Top 5 things to look for when selecting machine learning technology

BrandPost By Sourced Group
Sep 17, 20234 mins
Artificial IntelligenceMachine Learning
Big data and artificial intelligence concept. Machine learning and circuit board. Deep learning
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In the race to take advantage of AI, many organisations are rushing into machine learning projects only to crash and burn before the project even gets off the ground.

Common pitfalls include technical complexity, security worries and rapidly rising unexpected costs.

Fortunately, MLOps is a new field that aims to help companies avoid these issues and achieve success. Here’s are the top five considerations in selecting the right one to meet your organisation’s objectives.

1. Safety first: protect your sensitive data

Machine learning projects often involve training on sensitive data. Ensuring this data remains secure is paramount – it should be non-negotiable. The right MLOps platform prioritises security against potential threats. Sourced Group, an Amdocs company that helps organisations with their MLOps strategy, uses a framework that evaluates each platform’s security measures, scoring them out of 100.

2. Focus on user experience

Machine learning is powerful, but the user interfaces can be difficult to master. Products that have an intuitive interface can significantly boost the productivity of data scientists and engineers using the tools, leading to much greater chance of success in the organisation overall. Sourced uses a framework for clients that assesses the user-friendliness of each platform, considering factors like the intuitiveness of the user interface and the availability of documentation. A platform that scores highly ensures teams can easily adapt and maximize its features.

3. Avoid vendor lock-in.

One thing that’s certain about AI is that we’re only at the beginning of its adoption and platforms are evolving extremely rapidly. So, the age-old problem of vendor lock-in is very important to avoid, as today’s best toolset may not be the front runner in a year or two.

The ideal MLOps platform allows models to be transferred across different infrastructures.

Flexibility is key to allow your organisation stay ahead of the competition by using the best platform or tool at any given point in time. The evaluation matrix used by Sourced to help its clients develop their MLOps strategy awards higher scores for platforms that offer greater portability.

4. Focus on operational simplicity

It’s important that machine learning tools can operate relatively autonomously and don’t require extensive manual deployment or a great deal of feeding and watering by operational staff to stay running. MLOps aims to simplify operations. The evaluation framework rates each platform’s operational complexity, examining ease of deployment and maintenance. Platforms that simplify operational procedures score lower, indicating their ability to ease the burden on operational staff.

5. Ensure return on investment

Every decision has financial implications. The best MLOps platform offer value without costs that are disproportionate to the value being returned. The framework’s cost analysis considers licensing fees, infrastructure expenses, and ongoing maintenance costs. Platforms that are more cost-effective fare better in this evaluation.

Rating the platforms overall

After following these five evaluation steps, each MLOps platform is rated—low, medium, or high—based on its performance across the criteria.

Notably, operational complexity and cost are graded negatively to highlight their importance. These ratings are visualized on a radar diagram, offering a clear snapshot of each platform’s strengths and weaknesses.

Real-world MLOps success at a major financial institution

Sourced Group helped a major APAC financial institution to devise a robust MLOps strategy that aligned with the bank’s goals. The institution saw immediate success.

Sourced Group drew from its extensive expertise in evaluating MLOps platforms using its five-point assessment framework, tailored to the bank’s specific needs.

The importance of a robust MLOps strategy in today’s data-driven landscape cannot be overstated. By carefully considering security, user experience, portability, operational complexity, and cost, businesses can confidently select an MLOps platform that aligns with their objectives to achieve success with their machine learning projects.

Download our comprehensive whitepaper, “How to Choose a Machine Learning Operations Platform,” and gain valuable insights into selecting the ideal MLOps solution that aligns with your goals and ushers in a new era of machine learning excellence.