Methods to manage data and its authorized usage in Google Cloud Platform, including access control through data governance and enforced security measures:

• Fine grained access control in BigQuery by enforcing IAM policies
• Column level access control by defining taxonomies and policy tags
• Best practices for using taxonomies and policy tags
• Row level access control in BigQuery - Access control restriction by using resource tags.

Dive into the practical and strategic considerations when choosing between these two approaches for creating effective AI agents. Prompt engineering has risen as a fast, adaptable, and low-cost way to harness the capabilities of LLMs. However, its performance often correlates directly with the size of the model - larger, more costly models are required to achieve the desired results. This trade-off raises questions about scalability and cost-efficiency, especially for organisations with resource constraints.

On the other hand, fine-tuning offers a path to tailor models for domain-specific tasks or nuanced interactions, delivering consistent performance even with smaller models. While it demands more resources upfront, fine-tuned solutions can lead to significant long-term savings by reducing reliance on oversized models:

• The strengths and limitations of prompt engineering vs. fine-tuning in AI agent development
• Cost implications: why prompt engineering often requires larger, more expensive models to perform well
• Fine-tuning as a solution to achieve domain-specific precision with smaller models
• Case study: the TUI AI travel assistant for the UK market and lessons learned
• A hybrid approach: combining prompt engineering and fine-tuning for best results.

Exploration how our system uses a graph-based approach to store transactions and enhance fraud controls with advanced features, boosting the effectiveness of both ML models and static rules. Presentation of key components of the system, including a real-time feature computation service optimized for low latency, a visualization tool for network analysis, and a mechanism for historical feature reconstruction.

What makes a great meme? Is it the template? The reference to recent events? Or perhaps sheer luck? Using the image embedding pipeline with the refined Vision Transformer model by Google, we explore the memesphere (yes, it's a word) of Reddit, and it's most popular meme subreddit: r/memes. We brew a recipe for the best memes, by analyzing the upvotes and comments statistics. We determine the most similar memes in terms of content and graphics to establish relations and form clusters segregated by meme templates. Finally, we answer the world-shaking question: What was the best meme of last year?

The future of Artificial Intelligence in software testing: its transformative impact within the telecommunications industry. As AI continues to evolve, organizations are leveraging intelligent solutions to optimize testing processes, enhance product quality, and reduce time-to-market. Drawing from real-world use cases from Ericsson AB, this presentation will dive into how AI is revolutionizing testing methodologies, addressing challenges in AI deployment, and setting the stage for the next leap in testing innovation. Attendees will gain actionable insights into integrating AI into testing pipelines, handling the complexities of large-scale deployments, and overcoming the challenges that come with AI adoption in the software testing space.