How to Choose the Right Machine Learning Services for Your Company

Introduction: The Machine Learning Landscape

Machine learning (ML) is growing at an astonishing rate. Indeed, the global ML market size is projected to reach $503.40 billion by 2030. Currently, the US is the largest market, followed by China, Japan, Germany and India.

Regardless of where your company is based, given the acceleration of AI and ML adoption, you are likely to require machine learning services at some point to remain competitive.

This is especially relevant for organizations in industries that use ML the most, as per Statista: Manufacturing (18.88%), Finance (15.42%), Healthcare (12.23%), Transportation (10.63%) and Security (10.10%). Hospitality and Retail are also expected to benefit greatly from ML.

There are numerous factors driving the growth of the ML market. Prominent ones include advancements in computing power, the growing availability of data, an increasing need for automation, the rise of edge computing, and the expansion of the Internet of Things (IoT).

Furthermore, new growth opportunities are expected to be created in the coming years from the integration of ML with other technologies such as computer vision and natural language processing (NLP).

ML technology has become increasingly accessible to organizations of various sizes. However, due diligence is required to choose the correct machine learning service among a sea of similar-sounding options.

Steps to Choose the Best Machine Learning Services

ML, aligned with AI, brings a myriad of benefits to businesses, from improved customer experience to increased long-term consumer engagement to fraud detection. Here is how to choose the right machine learning services that will best suit your organization.

Appropriate Data Models

Some services focus on text and image data, such as understanding what people think about your company on Facebook or detecting their emotions when they see your new ad.

Other services handle various types of data from different sources like server logs, database dumps, and customer transactions. Some services are even more specialized, offering solutions for specific problems like fraud detection, ad bidding, and recommendations.

The key is to find a service or model that fits the type of data your business uses. Most services use a method called “supervised learning” to train their models.

This involves using a predefined dataset, such as, for instance, a thousand product reviews in Spanish. If you are analyzing Instagram posts in Spanish, this model might be useful. However, if you need to analyze product reviews in German, this model will not be appropriate.

Innovative Technologies

When choosing an AI/ML development service, it is important to gauge how committed the provider is to remaining updated with the latest advancements in AI/ML. Innovation is key in this field, and a good provider should actively keep up with new trends and technologies. This means they should not only understand current developments but also be dedicated to continuous learning and exploring new solutions.

A good AI/ML partner should show a passion for innovation. This goes beyond knowing what’s new; they should actively engage with new tools, methods, and frameworks.

By examining a provider’s history of adopting and successfully using new technologies, businesses can make sure their AI/ML projects are up-to-date and prepared for the future.

Customization

For some businesses, general pre-trained models might work well. However, many businesses use them because creating custom models is too costly.

Using a general model can mean losing a competitive edge due to a lack of specificity. Instead, consider a machine learning service that lets you refine and customize models to best solve your problems and fit your data.

Data Security and Compliance

When choosing an ML service provider, remember that they will have access to sensitive business and customer information.

Make sure they follow data security guidelines and comply with regulations. Furthermore, check what measures they have in place for handling a security breach.

Ease of Use

The easiest way to use machine learning is through an Application Programming Interface (API).

APIs integrate smoothly into software, require a low initial investment, and avoid hidden maintenance and hardware costs.

The API should support popular coding languages like Python, Java, and R to make it easier for your engineers and data scientists.

Transparency

To find the best fit, it can be useful to test models from two different services to see which one offers the best balance of speed and accuracy for your data. Many services offer demos or free plans for testing – it may be prudent to avoid those that do not.

Pricing should also be clear. Costs are based on usage and should be explained upfront before you make a commitment.

Maintenance and Support

Post-implementation

support and maintenance are crucial when choosing an AI/ML service provider.

A reliable provider will be dedicated to ensuring their solutions continue to work well after they are deployed. This includes providing support to address any issues that crop up, thus reducing downtime and business disruptions.

Additionally, the provider should offer ongoing updates and improvements to keep the AI/ML solutions current with industry standards and technological advances.

Conclusion

Businesses that actively take advantage of AI and ML will be better poised as industry leaders in the future.

Selecting machine learning services depends on your unique requirements. While it is a competitive industry, with careful planning and research, your company can get the best results.

Silverskills offers AI/ML services that build products and processes that kick off the next level of growth. Our offerings include process automation, data science, multiple systems integration, maintenance, and more. Contact us today to learn more about our machine learning services.