Builders & Brews: Stockholm MLOps Hack Edition – Hands-On AI Engineering With Nebius And Tavily + Video

Introduction:

The convergence of MLOps practices with accessible AI infrastructure is reshaping how developers build and deploy machine learning systems. On September 18, Stockholm MLOps, in partnership with Nebius and Tavily, will host Builders & Brews: Hack Edition – a global event series bringing together AI builders, ML engineers, and founders for workshops, technical mentorship, and focused build time. This event is part of a 20-city global tour spanning Tokyo, Singapore, London, New York, and beyond, offering participants hands-on support, platform credits, and the opportunity to compete in the Nebius Global AI Hackathon for over $50,000 in prizes. As AI workloads increasingly demand robust infrastructure, security, and operational excellence, events like this serve as critical training grounds for the next generation of AI engineers.

Learning Objectives & Secrets:

Objective 1: Master Nebius AI Cloud for Production AI Workloads – Gain hands-on experience with Nebius AI Cloud, an AI-centric platform offering infrastructure and computing capability for AI deployment and machine learning solutions. Learn to leverage features like Nebius Echo, a built-in AI agent for natural-language infrastructure control, and Workload Identity Federation (WIF) for secure environment communication.
Objective 2 Secret Tip: Optimize AI Search with Tavily API – Tavily provides a search API purpose-built for AI agents and RAG pipelines, returning ranked results with optional LLM-generated answers. Secret: Configure the `includeDomains` and `excludeDomains` parameters to filter search results, reducing noise and improving retrieval accuracy for domain-specific applications. The free tier offers 1,000 credits per month – enough to evaluate the API thoroughly before committing.
Objective 3 Secret Tip: Leverage Token Factory Credits for Cost-Efficient Inference – Nebius Token Factory provides access to 60+ open-source models via API. Secret: Stack your credits – event attendees receive Nebius Token Factory credits and Tavily credits. Combine these with the Nebius for Startups program ($5,000 introductory credits) for significant cost savings on training and inference.

You Should Know:

1. Setting Up Your Nebius AI Cloud Environment

Nebius AI Cloud provides a streamlined workflow for provisioning AI infrastructure. The platform’s Aether 3.6 release introduces new serverless capabilities that allow developers to build, test, and scale AI workloads without managing underlying infrastructure.

Step‑by‑Step Guide:

Create a Nebius Account – Sign up at the Nebius AI Cloud portal. New accounts receive approximately $1 in trial credit.
Access Nebius Echo – Upon login, Nebius Echo, an AI agent built directly into the web console, is available immediately with no setup required. Use natural language commands like “Create a GPU instance with NVIDIA RTX PRO 6000” to provision resources.
Provision an Instance – Follow the step-by-step instance creation workflow. Select from available GPU options, configure storage, and set up networking.
Configure Workload Identity Federation (WIF) – For secure cross-environment communication, enable WIF to allow environments to communicate without exposing credentials.
Set Up Budgets – Use the new Budgets feature to monitor and control spending across your projects.

Linux Command Example:

 Install Nebius CLI
curl -fsSL https://nebius.com/install.sh | bash

Authenticate with your API key
nebius auth login --api-key YOUR_API_KEY

List available GPU instances
nebius compute instance-types list --filter "gpu"

Launch a GPU instance
nebius compute instance create \
--1ame my-ai-instance \
--type gpu-standard-1 \
--image ubuntu-22.04 \
--ssh-key ~/.ssh/id_rsa.pub

2. Integrating Tavily Search API for AI Agents

Tavily’s search API is designed specifically for LLM-powered applications, providing real-time web search with high grounding accuracy (93.3% on OpenAI SimpleQA). The API integrates seamlessly with LangChain, LlamaIndex, and Vercel AI SDK.

Step‑by‑Step Guide:

Obtain a Tavily API Key – Sign up for a free account at Tavily to receive 1,000 API credits per month, with no credit card required.

