Everything from customer service chatbots to automated cybersecurity tools benefit from the effective use of machine learning. Companies now see it as a critical competitive differentiator on the quest for success in the modern digital economy.
However, significant challenges exist when integrating machine learning routines into a real world business application. As with most nascent software engineering innovations, best practices for a successful implementation are still emerging. Additionally, the difficulty in sourcing talented and experienced ML engineers continues, especially considering the tight technology job market. embedding machine learning into legacy applications is sometimes more complex compared to crafting new software with ML models.
Ultimately, the benefits of machine learning probably make those challenges worth it for most businesses. More efficient operations help companies become more profitable. ML also powers innovative new product and service ideas with the potential for massive earnings. So let’s analyze the best approach for overcoming any challenges when integrating machine learning into mobile, web-based, and desktop applications.
The Benefits of Embedding Machine Learning in Modern Software Applications
Some businesses exploring ML even enjoy quick wins by using it to improve data quality throughout their organization. The modular approach to building modern software makes it relatively easy to embed ML models in data pipelines, data lakes, and even production databases. Note that this approach differs from reengineering legacy applications to use machine learning.
Other benefits of machine learning directly relate to the type of application and its business function. For example, AI-powered chatbots used in securities trading allow human analysts to focus on more value-added tasks. An improved customer service approach also helps engender customer loyalty by providing a more personalized experience.
Additionally, machine learning comes into the play when analyzing customer behavior. This approach helps organizations in multiple business sectors design products that truly meet the needs of the consumer. It’s beginning to see wide use throughout the banking and financial industry when combined with design thinking concepts. Of course, any business sector depending on designing effective products or services also benefits from this use-case for ML.
The decision making process offers another area for machine learning to work its magic. Decision support systems increasingly leverage AI-assisted functionality to assist human analysts in making the right choice given a set of variables. It’s also important to emphasize the point that ML isn’t always a replacement for talented human workers. Instead, it provides useful insights whether in decision support, fraud detection, medical imaging, and more.
The Challenges of Integrating Machine Learning in Business Applications
Of course, any software engineering team integrating a nascent technology into a business application typically encounters a few challenges. While it might be relatively easy to develop and train a machine learning model, implementing it into an app requires a discerning approach.
For example, data quality plays a key role in successfully training a model. After deploying it into a production app, if you find the model performs below expectations, the data used during development and training needs close analysis. Additionally, using an iterative process throughout model development and training improves the overall data quality. This approach allows critical changes to the model based on testing results before its deployment into production. In short, don’t skimp on model quality assurance!
Another major challenge when using machine learning with a live business application involves the process of production deployment. Mature organizations use continuous integration/continuous deployment (CI/CD) to successfully integrate new or modified ML models into production. However, companies new to machine learning or modern software development methodologies might lack skills in this area.
Speaking of a skills gap, the shortage of talented software engineers with experience in AI and machine learning also challenges companies looking to integrate ML models into their production applications. While the move to remote working widened candidate pools when sourcing programming talent, specific experience in machine learning remains tough to find. In fact, the O’Reilly’s 2021 AI Adoption in the Enterprise
survey of business leadership ranked the lack of machine learning skills and experience as the top challenge of adopting AI. Nearly one-fifth of the respondents noted it as a significant barrier to adoption.
Overcoming The Difficulties of Using Machine Learning in Production Systems
As machine learning matures, the practice of developing and training models is simplified. For some simple use cases, nnovations like low-code tools and easier to use APIs make the technology more accessible to both programmers as well as business analysts. Ultimately, expect democratizing access to machine learning to play a key role in making it easier to integrate models into live production environments.
Even off the shelf pre-trained ML models are beginning to hit the market. However, the difficulty of finding one to meet the specific needs of an application or dataset makes building and training a bespoke model as a wiser option. Particularly for companies where a few incremental percentage points of accuracy have an outsized impact, a bespoke model can be a much better solution. Some companies now provide standalone application suites to facilitate the process of building and training models.
The emerging area of machine learning operations (MLOps) continues to formulate a collection of best practices to optimize model deployment into live production environments. As noted earlier, a company new to machine learning might not have experience in applying CI/CD concepts for real-time production model deployment. MLOps provides the best practices for these organizations to gain the valuable experience they need for an efficient and effective machine learning development process.
Even with some aspects of machine learning becoming easier to implement, choosing to integrate it into your core business applications and workflows is not a trivial matter. Adding AI to a business process is not a plug and play proposition.
In the end, no substitute exists for talented software engineers experienced in the entire machine learning development lifecycle. This experience provides the critical insights necessary to properly clean data, develop and train models, as well as deploy them into a production environment. Unfortunately, sourcing this talent is another major challenge in the current IT job market.
For this scenario, consider partnering with Gigster for your company’s machine learning development needs. Our business is based on assembling talented and experienced software engineering teams with technical skill and business acumen to ensure a successful project outcome. Make it a point to connect with us to discuss your company’s specific needs for AI development