Machine Learning Platforms: Should You Buy Commercial or Build In-House

Machine learning can be a powerful tool if you’re looking for a way to improve your business operations. When it comes to machine learning platforms, you have two main options: buy a commercial outlet or build your own in-house.

Both have pros and cons, so how do you decide which option is best for you? In this blog post, we’ll explore the pros and cons of commercial and in-house machine learning platforms so that you can make an informed decision. So, let’s get started!

Should You Buy Commercial or Build In-House?

The term “machine learning” covers various techniques and technologies, from simple linear models to deep neural networks. Machine learning contains algorithms that are used to learn from data. These algorithms can detect patterns automatically and build models that can be used to make predictions.

Machine learning operations, also called MLOps, is a software engineering practice aiming to streamline and automate the process of delivering machine learning models to production.

By using machine learning operations, you can optimize machine learning models for speed and accuracy while reducing the risk of bias and errors. It helps to ensure that machine learning models are deployed quickly and safely without compromising quality or performance.

As machine learning has become more popular and accessible, several commercial platforms have emerged, offering various advantages in ease of use, support, and scalability.

However, these platforms come with a high cost in terms of initial investment and ongoing subscription fees. For many organizations, whether to buy a commercial machine learning platform or build their in-house solution is complex.

There are pros and cons to both approaches. And the best decision for any given organization will depend on many factors, including budget, expertise, and specific needs.

  1. Why You Should Choose a Commercial Machine Learning Platform

Commercial machine-learning platforms offer many advantages. They are usually easy to use and require minimal training, even for users without prior experience. In addition, most platforms come with extensive support resources, including online documentation and forums.

This can be a huge benefit for organizations that do not have the internal expertise to troubleshoot problems themselves. Finally, commercial platforms are typically highly scalable, making it easy to add more users or data as needed.

There are some substantial drawbacks to take into account, though. First and foremost is cost: even the most basic commercial machine-learning platforms can be expensive, and prices can quickly spiral upward as more features are added.

In addition, many commercial platforms require organizations to sign long-term contracts, which can pose a risk if the platform does not meet expectations. Finally, there is always the potential for vendor lock-in: if an organization builds its solution on a proprietary platform, it may be difficult or impossible to switch to another platform later.

  1. Why You Should Build an In-House Machine Learning Platform

Building an in-house machine learning platform has many benefits over purchasing one commercially. 

Firstly, it allows organizations to control the data used to train the models. This is important from a privacy and security perspective, as it means that sensitive data does not have to be shared with a third party.

Secondly, it allows organizations to design their algorithms and models rather than being limited to those offered by a commercial provider. This can be important for organizations with specific requirements or who want to be at the cutting edge of machine learning research.

Finally, building a machine learning platform in-house can be more cost-effective in the long run, as it eliminates the need to pay license fees or subscription charges.

For many organizations, the decision of whether to buy or build will come down to a matter of budget and expertise. Those with limited budgets may find that they cannot afford a commercial platform, while those with more expertise may prefer to build their solution to have more control over the final product.

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Factors to Remember When Choosing a Commercial Machine Learning Platform

Choosing the right platform can be challenging, as a wide range of options is available on the market. There are a few key factors that should be considered when selecting a machine learning platform, including:

  1. Data Type

Machine learning requires large quantities of data to train models effectively. Therefore, it is essential to consider what type of data will be used to choose a compatible platform. For example, some platforms are explicitly designed for structured data, while others can work with structured and unstructured data.

  1. Size of the Data

In general, the more data available, the better results can be achieved with machine learning. Therefore, it is essential to consider how much data will be available when selecting a platform. Some platforms work with extensive data sets, while others may not be able to handle such volumes of data.

  1. Computing Resource Needed

Machine learning models can require significant computing resources to run effectively. Therefore, it is vital to consider the available computing resources before selecting a platform. Some platforms are designed to run on specific types of hardware, such as GPUs or TPUs, while others can run on more standard hardware, such as CPUs.

  1. Cost

Commercial machine-learning platforms can vary significantly in cost, depending on their features and functionality. Therefore, it is essential to consider the available budget before selecting a platform. 

Some platforms may offer free trials or other pricing models that could make them more affordable for certain businesses.

Source FreePik

Things to Remember When Building a Machine Learning Platform In-House

Building a machine learning platform in-house can be a great way to get ahead of the competition, but there are a few factors to consider before starting such a project. 

First, you’ll need a team of experienced data scientists familiar with machine-learning algorithms and libraries.Second, you’ll need access to high-quality data that can train the machine-learning models. 

Finally, you’ll need the infrastructure to support the training and deployment of machine learning models. Building a machine learning platform in-house may be the right move for your company if you can check all these boxes.

Final Thoughts

When it comes to machine learning platforms, organizations have two main options: buy a commercial platform or build their own in-house solution. Both strategies have advantages and disadvantages.

Ultimately, deciding whether to buy or build should come from a careful cost-benefit analysis. Organizations need to weigh both approaches’ benefits and drawbacks to make the best decision for their specific needs.