Predicting Storms and Building Networks: Vertex AI & Model Garden in Telecom

Google’s Vertex AI platform is a fully-managed, unified environment for developing and deploying machine learning models at scale. At Next ’25, Vertex AI received significant upgrades, including integration of advanced weather forecasting models and an expanded Model Garden catalog of foundation models and datasets. Google DeepMind’s latest weather AI, known as WeatherNext, represents state-of-the-art forecasting, delivering superior accuracy and speed in predictions​. Additionally, Vertex AI’s Model Garden now offers over 200 pre-trained models, ranging from Google’s own models to third-party and open-source models (including the full portfolio of Allen Institute for AI models)​. This rich model ecosystem – spanning text, vision, video, and more – can be tested, customized, and deployed easily on Vertex AI. These enhancements mean telecom companies can leverage cutting-edge models (like high-fidelity weather predictors or industry-specific NLP models) without building them from scratch.

Use Cases in Telecom: Vertex AI’s new capabilities open up a range of AI-driven scenarios in telecom:

• Predictive Network Maintenance (Weather-Aware): Weather is a major factor in telecom network reliability – storms can knock out cell towers, and extreme weather can affect signal quality (for example, heavy rain fading microwave links). With built-in access to WeatherNext models, a telecom can predict weather impacts on its infrastructure. For instance, using Vertex AI, engineers could run daily forecasts for each service region and predict the likelihood of weather-induced outages. An AI model could combine WeatherNext’s 15-day forecast with historical fault data to flag, “There’s an 80% chance of service degradation in Region A this weekend due to the forecasted storm.” This allows proactive maintenance (like staging repair crews or reinforcing equipment) before the storm hits. Such AI-driven forecasting can greatly reduce downtime by preparing the network for extreme conditions.

• Capacity Planning & Network Optimization: Telecom networks experience fluctuating demand influenced by factors like events, seasons, and yes, weather. Vertex AI’s platform can host a demand forecasting model that incorporates weather predictions as an input. For example, a model might learn that on rainy days, mobile data usage spikes (as more people stay indoors streaming video). By using WeatherNext’s output, the telco can more accurately forecast traffic and pre-adjust network capacity (e.g. allocate more bandwidth to certain cell sites) to maintain quality of service. Similarly, for long-term capacity planning, knowing a region has an upcoming dry season or monsoon season could influence where to bolster backhaul links or add redundant paths. The built-in weather model provides a reliable data feed for these planning algorithms​.

• Quick AI Solution Prototyping with Model Garden: The expanded Model Garden is a boon for telecom AI teams. They can rapidly prototype solutions by selecting from hundreds of models. For example, suppose a telco wants to implement an NLP system to summarize customer call transcripts and detect common pain points. Instead of training a language model from scratch, they might grab an open-source model from AI2 via Model Garden (for instance, a conversational text summarizer) and fine-tune it on their call center data. This could yield a customized summarization model in days rather than months. Likewise, for image processing tasks (like detecting equipment damage from tower inspection photos), the team could deploy a pre-trained vision model from Model Garden and fine-tune it on a small set of labeled images of telecom equipment. The variety of models (including ones from Anthropic, Meta, etc.) means the telco can choose one that best fits the task, whether it’s code generation for automating network config (using a code model) or time-series forecasting for user growth (using a suitable AI model).

• Geospatial Analytics and Site Planning: Telecoms often rely on geospatial data (maps, satellite imagery, demographic data) when expanding coverage. Vertex AI’s integration with Google Maps data for grounding​ and availability of geospatial models can streamline these tasks. A use case might be an AI agent that evaluates optimal locations for new 5G towers: it can use Google Maps grounding to account for real-world geography and population clusters, and perhaps even use an open model that analyzes satellite images to identify buildings or terrain features. The telco’s planners could simply specify the target area and coverage goals, and the Vertex-hosted model could output suggested tower sites with reasoning (e.g. “High population density here and line-of-sight availability – good candidate for a small cell”). By connecting to reliable mapping and location databases, Vertex ensures these agent recommendations are up-to-date and fact-based​.

• Custom AI for Telecom Operations: With Vertex AI’s unified platform, telecoms can build bespoke AI services that integrate multiple data sources. For instance, a churn prediction model might combine structured data (billing, usage patterns) with unstructured data (customer support chats). Vertex’s new AI Query Engine can help here by allowing models to directly tap into unstructured text data alongside traditional data​. Another example is a 5G spectrum optimization tool: researchers could fine-tune a published reinforcement learning model (available via Model Garden) that learns to allocate radio spectrum dynamically under various conditions. Vertex AI’s training infrastructure, with the latest TPUs like Ironwood, would enable running such complex training efficiently. Essentially, telecoms can exploit Vertex as a one-stop shop to access state-of-the-art AI building blocks – from weather to language to vision – and assemble them into solutions tailored to telecom challenges.

