MLOps has emerged over the last few years to simplify AI projects by offering a set of practices and tools to manage all stages of development in an automated manner. Its main goal is to reduce development time, speed up and minimize deployment-related risks by improving model reliability and stability. When using an MLOps stack to develop a model, the transition from experimentation to production can be done with just a few clicks, whereas it could take up to 6 months previously.
A true revolution in AI and data monetization, implementing an MLOps workflow can be done in two distinct approaches: building your own stack or using an end-to-end platform like Craft AI. Both cases serve different needs, and the goal of this article is to help you identify the best solution for you.
To know what suits you best, it's important to understand the operational challenges that each option addresses. Here are the main criteria to consider:
- Compatibility with the company's existing stack.
- Flexibility and customization: do you have the ability to configure your tools to meet the specific needs of your teams?
- User experience: without quick adoption of the selected solutions, the goal of streamlining with MLOps will not be achieved.
- Security: are your data and code adequately protected during deployment?
- Explainability: how to industrialize the deployment of large-scale AI projects without close monitoring of executions?
- Support: does the chosen solution provide you with resources and qualified interlocutors to solve problems and share best practices?
Before diving into the construction of a stack or the acquisition of a platform, if you're looking to refresh your understanding of MLOps, I suggest you read our article"A Beginner's Guide to MLOps."
Buying a platform, pros and cons
The first advantage may seem counterintuitive, but it is financial. It will cost you significantly less to pay for a turnkey solution than to do it all manually. To give you an idea, we have set up an ROI calculator that you can consult here.
The principle of a platform such as the one developed by Craft AI is to enable a data scientist to be fully autonomous in deploying a Python application on a large scale, without having to rely on developer teams to refactor the code or DevOps teams to set up and manage infrastructure at each stage of model development and deployment. In addition, turnkey platforms such as Craft integrate FinOps expertise, which makes it very easy to optimize infrastructure costs.
The initial costs of setting up your own MLOps solution will be very high in terms of time and money to hire a specialized team. Here are the steps that make up an MLOps platform that you would need to set up yourself:
- Source code management
- Feature storage
- Model training and selection
- Pipeline construction
- Joint management of code, data, models, metrics, etc. versions
- Model deployment
- Automated testing
- Continuous integration and deployment (CI/CD)
- Hosting and production deployment
- Monitoring and management of deployed models
- Automated retraining
Companies that build their own platform may lack expertise in MLOps, which can lead to errors and delays. Building a team capable of creating a stack and then managing it on a daily basis is not only extremely expensive but also very complex, given the tight job market in AI-related fields, which is not in favor of employers. It will also be necessary to provide internal training on the use of the various tools put in place.
In addition to the financial and HR aspects, time to market is crucial. The months spent developing an internal solution are time lost for other tasks and a delay in bringing your applications to market. A turnkey platform will allow you, after a few hours of training, to deploy as many models as you want with just a few clicks.
Months Vs. Hours. The time you take to build your stack depends on a number of parameters, such as the complexity of the stack you need, the technical skills of your teams and the resources available: how many full-time ML engineers do you have? At the TWIMLcon conference in San Francisco, a survey revealed a consensus ranging from a few quarters for the least ambitious projects with a few full-time ML Engineers, to 2 years for a serious deployment with a team of a dozen engineers.
By opting for an integrated platform, maintenance is considerably simplified. With a pre-built platform, there is virtually nothing to do in terms of maintenance, allowing you to concentrate more on the essential tasks associated with machine learning. On the other hand, when you decide to build your own infrastructure, it requires considerable work to maintain and update all the components, which can take a lot of time and effort.
The issue of security should play a crucial role in your choice. The open source tools available can have major security flaws, as their code is accessible to anyone and can be exploited by malicious actors. By opting for a paid-for platform, you generally benefit from solid security measures, which are regularly updated and reinforced by the service providers. This gives you peace of mind that your data and infrastructure are protected.
