Build (In-house) V/S Buy (Managed Service) for Machine Learning Deployment
Dec 28, 2023
Deep dive into the advantages & disadvantages of in-house v/s managed services way of of deploying machine learning models
Introduction
In the realm of machine learning, the successful deployment of models is as crucial as their development. Businesses and organisations are increasingly harnessing the power of machine learning models for tasks ranging from predictive analytics to natural language processing. However, the choice between using managed services for machine learning inference and building an in-house infrastructure for the same is a critical decision that can significantly impact the efficiency, cost-effectiveness, and scalability of machine learning operations. Both choices have their merits. For early stage businesses, its a no-brainer to go with managed services. For large scale enterprises, a deeper case-to-case basis evaluation should be done.
In this article, we'll explore the advantages and drawbacks of both approaches to help you make an informed decision. To save your time, we have highlighted the key message upfront. Please refer to the below table for a bird’s eye view.
- Refer our blog for a deep dive on compute costs analysis. Costs have been estimated assuming that the load is unevenly distributed (most real world scenarios).
- The engineering effort of doing in-house has been estimated basis our own experience of setting up kubernetes clusters, provisioning and monitoring of GPUs.We have further validated by speaking to 10+ startups
Now, Lets dive into details to understand things at a deeper level.
BUILD (Doing In-house)
Building an in-house machine learning inference system involves setting up the necessary kubernetes clusters and associated infrastructure yourself. You hire an internal team to handle everything, from troubleshooting network issues, setting up new systems to implementing security and privacy controls.
Advantages :
- Closer to your business: Since your team is part of your organization, they will have a better real time understanding of your specific evolving needs and company culture.
- Customization: In-house systems allow for extensive customization enabling you to integrate machine learning with your existing processes and tools seamlessly.
Disadvantages :
- High Costs: Setting up and maintaining an in-house infrastructure can be expensive. You'll need to invest in hardware, software licenses, and skilled personnel to manage and maintain the system. Refer Lilian Weng’s thread on optimisations for inference
- Talent scarcity: It is unrealistic to expect expertise in various tech-infra things from a small team that has been juggling with tech-infra as one additional thing on their plate. your team can loose focus from core business operations.
- Limited bandwidth of the internal team: it can be challenging for the team to gain in-depth expertise and stay up-to-date with the rapidly evolving field of digital infrastructure. This limitation can impact the efficiency and effectiveness of tech operations within the organization.
- Limited Scalability: As your machine learning requirements grow, expanding an in-house solution can be complex and costly.
- Security and privacy: the responsibility for security and compliance rests entirely on your team, demanding significant investment and expertise. Although that allows for enhanced control, enabling tailored security measures and high data privacy.
BUY (Managed Services: Container/Serverless)
Using managed services for machine learning inference, involves outsourcing the infrastructure and management to an external provider who specialize in machine learning operations. These professionals handle your tech infrastructure, which includes tasks like provisioning and management, inference performance optimisations, cybersecurity, data backup and disaster recovery.
Within managed services, you have option of deploying a container or going serverless. Containers provide a dedicated GPU for your machine learning model, ensuring consistent performance. In contrast, serverless offers a 50%-60% cost advantage as you only pay for GPU usage during your workloads. No charges apply during idle times when the GPU is available to other users. However, in case of serverless, you need to be mindful about the cold starts (read this thread to understand why cold starts are important). Choosing a serverless provider with fastest cold starts is critical for giving a pleasant experience to your end users. The choice revolves around balancing performance consistency, cost efficiency, and the specific needs of your machine learning applications.
Advantages:
- Releases your bandwidth: With managed services, you can focus on your core business activities, leaving the experts to manage the rest.
- Expertise: You get a team of experts in machine learning infrastructure, ensuring that your models run efficiently, and you benefit from their experience.
- Nuances resolved faster: The experts would have been addressing the specific problem statements around the clock, which equips them with the knowledge to unravel the intricacies/nuances of numerous potential scenarios that may arise.
- Its a cost-effective model – you only pay for the services you need eliminating the need for substantial upfront investments
- Scalability: Managed services are scalable, allowing you to adapt to changing workloads and requirements effortlessly.
- Security expertise: Managed services provide outsourced security expertise, relying on dedicated professionals and shared environments for cost-effective measures.
Disadvantages:
- Loss of Control: When using managed services, you relinquish some control over the infrastructure. This may not be suitable for organizations with stringent data privacy requirements or specific customization needs. Ensure that your managed service provider is infosec compliant. That will be crucial to safeguarding your data privacy
- Downtime: You become dependent on the service provider for infrastructure and may face challenges if the provider experiences downtime or service interruptions. Prior to making a commitment to the service provider, be sure to assess their uptime status.
Conclusion
To conclude, sharing our take on ML deployment and inference: if you want extensive customisations and can afford the high costs associated with it (both monetary and bandwidth wise), you can go for building in-house solution; otherwise always go for a managed service solution which will release your bandwidth by 10X to 20X and save recurring compute costs by 50%-60% depending on spikes in workloads.
Inferless specializes in providing managed services for inference, which means we take care of the technical aspects, allowing you to focus on what truly matters: your projects and goals. Our uptime of 99.993% is one of the best in the industry. Inferless is in the final stages of achieving information security compliance and will soon proudly hold certifications for SOC2, GDPR, and ISO 27001. These certifications are not just badges of honor; they are a testament to our dedication to ensuring your data remains safe and confidential.
Links to other informational pieces
We provide an unbiased, well researched opinion on machine learning and AI topics. Do checkout our other blog pieces:-