  
  

Integration into Workflows: Deploying Vertex AI solutions in telecom workflows involves a mix of data integration and application development. Data from telecom systems (network metrics, OSS data, customer data) needs to be made available to Vertex AI, typically via Google Cloud storage, BigQuery, or streaming pipelines. For example, to use weather-based predictions, the telco would feed historical network outage data and maintenance logs into BigQuery. Then they might set up a Vertex AI Pipeline that daily: calls the WeatherNext model for each region, merges it with the latest network data, and runs a custom predictive model to output risk scores. These results can then be piped back into business applications – e.g., displayed on a network operations center dashboard or sent as alerts to field teams. Vertex AI’s APIs make it straightforward to integrate model predictions into existing tools; a network management system could call a Vertex endpoint to get, say, a congestion prediction and then automatically adjust network parameters.

The Model Garden integration is largely about simplifying model selection and training. Telecom data scientists can browse the Model Garden (via Vertex AI Studio UI or CLI) for a suitable model. Once chosen, integration might mean fine-tuning that model on telecom data using Vertex’s training jobs. The resulting model is deployed as a service (endpoint) that other software can call. For instance, an internal CRM might call an NLP model endpoint to summarize a customer’s interaction history on-the-fly for a support agent. Vertex AI’s managed endpoints handle scaling and serving, so the telecom doesn’t worry about provisioning servers – integration is as easy as an API call to the model.

Moreover, Vertex AI’s new features like Model Optimizer and Agent support can be integrated to improve performance. The Model Optimizer can automatically choose among multiple models to give the best result for a query based on cost/latency preferences​. A telecom could integrate this by deploying several variants of a model (perhaps a large one for high-accuracy needs and a smaller one for quick responses) – the optimizer will route requests appropriately, ensuring a good balance of speed and accuracy without manual intervention.

Integration also extends to on-prem or hybrid scenarios. If the telecom has data residency requirements, they might use Google Distributed Cloud so that Vertex AI (and even Gemini models) run on-premises​. This way, sensitive data never leaves their data center, but they still call the Vertex AI APIs similarly. Lastly, connecting Vertex AI agents with telecom’s API ecosystem is vital: using tools like Apigee, the telco can expose certain network operations or customer account actions as APIs, which Vertex AI’s agent solutions (or ADK-built agents) can invoke. This enables a closed-loop system where AI not only makes predictions but can act (e.g., schedule maintenance crews via an API call to the field management system).

Technical Benefits:

• State-of-the-Art Accuracy: By using Google’s frontier models (like WeatherNext for weather or other top-tier models in Model Garden), telecoms get high accuracy and reliability in predictions. For example, WeatherNext’s advanced ML approach yields more reliable forecasts up to 15 days out than traditional methods​, directly translating to better planning in telecom operations. Improved accuracy in demand forecasts or anomaly detection means fewer surprises in network performance and more efficient resource utilization.

• Reduced Development Time: The breadth of pre-built models in Model Garden and Vertex’s tools significantly shorten the AI development lifecycle. Telecom data science teams can skip months of model training by fine-tuning existing models that are “enterprise-ready”. This allows faster experimentation and deployment of AI solutions. For instance, an NLP problem can be tackled by an existing language model (like Anthropic’s Claude or Meta’s Llama2 in the Model Garden​) rather than creating a new model. Faster development means the business reaps benefits sooner and can iterate quickly as requirements change.

• Scalability & Performance: Vertex AI is built on Google’s robust infrastructure, including the new Ironwood TPU inferencing chips which offer 5x compute capacity of previous gen​. For a telecom handling millions of predictions (say, real-time call quality scoring on every call, or a nationwide deployment of a customer chatbot), this ensures low latency and the ability to scale seamlessly. The telco doesn’t need to architect for scale – Vertex will auto-scale model endpoints as usage spikes, maintaining responsiveness. This is especially important for telcos that might experience sudden bursts of activity (e.g., network outage triggers thousands of customer queries).

• Unified Platform (Data to AI): Vertex AI’s tight integration with Google Cloud’s data services (BigQuery, Dataflow, etc.) creates a smooth data-to-insight pipeline. BigQuery’s data can be accessed directly for training or inference, meaning no cumbersome data transfers. This reduces latency and complexity in solutions; for example, the BigQuery AI Query Engine can co-process data with an LLM at query time, letting analysts get answers that combine raw data and AI reasoning in one step. For a telecom, that means a query like “Which network element is likely causing the latency issue, given these log snippets?” could be answered by BigQuery + a language model together. The outcome: faster insights without jumping between platforms.

• Domain-Specific Customization: With the Model Garden’s open models and fine-tuning capabilities, telecoms can achieve models tailored to their domain. They benefit from Google’s research while injecting their own data for relevance. For example, by fine-tuning a base LLM on telecom jargon and support logs, they get a model that understands terms like “LTE handover” or “OSS alarm codes” intimately, leading to more relevant responses in that context. This level of customization can yield higher prediction accuracy and user satisfaction compared to off-the-shelf models that don’t speak the telecom language.

