rapidly. Some of the better-known categories include search engines and product recommendation engines, as well as self-driving cars.
However, as much as the current climate is ripe for further developments in ML, not all business leaders are up to speed.
The Case for Businesses
Several challenges face companies attempting to use machine learning, however. Too often, machine learning is seen as a costly activity or a one-time project—not an essential investment. Some organizations have bought into the misconception that ML is too expensive, too complex to deal with, or simply unnecessary.
This lack of understanding frequently leads to issues that have further delayed the adoption of ML technology. As a result, businesses are being left behind. Common problems for these enterprises include: insufficient access to data, inability to deploy models at scale, and hold-ups in transitioning from on-premise to cloud-based hardware.
Overall, many companies lack the IT resources so critical for ML. And there’s fierce competition to secure the best engineers.
The Case for Engineers
For a machine learning engineer, work opportunities have grown consistently in recent years and continue to expand. People with ML and AI skills are currently a hot property. Companies are hiring at full speed as well as outsourcing to organizations that already have ML expertise.
There are online courses for developers looking to advance their machine learning knowledge. Resources for ML are diversifying, with open-source toolkits helping to build enterprise-grade applications.
Several large tech companies hired a number of the top ML professionals early on. Then, with more widespread adoption of machine learning, the technology democratized to non-tech companies. Lately, the field of machine learning has shifted from a scientific orientation to a more practical engineering focus.
Furthermore, data science teams are growing in number and size to meet demand. Increasingly, companies even have multiple ML groups for their different departments. This can happen when there isn’t a company-wide effort to hire or outsource ML engineers.
Future Prospects
These days, machine learning can be found virtually everywhere. Its massive popularity accompanies advancements in supporting hardware and services. Businesses of all sizes and verticals are turning to ML for image and audio processing, sales and marketing, and an expanding range of business applications.
Machine Learning in Business
So, exactly what is machine learning? Machine learning is an approach within the broader field of artificial intelligence. AI technology uses various reasoning techniques that can be applied in business. ML methods involve statistical analysis of data to replicate human thinking. The system makes predictions which drive smarter decision-making—resulting in superior business data analytics.
An effective system incorporates ML design with model development and automated operations. At the design stage, there’s initial modelling and data acquisition to provide answers to business questions. This is followed by continuous integration, deployment, and delivery. It’s essentially DevOps geared specifically for machine learning.
MLOps Implementation
Machine learning is more than a few algorithms or “quick math.” Rather, it involves the full end-to-end lifecycle of machine learning operations (MLOps) to derive maximum value. Moreover, MLOps implementation and adaptation isn’t a one-time event. It needs to be managed and monitored regularly to meet evolving business needs. As such, it’s important to allocate resources for its ongoing maintenance.
How Important is Regular ML Maintenance and Monitoring?
While the theory behind it has been developed over many years, it doesn’t make machine learning any easier to apply in business. Often, it’s done so poorly that it endangers the organization. For example, multiple systems and mission-critical processes may have been built on simplistic code written by a machine learning engineer who’s no longer with the company.
In some cases, the fragility of a poorly written ML solution threatens to undermine the accuracy of business data. To start, the company may not have anyone who understands the problem or how to fix it. Further, they may not even be aware that there is a problem. This type of scenario can result in decision-making based on false information—with serious financial consequences.
Best Practices for Getting Maximum Value From ML
Best practices for data-driven companies has the potential to avoid common problems—and get the most out of machine learning. From simple experimentation, you’ll be able to advance to making ML an integral part of your workflow. However, for this to succeed, you need appropriate data management and data governance—precisely the benefits that a partner such as Hakkoda can supply.
Machine Learning’s Talent Gap
One consequence of the current hiring frenzy in machine learning is that there isn’t enough talent to go around. This means that companies lacking skilled engineers will struggle to compete, underlining the importance of finding capable engineers.
Globally, businesses that want to introduce ML talent can’t find enough people with relevant experience. In some cases, different departments within the same company are fighting over talent. Typically, a machine learning engineer would work for IT. However, there’s an ever greater demand for such skills—particularly in areas like marketing and finance.
Without the right employees, any investment in machine learning may go to waste. Deriving value from ML takes more than just the appropriate tools. Nearly two-thirds of decision-makers say they face talent deficits in machine learning and AI. And it’s harder than ever to find the time and resources to develop in-house talent.
For example, hiring an American data scientist can cost well over $100,000 per year. This is pushing many organizations to consider nearshoring to less costly locations that still offer high-quality services—such as Costa Rica.