Finally, the advantage of a single point of contact in the event of a problem or unavailability with a tool is undeniable. When you use a platform, you have dedicated technical support that can help you if you run into difficulties. On the other hand, if you build your own stack, you have to turn to different open source communities for help, which can be more complex and less effective in terms of resolving problems quickly.
Yes, we said above that acquiring an integrated platform costs much less than creating your own solution. But it all depends on how your budgets are allocated. The cost of building a platform is mainly measured in man-days invested, which represents a lost of productivity, and while the sums involved may be significant, you have no money to pay. If you have sufficient human resources and a lot of time on your hands, there may be no point in using funds to acquire an external solution.
By choosing a third-party MLOps platform, you're committing to a partnership. This allows you to benefit from their regular updates and ongoing technological advancements. But in the unsystematic case where the service provider requires a commitment period, it's important to assess the current features available and the provider's development roadmap. This allows you to determine if the platform's existing capabilities meet your needs and if their future plans align with your business goals, offering you long-term benefits and growth opportunities. It is up to the supplier to provide you with these guarantees.
Building a stack, the pros and cons
The "Build" option as we understand it here is not about developing a platform in-house, but rather creating a technical stack based on the myriad of open source solutions available on the market.
There is also a tool that allows you to visualize and mock up your stack: MyMLOps.com
Whether due to regulatory constraints, internalization strategy, or innovation needs, there are plenty of reasons to create your own AI application deployment stack. However, be careful to properly assess your needs as the costs involved can be significant.
While creating a platform may be more expensive than using a turnkey solution, it is possible to come out ahead in certain cases.
By owning your own MLOps stack, you have total control over all aspects of your infrastructure and processes. You are not limited by the functionality and policies of an external supplier. You can make changes, improvements and adjustments at any time, without depending on a third party.
Although building a bespoke MLOps stack may require significant initial investment, it could be cost-effective in the long term. You don't have to pay recurring fees for using an external platform, and you have direct control over the costs associated with your infrastructure and tools. You can choose the technologies that best suit your team and your infrastructure, select the frameworks, tools and platforms of your choice, without paying for features you wouldn't need.
Building a bespoke MLOps stack requires an in-depth understanding of your business needs and best practice in model deployment. This allows you to develop internal expertise within your team and become more autonomous in managing your MLOps infrastructure.
Building a tailored solution can be complex and time-consuming. It requires careful planning, technical expertise and adequate resources. Development times can be longer than expected, which can delay the release of your models and potentially affect your projects.
If you don't have the necessary technical skills in-house, it will cost you a lot of money. You will need to hire external experts to help you through the process of designing, developing and implementing the infrastructure. Long-term maintenance and upgrade costs also need to be taken into account.
If you don't have an in-depth knowledge of model management, infrastructure and security, you run a significant risk of designing a sub-optimal solution. It may be deficient in terms of performance, security or maintainability.
Maintenance and scalability
You need to take into account the challenges of ongoing infrastructure maintenance, updates, security patches and technical support. This requires dedicated resources and specific technical expertise, in addition to construction. What's more, if your business is growing fast or you need to manage large amounts of data, the MLOps stack you've custom-built may not keep up. You will need to make significant adjustments to your infrastructure to manage this growth, which will undoubtedly be complex and costly.
In conclusion, acquiring a ready-to-use MLOps platform offers a number of advantages over building a stack. It offers significant financial savings, avoids the need to set up a costly specialist team, and enables models to be put into operation quickly. What's more, maintenance is simplified, security is enhanced and technical support is available in the event of a problem. Although building a custom stack can offer greater flexibility, it involves design risks, supplier dependency and higher long-term costs. Depending on the level of maturity in deploying models and managing infrastructures, acquiring an MLOps platform seems to be the best option in most cases.
To learn more about integrating MLOps into your AI innovation projects, ask for a demo of Craft AI's MLOps platform.
Written by Hélen d'Argentré