• Tooling and MLOps Efficiency: Vertex AI provides end-to-end tooling – from notebooks with AI assistance to pipeline orchestration and monitoring. Telecom AI teams can track model performance drift, data changes, etc., via Vertex’s dashboards. The platform handles versioning and can do A/B testing of models easily. This means technical benefits like faster debugging, easier updates, and robust deployment processes. In a telecom setting, where one might deploy dozens of models (for network, customer, and business functions), having a unified MLOps framework ensures reliability and consistency across all those models.

  
  

Deployment Considerations:

• Data Preparation & Quality: The adage “garbage in, garbage out” applies. Telecom datasets (like network logs or customer profiles) can be massive and messy. To fully leverage Vertex AI’s models, the telco must invest in data cleaning and integration. Weather forecasts combined with network data only help if the historical network data is well-labeled (e.g., precisely timestamped outage records). Setting up pipelines to continuously feed clean data into the AI system is a challenge. They may need to use Google Cloud Dataflow or similar to preprocess streaming data (like formatting syslogs, filtering noise) so that any model (like an anomaly detector) isn’t confused by irrelevant or corrupted data.

• Model Selection & Complexity: With over 200 models available​, choosing the right one is non-trivial. Telecom teams need to evaluate models for their specific use cases (accuracy vs. size vs. latency). For example, a huge language model might be very accurate for analyzing trouble tickets, but too slow to use in a real-time assistant. The Model Optimizer can help dynamically, but initially, experts must benchmark a few candidates. This model selection process requires AI expertise, which might mean upskilling staff or bringing in AI partners. It’s a new kind of decision-making that telecom engineering teams (traditionally focused on networks) will have to get comfortable with.

• Cost Management: Running large-scale AI, especially with multiple models, can be expensive. Every API call to an LLM or every batch prediction on big data incurs cost. Telecoms must monitor the usage of Vertex AI services. For instance, using the weather model for hundreds of cell sites every hour might accumulate costs, so they might choose to only do detailed forecasts for high-priority sites or certain times. Budgets need to account for cloud inference costs, and the team should use features like cost alerts or budget quotas. Using open-source models via Vertex could mitigate costs (if those models can run more efficiently on CPUs/GPUs that the telco already pays for), but often the highest accuracy comes from large proprietary models.

• Skill and Training: Adopting Vertex AI at full scale means training or hiring for new skill sets. Data scientists need to know how to fine-tune models, engineers need to understand deploying and calling ML models, and decision-makers must understand AI outputs. The organization should plan for training programs or workshops (possibly with Google Cloud’s support) to ensure teams know how to use the platform. A telecom might also consider establishing a cross-functional AI Center of Excellence to guide projects – as AI becomes pervasive, it touches network ops, IT, and business units alike.

• Regulatory Compliance & Data Residency: Telecoms operate in regulated environments. Customer data and certain network data might be subject to local data residency laws or telecom regulations. If using Vertex AI cloud services, the telco must ensure compliance – e.g., using cloud regions that align with data residency requirements. In some cases, the option to deploy on-prem (via Google Distributed Cloud) might be chosen for sensitive data​. Compliance teams should be involved early to audit how data is used in AI models (for instance, if customer usage data is used to train a model, privacy rules like GDPR require ensuring it’s properly anonymized or consented). Additionally, outcomes of AI (like decisions that affect customers) may need to be explainable to regulators; telecoms should document how models like churn prediction work and have processes to explain decisions if needed.

• Maintenance and Model Lifecycle: AI models are not one-and-done – they require maintenance. Data drifts, user behavior changes, and models can degrade over time. The telco must monitor performance and retrain or update models regularly. For example, if a competitor launches a new plan, the churn model’s features might need updating to account for that factor. If climate patterns shift, even the weather model integration may need re-tuning for new normals. Using Vertex AI means updates to underlying Google models might happen automatically (e.g., if Google improves WeatherNext or releases Gemini 3, etc.), which is great but the telco should validate that those updates don’t adversely affect their use case. A formal model governance process should be established to review models periodically, incorporate new data, and ensure they still meet business objectives.

  
  

By leveraging Vertex AI and its expanded Model Garden, telecom companies can accelerate their AI initiatives – whether it’s making their networks smarter or their customer interactions more insightful. The combination of Google’s best models and the telecom’s own data can yield powerful results, but success will depend on careful integration, cost/quality trade-offs, and diligent governance in deployment.

Full disclosure: this blog was crafted with a little help from AI (because who better to write about AI than AI itself?). It helped organize my excited, caffeine-fueled notes from Google Cloud Next '25 into something coherent—no small feat. Thankfully, I still get credit for the enthusiasm.