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KubeCon Q&A: Reshaping the Developer Role in the Age of AI
A KubeCon Q&A with Briana Frank, Vice President of Product & Design for IBM Cloud
1. How has the developer role evolved in the last two years?

As more enterprises adopt AI technologies, we've seen a shift in the role of developers. For one, more enterprises are offloading some of the more operational tasks that developers were responsible for to AI solutions. offloading the operational tasks is allowing developers to be more entrepreneurial and inventing new ways to harness the power of AI to delight users. At the same time, many developers are shifting to become "prompt engineers" - helping to guide AI and LLMs to produce more accurate outputs. There is a huge opportunity here for developers to not only become more familiar with the AI they're using but help their enterprises use AI more efficiently.

2. What are 2-3 skills that are essential to developers today to prepare for the rise of Gen AI and cloud technologies?

For organizations, Gen AI is creating numerous possibilities, but it's also shined a light on the new skills that developers need to adopt and execute tasks with this technology. One of the most important skills is understanding the balance of how to integrate Gen AI into existing applications, while also understanding how to select the appropriate new tools and models for your organization.

Another crucial skill for developers that is being driven by the rise in Gen AI adoption is the need to understand the ethical implications around AI. If AI is not deployed responsibly and transparently, it could have real-world consequences - especially in sensitive, safety-critical areas and within regulated industries. Developers need solutions that can help them manage risk, enhance transparency, and prepare for compliance with future AI-related regulations.

3. How are developers driving the next wave of Generative AI with RAG and open source technologies?

Generative AI has great promise, but in some ways, it can be flawed - such as AI chatbots producing inconsistent results due to the large stores of data they pull from. The next wave of generative AI requires fine-tuning of foundational models, which Retrieval-Augmented Generation (RAG) and open-source technologies are supporting. RAG, for example, has emerged as a promising solution to ground large language models (LLMs) on the most accurate, up-to-date information, allowing them to produce increasingly accurate results and enabling greater generative AI outcomes for organizations across industries using LLMs.

InstructLab is an example of a revolutionary open source technology that puts LLM development into the hands of the open-source developer community, allowing them to collectively contribute new skills and knowledge to any LLM. Developers can rapidly iterate and merge skill contributions together, enabling one base model to be improved by the entire community's contributions and in turn, produces more consistent and accurate results.

4. Why are more developers adopting serverless and low code technologies to support AI initiatives?

AI initiatives and solutions have brought on a daunting influx of data that developers are tasked with managing, especially when they must manage the underlying infrastructure as well. Some developer teams are spending too much time worrying about their surrounding infrastructure when deploying AI applications on Kubernetes, taking valuable time away from where their focus should be: driving business outcomes and innovation through these AI applications. One way to think about this would be like driving a car - you want to be able to drive your car without worrying about the engine under the hood. Serverless and low-code technology support AI initiatives by making sure that the engine will continue to run smoothly no matter what.

With IBM Cloud Code Engine serverless computing, IBM deploys, manages and autoscales our clients' Kubernetes cluster. This allows for an increase in productivity, while decreasing time to deployment. Developers have access to an infrastructure where they can run containers in the cloud without worrying about ownership or management of their environments. Overall, serverless and low code technologies help combat the challenges developers face when it comes to Kubernetes and managing AI workloads.

5. What are some concerns that developers should be aware of regarding data management in AI technologies, especially when it comes to international data exchange?

The rise in data privacy regulations - both in the U.S. and abroad - have created a hyper-focused lens on security and compliance when it comes to data management. This focus will only continue as international laws continue to evolve and security risks become more sophisticated. This is especially true for developers working in highly regulated industries, such as financial services and healthcare.

In response to the evolving regulatory landscape, federal banking agencies have enacted rigorous requirements on data retention. Developers need solutions that ensure that data retention is unalterable at the storage layer to safeguard it from any tampering or manipulation. We offer a solution called Cloud Object Storage that addresses this issue head on. We also offer clients the IBM Security and Compliance Center, which is an integrated solutions suite that has an "always on" approach to security and compliance. It implements controls for secure data and workload deployment so developers can ensure all their security and compliance goals are being met.

6. What are some 2025 predictions for developer and enterprise AI trends?

One trend that we're already starting to see impact developers are new compliance and security regulations related to AI and data management. We expect these regulations to grow in the years to come. Enterprises need to prepare for these potential regulations now by adopting solutions with the right technology and controls in place so that compliance doesn't slow down your development velocity.

Another trend we can anticipate seeing is that AI is going to be more accessible to a broader pool of IT talent. No longer are you expected be a developer to train a Large Language Model (LLM). Open source projects like InstructLab are enabling contributed updates to existing LLMs in an accessible way, allowing more individuals to contribute to AI projects even if they don't have a deep skillset in AI/ML technologies.

Lastly, we are seeing more enterprises bring on platform engineers to support their evolving AI needs. Platform engineers are responsible for building and maintaining the platforms for software development, which could include anything from administering deployable architectures with set configurations, managing deployment, and reporting the success of their configurations and application environments.

 

To learn more about Kubernetes and the cloud native ecosystem, join us at KubeCon + CloudNativeCon North America, in Salt Lake City, Utah, on November 12-15, 2024. 

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Briana Frank, VP Product & Design IBM Cloud at IBM

Briana-Frank 

Briana leads the Product Management and Design organizations within IBM Cloud. Briana strives to make the world work better for her clients by creating intuitive cloud user experiences through product management best practices, design thinking and operational rigor. Briana is an entrepreneur with a proven track record of building high growth award winning products by listening to the client's needs and following through with experiences that delight users.

Briana is also a strong advocate for diversity and inclusion in the tech industry.
In addition to her work at IBM, Briana is also an active member of the tech community and a patented inventor. Briana volunteers her time by mentoring small business owners in underrepresented groups. She frequently speaks at industry events and has published numerous articles on the topics of digital transformation and cloud computing.

Published Wednesday, October 23, 2024 7:30 AM by David Marshall
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