• With the cloud, enterprises can easily experiment with machine learning capabilities and scale up as projects go into production and demand sees an uptick.
  • It’s nothing like it when those experiences help users and consumers get what they need when they need it.

Many people are familiar with leading technology platforms like Windows, iOS, and AWS. Platforms are a group of technologies that serve as a base from which other applications are built and scaled. These platforms enable most of today’s advanced technology capabilities and help organizations deliver cutting-edge customer experiences.

In a bid to maintain pace with the scale and complexity of the technological capabilities brought by big data, Artificial Intelligence (AI), and machine learning, many organizations are building sophisticated internal platforms. Businesses are beginning to understand that investing in the infrastructure requires training, testing, and deploying models and is crucial for long-term viability.

Business and IT experts have forever expressed opinions about a single-purpose solution and an Enterprise Resource Planning (ERP) solution. Enterprise technology platforms have yielded significant benefits and have helped turn technology into a competitive advantage. Let’s learn how.

The evolution of enterprise platforms

Enterprise platform is referred to a set of technologies and tools that form the base and upon which other applications, processes or technologies are developed. Such solutions are used to accomplish cross-functional duties and other capabilities that are mostly delivered by one or more enterprise systems.

Crucially, enterprise systems are a pool of integrated software applications that manifest different capabilities and work with shared data.

AI and machine learning are steadily making their way into enterprise applications in areas such as customer support, fraud detection, and business intelligence. In their bid to offer a better customer experience, organizations are leveraging cloud-native platforms such as Kubernetes that can run large AI and ML workloads.

What are the Benefits of Machine Learning in the Cloud?

With the cloud, enterprises can easily experiment with machine learning capabilities and scale up as projects go into production and demand sees an uptick. The cloud does not need advanced artificial intelligence or data science skills to make intelligent capabilities accessible. Leading technology companies such as AWS, Microsoft Azure, and Google Cloud Platform offer various machine learning options that do not call for deep knowledge of AI, machine learning theory, or a team of data scientists.

Best practices to develop cloud-based machine learning platforms

Even as cloud-based applications offer greater safety and efficiency, it’s often difficult to manage them appropriately. Below we have listed a few best practices in cloud-based app that can help you minimize possible downsides and make the most of all the benefits these practices provide.

Start small: Even the best testing and Quality Assurance (QA) environment can miss problems that aren’t found until something goes into production. Start with a small number of people when making big changes that can affect customers meaningfully. Once you see that things are working in production at a small scale, you can scale up. Try using associates only for the initial population when small change impacts external customers.

If something looks too good to be true: Exception monitoring is a great way to ensure that execution matches your intent. The goal is often to have no exceptions at all. For instance, latency should never be more than 200 milliseconds. If your exception reporting never shows any errors, the possibility is that monitoring is broken. Always force an exception to make sure it works as it should.

Be honest and communicate well:  Share decisions, progress, and plans with stakeholders. Besides clarifying what you are working on, you should also emphasize what you are not working on right now. Invest in the documentation that makes it easy for people to contribute and join the platform.

Get serious about being well-managed: Platform owners always keep platform performance in mind. All problems should be identified with the help of controls and automated alerts. Exceptions should be dealt with right away. On priority, get to the bottom of the problem and make changes to stop them from happening again. If there are no issues, it must be celebrated appropriately so that teams feel appreciated.

Focus on business goals: Building great platforms can take time. It is essential to put the work in sequence so that business value can be gained at each step. This keeps the team going, builds trust, and starts a positive cycle.

Work backwards from a well-defined end state: Before you start building, spend sufficient time aligning on the end state architecture and how you plan to get there. Make sure your architecture is set up to allow self-service and contributions from the start. Better yet, design the platform with the idea that people outside your business or organization will use it.

Analyze how long it will take, then double it: It’s important to take the time to think about all the capabilities you need to build at the start and then figure out how much work each one will take. When your tech teams combine this information with their speed to determine how long it will take to build each feature, add a 50% buffer.

Build a cross-functional team, even if it slows you down: The team size does not always matter. At a minimum, product managers, engineers, and designers are required. Allot these functions to those who truly understand the platform users.


Opportunities are galore when a team has a strong culture and is supported by apt platform technology. Businesses must experiment with newer, more innovative products and experiences by combining cloud-native platforms and data. It’s nothing like it when those experiences help users, and consumers get what they need when they need it.