In the ever-evolving sector of asset management, the integration of artificial intelligence (AI) stands as a strategic imperative. A recent survey led by Hedge Week further underscores the growing trend of Asset management funds leveraging AI trading models to gain a competitive edge. “Over 60% of quants are using AI to generate returns and nearly half for autonomous trading”, according to HedgeWeek. While the benefits of successful AI integrations and their proposed impact on alpha generation seem self-evident, implementing AI strategies is still fraught with complexity and a staggering 85% of AI projects are still fated to fail. Harnessing the power of AI while mitigating the downside of project failures has served as the catalyst for asset managers to explore an emerging trend within the sector called MLOps. However, determining the opportunity cost of Buying vs Building an MLOps platform from scratch is a bit of a conundrum for asset management firms.

What IS Machine Learning Operations (MLOps)?

MLOps stands for Machine Learning Operations. MLOps is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production and then maintaining and monitoring them. MLOps is a collaborative function, often comprising data scientists, DevOps engineers, and IT.

The following sections examine the pros and cons of each approach with regard to talent, performance, and cost.

Building From Scratch

For asset management firms considering building MLOps platforms from scratch, the appeal of having your own proprietary platform fully customized and maintaining complete control remains enticing. But it requires a more substantial investment in time, human resources, and capital weighed against the urgent need for accelerated development cycles and rapid deployment. Here, we take a deeper look at the pros and cons of building from scratch.

Pros of Building From Scratch

The biggest advantage of building your MLOPs platform from scratch is that it gives you complete control. Rather than using an existing platform and trying to engineer it to fit your needs, a built-from-scratch MLOps system can be designed specifically with your fund’s goals front and center.

When you build your own platform from scratch, you also get full customization of that platform. As your specific needs and workflows evolve, you can prioritize iterations to your platform to meet those needs. If a component of your platform becomes dated or obsolete, your own engineering team, which built the platform to begin with, is there to update it, which means you don’t have to wait for system updates from a third-party provider.

Security is another benefit of building your own platform. Because the platform is completely contained in-house, your data is private and secure, and potential breaches of your company’s intellectual property are much less likely.

Cons of Building From Scratch

There are a few downfalls to building your MLOps platform from scratch. The first is talent scarcity— specifically, attracting and retaining Machine Learning talent is getting increasingly more competitive and not without its cost. Moreover, embarking on an MLOps journey even with prior experience introduces significant risks, including the likelihood of repeating avoidable errors and key challenges such as look-ahead bias, and train/test contamination can significantly escalate the development time and cost of creating your own MLOps platform. The hidden cost of this undertaking is the significant ongoing maintenance burden on the organization, a factor particularly critical for hedge funds where precision and uptime are vital. This maintenance involves regularly updating the platform to keep up with the latest advancements in machine learning technologies but also the continuous monitoring for and addressing of potential security vulnerabilities. This commitment to building an MLOps Platform from scratch can divert valuable resources and attention away from the core activities of the fund.

In the fast-paced and competitive world of finance, where every second can impact outcomes, the need for constant, robust maintenance of a self-built MLOps platform can become a significant operational and financial challenge

Asset managers as fiduciaries must carefully assess the decision to build an MLOps platform from scratch factoring in both the qualitative and quantitative impact this undertaking can have on their respective fund as they look to harness the power of AI to generate Alpha.

Leveraging an Existing Platform

If building an MLOps platform from scratch sounds onerous, you have the option of leveraging an existing platform from a provider like LIT AI.

Pros of Leveraging an Existing Platform

Leveraging an existing platform that serves as a central repository for AI development within your fund accelerates time to value in a myriad of ways and by all accounts is a risk-mitigating strategy worth exploring. Below we will review a few of the pros to be taken into consideration.

Cost-Efficiency: Leveraging an existing MLOps platform is inherently more cost-efficient than building one from scratch. The infrastructure has already been developed and fortified and is typically more feature-rich. Features such as automated Data error detection and correction, Feature Stores, Model Training, Model Archiving, One-click Deployment, GPU usage monitoring, and Explainable AI tools are all easily accessible via dashboards as part of their user interface. Vendors of existing systems also have AI subject matter experts readily available to collaborate with your teams to alleviate technical bottlenecks.
Focus on Core Competencies: 80% of the AI project lifecycle is spent cleaning data rather than creating insights. By opting for a platform like LIT AI, asset management funds can liberate their in-house talent to focus on higher-value tasks. Instead of being bogged down wrangling with the intricacies of MLOps development, Asset management teams can redirect their expertise toward critical areas like trading and investment ideation.

Market readiness: Existing MLOps platforms like LIT AI accelerate the AI development life cycle and users have reportedly been able to Build, Train, and Deploy models in weeks contrasted with typical development cycles that ordinarily would take months to years depending on scope and complexity. Existing MLOps vendors also provide on-premise or cloud hosting tailored to serve your fund’s compliance requirements.

Reliability and Scalability: Working with a dedicated provider like LIT AI ensures systems remain reliable and scalable. The platform is designed to evolve with technological advancements, offering regular updates and infrastructure improvements. This reliability, coupled with first look, and experimentation with bleeding-edge technological advances, is another very compelling value proposition partial to buying vs building an MLOps platform.

Cons of Leveraging an Existing Platform

There are a few drawbacks to leveraging an existing platform. Onboarding a new vendor, training existing team members to properly navigate the user interface, and systems integrations are realities that cannot be shunned and must be contemplated thoughtfully to avoid disruptions to the asset management fund. However, it is prudent for asset managers to assign a dedicated team of internal advocates as project managers to ensure strategic planning, alignment, and execution are established up front by internal teams and your MLOps provider. Lastly, verifying the protection of Intellectual property, maintaining complete model ownership, and avoiding vendor lock-in are variables that should be assessed thoroughly when considering the use of an existing MLOps platform.

The above-mentioned concerns of using an existing platform can be alleviated by working with companies like LIT AI.

Which Solution Is Right for My Hedge Fund?

The decision to buy versus build an MLOps platform is nuanced, as stated above, and must be made with careful consideration of the trade-offs of each approach. However, the fact that best-of-breed AI tools and workflows are being democratized by existing MLOps SaaS platforms makes the purchase of an existing system advantageous for asset managers. We are amidst an inflection point with predictive AI, and a growing number of market participants will view adopting an existing MLOps platform to accelerate the development and deployment of AI models to generate Alpha as the ultimate hedge.

LIT AI — The Future of AI-Generated Alpha

LIT AI is an end-to-end, fully-managed machine learning operations platform that enables teams to Build, Train, and Deploy deep learning models with unparalleled speed and accuracy. To learn more about LIT AI and its cutting-edge MLOps solutions, contact us today for your free demo!

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