Machine learning solutions

The choice between ready-made or custom ML solutions depends on many factors, such as:

• the complexity of the business challenges to be tackled with ML
• industry specifics and requirements
• stakeholders’ and investors’ buy-in
• previous company experience with exploratory data analysis, predictive analytics, advanced analytic solutions, and machine learning solutions
• presence of in-house data scientists
• budget and time constraints

Before launching an ML project, you should thoroughly consider and prioritize your goals, needs, and available resources to avoid overstretching your company’s capacity and decide on the best-fit option for your business model. With a careful approach, your machine learning initiatives are likely to thrive.

Leveraging the expertise of a proficient machine learning solutions company like Yalantis can further propel ML opportunities for your organization. Whether you need to seamlessly integrate an off-the-shelf ML solution or develop a custom one, Yalantis can support your journey towards realizing the maximum potential of machine learning technologies.

Ready-made machine learning solutions

There are a wide range of ready-made ML solutions on the market, each distinguished by its functionality, target industries, and degree of customizability. You can find ML solutions tailored to diverse needs across various sectors.

One of the primary advantages of off-the-shelf ML solutions lies in their accessibility and ease of implementation. They offer a convenient plug-and-play approach, enabling businesses to easily deploy ML capabilities without the need for extensive expertise or development time. Additionally, off-the-shelf solutions often come with pre-trained models and extensive documentation, further streamlining the integration process.

Let’s look at other common advantages and shortcomings of off-the-shelf ML solutions.

Pros of ready-made ML solutions according to customer reviews on Gartner Peer Insights:

• A well-thought-out feature set and a high degree of customizability to meet needs within the industries and business models they cover
• Ease of use and platform adaptation for non-technical users
• Onboarding and knowledge sharing for new and existing customers
• Leading providers of off-the-shelf ML solutions frequently update their offerings with the latest machine learning techniques and algorithms (machine learning as a service)
• Many off-the-shelf ML solutions boast vibrant user communities and support networks, facilitating knowledge exchange, troubleshooting, and best practice sharing

Cons of ready-made ML solutions according to customer reviews on Gartner Peer Insights:

• Difficulty finding a machine learning solution for niche industries like agriculture
• Off-the-shelf ML solutions may require sharing sensitive or proprietary data with third-party vendors, raising concerns about data privacy and security
• Frequent system upgrades are a potential threat to the established workflow
• Necessity to compromise your business goals with a one-size-fits-all functionality set
• Non-typical customization requests have to go through the core development team and may not be approved
• Limited number of sources for extracting data to build advanced ML algorithms
• Difficulty integrating with other software due to the limited set of APIs
• Need for an in-house team of specialists to fix possible issues with the purchased software to avoid the risk of sharing confidential information with the customer support team

Despite ready-made solutions’ initial lure of cost-efficiency and quick deployment, you can struggle with such issues as potential security vulnerabilities, inability to solve complex business-specific issues, and disruption to the overall business workflow due to limited and time-consuming integration capabilities.

Custom machine learning solutions development

When weighing custom and ready-made ML services, consider the following benefits of custom development:

• Tailored algorithms. When you hire a team for custom ML development, you can choose ML algorithms that completely cover your needs. When purchasing off-the-shelf software, it may require much time and effort to differentiate between those solutions that fit and don’t fit your business model and data infrastructure.
• Specialized problem-solving. Custom development enables specialists to address complex and niche challenges with tailored solutions. Unlike off-the-shelf software, which may offer generic approaches to problem-solving, custom ML development empowers experts to delve deep into specific issues and devise innovative strategies and predictive models to overcome them. This specialization can result in more effective and efficient solutions that deliver tangible business value.
• Flexibility and adaptability. If the algorithm doesn’t work as planned, it can be fixed promptly based on new criteria. This is possible if you work with a specialist with deep machine learning solution expertise. For instance, Yalantis can implement a factorization machine (FM), a supervised learning algorithm, to reduce the problem dimension and improve ML model calculations. An FM can estimate parameters when only sparse data is available as well as scale to fit large data sets.
• Freedom to experiment. Custom development provides a broader scope for experimentation compared to off-the-shelf software. With no constraints on the number or complexity of ML algorithms you can explore, you have the freedom to innovate and iterate, continuously refining and enhancing your business operations.
• Scalability and future-proofing. Custom ML development provides scalability and future-proofing capabilities. By working closely with a team of specialists, you can design algorithms and systems that not only address current needs but also anticipate future outcomes and scalability challenges. This proactive approach ensures that your ML solutions remain relevant and effective as your business grows and evolves.

While custom machine learning solutions development offers numerous benefits, it’s essential to consider potential drawbacks:

• Cost and resource intensiveness. Developing custom ML solutions can entail significant investment in terms of time, resources, and expertise. The need for specialized talent and infrastructure may pose challenges for some organizations, particularly those with limited budgets or technical capabilities. Thus, you should choose your custom software development partner wisely to develop solutions within your time and budget constraints.
• Complexity and maintenance. Custom solutions may introduce complexities in deployment, integration, and ongoing maintenance. Managing updates, troubleshooting, and ensuring scalability can require dedicated attention and resources, potentially increasing operational overhead.

However, those drawbacks are outweighed by the unparalleled advantages custom development brings. While the initial investment may seem daunting, the tailored ML solutions and specialized problem-solving capabilities offered by custom ML development can lead to substantial long-term savings and efficiency gains. Moreover, experienced specialists can effectively manage the complexity and maintenance challenges, ensuring that your custom ML solutions remain robust, scalable, and aligned with your evolving business needs.

If custom machine learning solutions development sounds particularly promising to you, our AI/ML team is here to provide you with a thorough consultation on which algorithms to choose to gain the most value for your organization and to build efficient ML solutions (ai/ml use cases). If custom development isn’t exactly what you’re looking for, we can conduct market research on ready-made AI/ML solutions for your specific business problem so you can make a measured decision.