Google Cloud Update: June 2024

Google Cloud Update Summary - June 2024

The summary of essential information about Google Cloud in June 2024

AI & Machine Learning

⭐ Google has named Google as a Leader in the Magic Quadrant™ for Data Science and Machine Learning Platforms.

Google has been named a Leader in the Magic Quadrant™ for Data Science and Machine Learning Platforms by Gartner®. This recognition is attributed to Google's pioneering AI research and development, which includes technologies like transformers, compute-optimal training, optical circuit switch networks, and Tensor Processing Units. Their research has over 2.6 billion citations. Read more.

⭐ Introducing Vertex AI Model Monitoring

Google Cloud is launching a new feature called Vertex AI Model Monitoring. This improved system aims to be more versatile and easier to use for monitoring the performance of machine learning models. Here are the key improvements:

• Wider compatibility: You can now monitor models even if they're not deployed using Vertex AI itself. This means they can be running on Google Kubernetes Engine, Cloud Run, or even on other cloud platforms.
• Centralized management: Vertex AI Model Monitoring acts as a central hub for managing all your monitoring jobs. This simplifies overseeing how your models are performing in production.
• Unified monitoring: The system can monitor models used for both real-time predictions (online) and bulk predictions (batch).
  Easier setup and analysis: Configuration is simplified, and the results are visualized directly with the model itself, making it easier to understand how a model is performing. Read more.

⭐ Integrating Gemini and Sheets with BigQuery

You can now use BigQuery as a bridge between Google Apps Script and Gemini for large datasets. 

• Challenge: Traditionally, Apps Script (used for customizing Google Sheets) interacts with Gemini (large language model) for smaller tasks. This might not be efficient for massive datasets.
• Solution: BigQuery acts as an intermediary. You can query your data in BigQuery and send it directly to Gemini Pro in Vertex AI for processing.
• Benefit: This approach is particularly useful for large datasets or if you already use BigQuery with Sheets.
• Setup: BigQuery Apps Script service allows querying BigQuery directly from Sheets. Additionally, you'll need to create a model endpoint in BigQuery specifying the desired Gemini model. Read more.

API Management

⭐ Strengthen Your Dialogflow CX Chatbot Security with Apigee

Organizations frequently choose Dialogflow, Google Cloud's service for creating conversational agents, with Dialogflow CX being a popular choice for its advanced capabilities. However, integrating a chatbot without a middleware solution can compromise security by exposing sensitive credentials, such as service account keys, or using unauthenticated URLs. This approach is not suitable for production systems as it can leave your agent and Google project ID vulnerable. Read more.

Application Modernization

⭐ Gain API performance insights with Apigee custom reports

Monitoring API performance is essential for ensuring a good user experience, protecting the business, and resolving issues proactively. Key metrics to track include response time, throughput, error rate, latency, and availability. Poor performance in these areas can result in user frustration, lost business, and reputational damage, highlighting the importance of optimization. Read more.

Containers & Kubernetes

⭐ Introducing GKE Compliance: Ensure your clusters and workloads adhere to industry standards

Maintaining Kubernetes compliance is challenging due to its dynamic, distributed nature and evolving standards. To address this, Google is introducing a new feature for GKE Enterprise customers: built-in, fully managed GKE Compliance within GKE posture management. This makes achieving and maintaining compliance for Kubernetes clusters easier than ever. Read more.

Cost Management

⭐ Billing data across clouds with the new Looker template and BigQuery views

Google Cloud believes in providing tools for analyzing costs across cloud providers using open standards. As a founding member of the FinOps Foundation and the FOCUS™ project, Google Cloud is introducing a new Looker template view based on FOCUS v1.0 GA to simplify cloud cost management across different clouds. Read more.

⭐ Advancing FinOps: 5 Cost Management Innovations from FinOps X 2024

Google Cloud is leading with new product innovations aimed at unlocking cloud value through continued innovation.

• Making open cloud billing data a reality 
• Speaking in the language of business, not technology   
• Expanding the definition of cost to include carbon
• Modeling what an efficient cloud looks like, in near real-time 
• Sending actionable alerts, not noise  
  
• Read more. 

Data Analytics

⭐ Using multilingual embeddings and vector search in BigQuery

In a globalized marketplace, finding reviews in a customer's preferred language can be tough. This blog post explores how BigQuery's multilingual embeddings, vector index, and vector search can help by converting text into numerical vectors for advanced, language-specific search capabilities, improving the accuracy and relevance of search results. Read more.

⭐ Easily track historical data in BigQuery with Datastream's append-only CDC

Organizations need both a current "source of truth" and a way to track data changes over time. To achieve this, they use change data capture (CDC) to replicate changes from operational databases like MySQL or PostgreSQL to a cloud data warehouse like BigQuery. Google Cloud's Datastream recently introduced an "append-only" mode, which makes it easier and more cost-effective to replicate changes and maintain historical records in BigQuery. Read more.

⭐ Develop your own generative AI chatbot using BigQuery.

Organizations need both a current "source of truth" and a way to track data changes over time. To achieve this, they use change data capture (CDC) to replicate changes from operational databases like MySQL or PostgreSQL to a cloud data warehouse like BigQuery. Google Cloud's Datastream recently introduced an "append-only" mode, which makes it easier and more cost-effective to replicate changes and maintain historical records in BigQuery. Read more.

Security & Identity

⭐ Introducing BigQuery Encrypt and Decrypt Functions for Sensitive Data Protection Compatibility

Organizations gather extensive data for innovation, research, and optimization, necessitating robust data protection to meet various security requirements. Transitioning data warehouses to cloud systems like BigQuery requires safeguarding sensitive data from unauthorized access or exposure. Encryption-based tokenization offers an additional defense layer and precise data control. BigQuery now integrates with Sensitive Data Protection, supporting native SQL functions for seamless, interoperable deterministic encryption and decryption, enhancing data security. Read more.

Source: https://cloud.google.com/blog/products/gcp