The last couple of years have ushered in a transformative shift in generative AI, with large language models (LLMs) becoming household names, making waves in every industry, and shifting the very foundation of business. Organizations that want to unlock the full potential of AI technologies for their specific niche and workflows must turn to large language model operations (LLMOps), where AI technologies and operational strategies converge. As the key to managing and optimizing AI model lifecycles, LLMOps is constantly evolving. To use the technology and operational strategies effectively, business leaders must stay updated with developments and trends.
Read on to learn about the current landscape and future predictions of LLMOps.
How has LLMOps evolved to the present day?
As more and more industries began adopting AI technologies across diverse applications, there became a pressing need for a specialized framework to facilitate AI model development, deployment, and management. LLMOps emerged to help businesses satisfy the growing demand for agile, scalable, and efficient AI operations. Now, with LLMOps, companies can continuously iterate and improve AI solutions, evolving the models in response to dynamic environmental changes and user feedback, reducing latency, and boosting performance along the way.
5 Future Predictions of LLMOps
Here are some of the innovations and developments that are on the horizon for LLMOps.
1. Higher Prioritization within Organizations
One of the most significant developments in the world of LLMOps is that organizations will need to allocate money and resources to leverage the power of LLMs in particular ways. Ultimately, every industry will evolve as businesses make LLMs and LLMOps a top priority. The increased resource dedication will help:
- Change business models.
- Innovate solutions to complex business problems.
- Create improvements to customer care.
- Transform customer experiences.
- Facilitate automation of time-consuming manual processes.
2. Increasing Use of RAG
Retrieval Augmented Generation (RAG) is a specific AI framework that serves as both a data retriever and a generator. RAG empowers businesses to use LLMs to fetch data from external sources and then create accurate and usable answers, ultimately allowing businesses to power LLMs on their own internal data to generate business-specific outputs. Businesses will stand to benefit from the RAG implementations in the following ways:
- RAG helps address challenges that can arise from limits to LLM knowledge.
- With RAG, businesses can achieve more precise and actionable outputs from the LLM.
- RAG can help simplify exhausting retraining cycles by creating a pipeline for feeding the LLM continually up-to-date and domain-specific data. In this instance, vector databases are also essential and will be addressed below.
3. Expanding Use of Vector Databases
Vector databases serve as the repositories for domain-specific data that RAG fetches. Vector databases also act as the long-term memory banks for LLMs, caching queries and retrieving data from recent interactions. While the use of vectors is not new, the demand for vector databases is projected to increase because the frameworks are valuable for enterprise LLMOps, enhancing the success of semantic searches, supporting high-volume querying scenarios, and reducing the burden of high-level computation.
4. Rise of Cloud-Based Solutions and Edge Computing
LLMOps require immense computing power and high bandwidth and low latency networking solutions. Given this, cloud-based solutions and edge computing will become more commonplace, allowing businesses to leverage the scalability and elasticity of the cloud alongside the real-time processing capabilities of edge computing. These approaches will allow businesses to automate and optimize while keeping LLMs and the associated data safe at the network edge.
5. Training, Upskilling, and Outsourcing
As LLMOps is a relatively new, highly specialized, and quickly evolving technology and operational strategy, many businesses are faced with a scarcity of LLMOps expertise. This year will usher in a wave of professional development alongside an emphasis on outsourcing to achieve effective LLMOps solutions. Businesses will need to dedicate resources to training and upskilling while also strategically outsourcing machine learning (ML) services and custom-tailored LLMOps solutions. The combination of professional development and outsourcing will ensure companies have the internal resources to support and fuel advancement while leveraging the most advanced expertise to develop growth-driving solutions cost-effectively.
Embracing the Future of LLMOps with Encora
The future of LLMOps is unlimited. That's why fast-growing tech companies leverage a relationship with Encora to outsource product development and drive growth. Encora's team of software engineers is experienced with LLMOps and innovating at scale. We are deeply expert in the various disciplines, tools, and technologies that power the emerging economy, and this is one of the primary reasons that clients choose Encora over the many strategic alternatives that they have.
Contact us to learn more about LLMOps.