Presently, around 300,000 people work in AI globally, with demand for tens of thousands more.
For a machine learning engineer, teamwork is very important because the work involves thinking through problems and developing solutions. On-the-job experience is also extremely valuable. In fact, even PhD holders who’ve studied AI extensively may struggle to put their knowledge into practice in the business world.
Not that you need a PhD to work in machine learning. In response to the skills shortage, companies are looking beyond degrees to broaden their hiring pool. They’re increasingly interested in industry certifications and, above all, project experience.
Where Managed Service Providers Come In
An effective way to solve the challenge of acquiring ML talent is to use a managed service provider (MSP). Having an MSP is similar to having an instantly deployable team to boost your agility. A reliable provider such as Hakkoda delivers consistency and quality, with the requisite expertise as well as the newest machine learning techniques.
Considering the fast pace of innovation and the rising trend of remote work, internal IT teams are struggling to devote sufficient resources and attention to ML. A managed service provider handles all the challenges—from data architecture and personnel management to secure hosting and solutions development.
Working with an MSP offers your company numerous benefits, one stand-out being cost-effectiveness. It’s simply more efficient to work with a specialized team than to invest in building one from the ground up.
In addition to reducing the expense of hiring and training your own machine learning engineers, using an MSP lets you select precisely which services you need—and when. These service providers have all the hardware and software you require, tailored specifically for machine learning. Moreover, you’ll be gaining a valuable strategic partner.
The expertise that an MSP brings can elevate your organization’s technical support experience. Any questions regarding data or machine learning? You’ll have access to an entire team armed with top-level knowledge and resources. It’s a simple way to introduce an ML powerhouse to your business operations, no matter your current capabilities.
With Hakkoda, years of experience add up to premium technology and powerful engagement with the ML landscape. Solutions are custom-made to meet your needs and can scale to match specific requirements as your business grows. Automating tasks through a managed service provider will raise productivity. After all, you know your business, and they know machine learning.
Offshore, Onshore, or Nearshore Talent in Machine Learning: What’s the Difference?
Looking to hire a machine learning engineer? There are several options.
Onshore means that the provider works in the same country as your organization. By contrast, offshore refers to working with an MSP based in another country—and in a significantly different time zone. With a nearshore business, you’re getting the best of both worlds. That is, dealing with a provider based outside your country, but still geographically or culturally connected, and often sharing the same or a similar time zone.
Although each approach has its advantages, nearshoring offers an appealing combination of benefits. It costs less than employing onshore talent, yet retains the same high quality and ease of communication. Furthermore, it’s easier than venturing offshore, where there may be complicated legal processes and red tape. Nearshoring tends to be a much more straightforward process. And when you work with a nearshoring partner like Hakkoda, collaborating to achieve your ML goals couldn’t be simpler.
Selecting the Best Team
Outsourcing brings together a team of engineers, analysts, and data scientists under the appropriate management to deliver effective solutions. If you’re hiring a full team of onshore experts, it could work out to be quite expensive, not to mention time-consuming. On the other hand, working with teams based in a far-off country has its own set of complications. This is where nearshoring shines—with that perfect balance between affordability and familiarity.
A nearshoring team based in a location such as Costa Rica is a perfect solution. For instance, Costa Ricans share an American-style work culture, the country’s socioeconomic climate is stable, and the infrastructure is excellent. An added benefit is the ease of travel between the US and a nearshoring country such as Costa Rica, should the need arise.
Machine Learning Engineering with Hakkoda
Thanks to the functional advantages of machine learning, more and more companies are using it to automate tasks and improve their decision-making abilities.
Hakkoda offers nearshore expertise in machine learning. Rather than battling to hire scarce and expensive talent, choose a flexible team structure for cost-effective ML. Hakkoda’s ML team works from Costa Rica, which shares a time zone and cultural rapport with the US.
Working with Hakkoda, you have access to product accelerators who ensure you get the most value from your data. For example, the Looking Glass cloud accelerator manages large files for healthcare regulatory compliance. The FHIR Data Loader is another healthcare accelerator which simplifies the processing of health data.
At Hakkoda, we can work with you to build what you need—whether in machine learning or other fields such as data governance and data operations. Hakkoda already has the right people and tools for ML teams and offers prompt support via a Slack channel.
If you’re after a machine learning engineer—or engineers—look no further than Hakkoda. Our team of experts will exceed your expectations and bring the functionalities of ML to the core of your enterprise. Contact Hakkoda now to learn more!
