Daniel Hand, Field CTO for APJ, Cloudera, on trends to emerge in 2024.
Twelve months ago, Generative Design AI appeared on the fringes of the Gartner hype cycle for emerging technologies.
Today, Gartner believes Generative AI (Gen AI) is close to the peak of inflated expectations.
IDC research highlights how two-thirds of APAC organizations are exploring or investing in Gen AI this year.
As 2024 approaches, I expect companies to intensify efforts on operationalizing and improving Gen AI and adjust their approaches to managing growing volumes of data across environments – especially the cloud – to Drive flexibility and growth.
Here are some trends we will see next year:
Strong MLOPS and Data Integration will help operationalize Gen AI
ChatGPT and other software-as-a-service (SaaS) based Large Language Models (LLMs) have presented significant data privacy challenges for organizations.
Rapid advances in open source LLMs like Meta’s Llama-v2 have delivered comparable performance to ChatGPT and present a viable alternative.
However, Gen AI models are difficult to move from the lab into production in a scalable and reliable way – being typically shared between multiple applications and therefore posing greater data integration challenges compared with traditional ML models.
In 2024, I expect organizations to continue to focus on developing strong Machine Learning Operations (MLOPS) and data integration capabilities.
Organizations will double down on RAG and Fine-Tuning to optimize LLMs
There are several approaches to optimising the performance of LLMs, including Prompt Engineering, Retrieval Augmented Generation (RAG) and Fine-Tuning.
RAG uses content from a knowledge base to enrich the prompt and provide necessary context. A key component of the RAG architecture is a database of knowledge base content that is indexed in a special way.
User questions are encoded in a mathematical representation that can then be used to search for the content that’s nearest to it in the database. The user’s question, as part of the prompt, is then sent to the LLM for inference. Providing both the question and domain context delivers significantly better results.
RAG has proven to be an effective approach to adopting LLMs because it does not require any training or tuning of LLMs – while still delivering good results. It does, however, require data engineering pipelines to maintain the knowledge base repository and a specialized vector database to store the indexed data.
I believe that RAG will continue to be an accessible approach to Gen AI for many organizations in 2024.
One approach to fine tuning that has gained a lot of interest in 2023 is Performance Efficient Fine Tuning (PEFT) which trains a small neural network on domain specific data and sits alongside the general purpose LLM.
This provides most of the performance benefits of retraining the larger LLM but at a fraction of the cost and required training data.
Fine-tuning LLMs requires stronger ML capabilities but can lead to greater efficiency, explainability and more accurate results – especially when training data is limited.
In 2024, fine-tuning approaches like PEFT will be increasingly used by organizations, both for net-new projects and replacing some of the earlier RAG architectures. Uptake to be greatest within organizations with larger and more capable data science teams.
Organizations will shift from Cloud First to Cloud Considered
Cloud computing will continue to be an important, transformative technology in organizational data strategy in 2024.
In 2023, some businesses adjusted their cloud strategy shifting from a cloud first approach to a considered and balanced stance aligned with the conservative moves made by most large organizations.
These organizations have settled on a cloud native architecture across both public and private clouds to support their data and cloud strategy – the additional architectural complexity associated with cloud native being offset with the flexibility, scalability and cost savings it provides.
The resulting data fabric across public and private clouds provides the foundation for an intelligent, automated and policy Driven approach to data management.
Data Management Automation, Data Democratization, Zero-Trust Security will remain top of mind
Observability across infrastructure, platforms and workloads will play an increasingly important role in 2024 – as a precursor to automating intelligent platforms that are highly performant, reliable and efficient.
At the core of the intelligent data platform will be operational data used to train ML models with data practitioners continuing to push for greater democratization of data and greater self-service options. This is aligned with one of the most important principles of the Data Mesh paradigm.
The most innovative organizations empower data scientists, data engineers and business analysts to get greater insight from data without going through data gatekeepers. Removing friction from all stages of the data lifecycle and increasingly providing access to real-time data will be a focus of organizations and technology providers in 2024.
Hybrid cloud native architectures, adoption of third-party SaaS and platform-as-a-service (PaaS) services and a strengthening of cybersecurity continues to Drive a focus on data security, Zero-Trust and clear separation of responsibilities for data management.
Zero-Trust requires continuous authentication and authorization of users and systems working with data.
These entities will increasingly be granted the minimum permissions required to perform a given task with strong auditability. This will be a forcing function to Drive innovation within data governance and management while meeting the increasing demands to democratize access to data.
In 2024, I expect technology to increasingly simplify the implementation and enforcement of Zero-Trust both within organizations and across them as data federation becomes an increased area of interest.
An impending migration to Open Data Lakehouses
2022 witnessed significant innovation within Data Lakehouse implementations with leading industry data management providers settling on Apache Iceberg as the de facto format.
Iceberg’s rapid adoption as the preferred open technology influenced several data management providers to change their open source strategy and build support for it into their products.
In 2024, I expect to see a steady migration of data and workloads into Open Data Lakehouse architectures across public and private clouds.
