Insights from Scaling and Maintaining AI Apps on Azure Webinar
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Miyoko Shimura - 26 Sep, 2024
Just wrapped up an incredibly insightful webinar on ‘Scaling and Maintaining AI Apps on Azure’ from the GenAI for Software Developers series held on September 26th, 2024. The session provided developers with invaluable insights into the practical aspects of managing AI applications in a production environment.
🎙️ Today’s Speakers & Hosts
- Liam Hampton (Speaker) - Senior Cloud Advocate at Microsoft
- Moria Dror (Host) - EMEA Regional Reactor PM, Microsoft
- Adi Stein (Host) - Digital & App innovation Azure GTM, SouthEast Europe, Microsoft
Challenges for Scaling and Maintaining AI Apps
A portion of the webinar focused on the challenges and solutions for scaling AI applications. Speaker Liam Hampton presented an insightful discussion on the thoughts and four major challenges associated with scaling AI apps:
1) Cost Considerations
The financial implications of scaling AI applications were highlighted as a primary concern. As AI models grow in complexity and data volume increases, the associated costs for computing resources and storage can escalate rapidly.
2) Skills Gap
The webinar addressed the challenge of finding and nurturing talent with the right skill set to develop, deploy, and maintain AI applications at scale. The rapidly evolving nature of AI technologies requires continuous learning and adaptation.
During the webinar, interesting data on the demand for AI talent was shared. It was revealed that as of September 2024 (at the time of the webinar), there are 786 job postings for AI engineers in London, England, United Kingdom . This number reflects the high demand for AI specialists and is closely related to the challenge of skills gaps.
3) Necessity vs. Trend
A crucial point of discussion was the question: “Do you NEED AI or is it for the clout?” This emphasized the importance of critically evaluating whether AI is truly necessary for a given application or if it’s being implemented merely for prestige or keeping up with trends.
4) Availability of AI Models
The session explored the challenges related to the availability of suitable AI models for specific use cases. This includes considerations such as model performance, licensing, and compatibility with existing systems.
🔄 DevOps for AI Applications
The webinar introduced the DevOps lifecycle, emphasizing its cultural importance in maintaining AI applications. Liam presented a visual representation of the DevOps cycle, showcasing key tools supporting each stage:
- Development (Dev): Plan, Code, Build, Test Tools: Git, GitHub, Cypress (for testing)
- Operations (Ops): Release, Deploy, Operate, Monitor Tools: Azure services, Docker, Kubernetes
- Version Control: Git
- Collaboration: GitHub
- Programming: Pyton, JavaScript, Java, Go etc.
- CI/CD: Integrated throughout the cycle
🔨 Practical Tools and Resources
During the webinar, the speaker also introduced practical tools for the audience. One notable resource shared was a deployable template for scaling AI applications.
- Template: aka.ms/scaling-ai-creative-writer
GitHub: Azure-Samples/contoso-creative-writer
This template, specifically designed for scaling AI creative writing applications, is providing developers with a ready-to-use framework to jumpstart their projects. This hands-on demonstration was particularly valuable, showing real-time deployment of an AI application using azd and Azure services.
I’m truly grateful for the opportunity to participate in this insightful webinar!
#AzureAI #AI #CloudComputing #DevOps #MachineLearning #Webinar
This article was originally published on LinkedIn.
