MLOps or Machine Learning Operations is a set of processes and practices that automate and scale the deployment and management of machine learning models in production environments. By bringing DevOps principles to machine learning, it enables a faster development cycle, better quality control, and the ability to respond to changing business requirements. In this article we identify four considerations to evaluate in your MLOps software comparison.
MLOps is much more than model design and development. It also includes data management, model retraining, monitoring of the model and continuous development.
The origins of MLOps goes back to 2015 from the research paper “Hidden Technical Debt in Machine Learning Systems” and since then, there has been no looking back. What began as a broad set of practices has, over time, evolved into an independent approach to machine learning lifecycle management.
As per a 2019 report from MIT, 7 out of 10 executives whose companies have made investments in AI reported a minimal impact of the technology on their business. Why? Because developing machine learning models and putting them into production environments are two completely different things. The cost of poor deployment can lead to risk, not being able to scale, and delays to projects taking months and negatively impacting organisations’ top and bottom lines.
Data scientists can create the most effective models for machine learning problems but since they are not dedicated developers, they have limited knowledge of the tools and skills to test, deploy or maintain models. This is where MLOps plays a crucial role.
It facilitates smooth communication and collaboration between operations professionals and data scientists. With MLOps software, it also becomes easier to align models with business and regulatory requirements.
While a few companies have succeeded in generating value with AI, most companies have a hard time with it. MLOps helps companies unlock potential, manage risks, and minimise the bottlenecks associated with machine learning.
Finding the right solution
There is no one size fits all approach when it comes to choosing the right MLOps software. There are internal and external factors that will influence the questions you ask and decisions you make, including company size, team skills, sophistication, and industry to name a few.
Open Source vs Enterprise
When running an MLOps software comparison, one of the biggest arguments is between open source and enterprise software. Open source is a term used to describe software for which the original source code is made freely available. The public is allowed to copy, modify and redistribute the source code without paying any royalty or fee. Enterprise software is software licensed under exclusive legal right of its owner. When purchased, the purchaser gets the right to use the software under certain conditions. However, the purchaser cannot modify or redistribute the same.
Both have their advantages and disadvantages but what works for an organisation depends upon the functionality it is looking upon and skills internally. In a recent survey, it was found that 78 percent of the companies run open source software which clearly indicates the widespread adoption of open source.
In some ways, open source outperforms enterprise software and the reason is being free and flexible. However, there are hidden costs most notably the cost of resources in building a platform internally and the risks associated with it breaking or not achieving what it set out to, especially with open source software not being standardised.
Enterprise software is ready built, standardised, often has more features but is sometimes less configurable. It is more powerful, quicker to use and usually updated regularly with new features to aid customer experience. Those looking for enterprise MLOps software with a user interface that makes it seamless to deploy, monitor and explain models should consider Seldon Deploy Advanced.
MLOps is not only about technology, but also about the processes, operations and people involved. Using the maturity model will help you assess the current effectiveness of a team or tool to figure out what capabilities are needed to acquire next in order to improve performance, and become more mature as an organisation when it comes to machine learning.
Regardless of where you fall on the maturity model, as part of your MLOps software comparison, you should be asking questions around your long term machine learning plans and objectives, and consider the bigger picture to future proof your infrastructure. Most organisations are at the serving maturity stage, but something we’re seeing at Seldon is more interest in machine learning monitoring, and explainability features in highly regulated industries such as financial services.
MLOps is all about accelerating the machine learning lifecycle in a secure and trusted way. MLOps software must provide organisations the confidence of being able to ensure that data is secure and not shared openly, user access controls are developed properly and teams aren’t overlapping. To get models to production faster in a secure way, it is important to leverage multi-factor authentication, role-based authorisation, data encryption, and other security and privacy best practices. When businesses run the majority of their digital infrastructure on the back of ML, it is of paramount importance that the system is secure.
Best of breed vs end-to-end
A huge topic in MLOps which will influence your MLOps software comparison process is whether to go down the path of best of breed or end-to-end platforms. There is no right or wrong answer to this, and the outcome will depend on company and team sizes, skills, company strategies and direction.
Often for smaller companies or teams, it makes sense to go with an out of the box platform with the ability to train models as well – this will mirror the team setup where the number of team members is small and manage the full pipeline. However, a common mistake made is going down this route when the machine learning operation is fastly expanding and becoming more sophisticated in approach. Lengthy implementation periods make it difficult to replace poor-performing technology within the suite with more effective tools, limiting its value and return on investment, which is why we must consider the long-term goals and bigger picture.
For many organisations either now or in the future, you may need stronger serve, monitor, or explainability features, or you may plan to run complex inference pipelines. This is where best-of-breed platforms are more suitable. Flexibility is also an important consideration, in terms of language, connectivity with your wider digital ecosystem or servers in the cloud or on-premise.
Unlock your business potential and manage risk with Seldon
Seldon moves machine learning from POC to production and can handle massive scale. By enhancing time-to-value, your models can get to work up to 85% quicker. Seldon enables you to:
- Transform your organisation by managing your models and workflows at scale
- Boost productivity and reduce costs through improved model performance
- Automate workflows to improve customer experience and increase ROI
- Better understand changes to your data and reduce risk through monitoring and explainability
Those looking to scale their open source machine learning deployment can leverage Seldon Core.
For organisations evaluating monitoring and explainability capabilities, and a ready-built platform, where these more advanced features can be powered by best-in-breed orchestration functionality, Seldon Deploy enables organisations to manage and monitor ML models to minimise risk and understand business impact.