2. Install the Tavily SDK – For Python:

pip install tavily-python

For JavaScript/TypeScript:

npm install @tavily/ai-sdk

Initialize the Client and Perform a Search – Python example:
```
from tavily import TavilyClient</li>
</ol>

tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY")
response = tavily_client.search(
query="latest MLOps best practices 2026",
search_depth="advanced",
include_answer=True,
include_domains=["mlflow.org", "kubeflow.org"]
)
print(response["answer"])
print(response["results"])
```
4. Integrate with LangChain:
```
from langchain_tavily import TavilySearch

tool = TavilySearch(
api_key="tvly-YOUR_API_KEY",
search_depth="advanced",
max_results=5
)
result = tool.invoke("What are the latest trends in AI infrastructure?")
```
1. Configure Search Parameters – Customize with parameters like includeDomains, excludeDomains, and `auto_parameters` for latency vs. relevance tuning.
3. Building an End-to-End MLOps Pipeline on Nebius

Modern MLOps requires versioning everything – not just code, but data, environments, and hyperparameters. Automated gates prevent costly failures by validating data and evaluating models before production deployment.

Step‑by‑Step Guide:
1. Version Your Data and Models – Use tools like DVC or MLflow to track datasets, model artifacts, and hyperparameters. Store model artifacts as OCI artifacts for immutability.
2. Implement CI/CD for ML – Build multi-level CI/CD pipelines that include:
– Data validation gates – Automated checks for data quality and schema compliance
– Model evaluation gates – Offline evaluation against a holdout dataset before allowing deployment
1. Deploy with Shadow Deployment – Deploy the new model in parallel with the existing one, compare predictions without impacting production, and validate for 2-4 weeks before switching over.
2. Monitor Drift and Performance – Implement monitoring for data drift, concept drift, and model performance degradation.
3. Automate Retraining – Set up automated retraining pipelines triggered by performance degradation or scheduled intervals.
Kubernetes-1ative MLOps Command Example:
```
 Deploy Kubeflow Pipelines on Nebius Kubernetes
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=master"
kubectl wait --for=condition=ready pod -l app=ml-pipeline -1 kubeflow --timeout=300s

Submit a pipeline run
kubeflow pipelines run submit \
--pipeline-id my-pipeline \
--experiment-1ame production-training \
--run-1ame "model-training-$(date +%Y%m%d)"
```
4. Security and Governance for AI Workloads

Nebius AI Cloud 3.6 introduces expanded security and governance features for sensitive workloads. These include enhanced user access controls, Bring Your Own Image (BYOI) for custom security-hardened environments, and centralized notification management.

Step‑by‑Step Guide:
1. Configure IAM Policies – Define granular access controls for users and service accounts using Nebius’s identity and access management.
2. Enable Workload Identity Federation – Allow workloads running on Nebius to authenticate to external services without storing long-lived credentials.
3. Use Bring Your Own Image (BYOI) – Build custom images with your organization’s security baseline, compliance tools, and monitoring agents.
4. Set Up Budget Alerts – Configure budget thresholds in the unified Notification Center to receive alerts when spending approaches predefined limits.
5. Audit and Monitor – Enable audit logging for all API calls and infrastructure changes to maintain compliance with regulatory requirements.
5. Participating in the Nebius Global AI Hackathon

The Nebius Global AI Hackathon, hosted in partnership with NVIDIA, invites AI builders to create with open-source AI and compete for more than $50,000 in cash prizes, including a $500 city winner prize. The hackathon runs online, so attending the Stockholm event is not required to participate.

Step‑by‑Step Guide:
1. Register – Sign up for the hackathon at the Devpost page.
2. Choose a Challenge – The hackathon focuses on developing high-performance AI systems using NVIDIA open-source models on Nebius infrastructure.
3. Build with Nebius and Tavily – Utilize Nebius AI Cloud for compute and Tavily Search API for real-time data retrieval.
4. Submit Your Project – Follow the submission guidelines, which typically include a demo, code repository, and project description.
5. Network and Learn – Attend Builders & Brews events for workshops, technical mentorship, and team formation.
What Undercode Say:
- Key Takeaway 1: The AI Infrastructure Stack Is Democratizing – Platforms like Nebius AI Cloud and Tavily are lowering the barrier to entry for AI development. With serverless capabilities, natural-language infrastructure management via Nebius Echo, and purpose-built search APIs for AI agents, developers can focus on building intelligent applications rather than wrestling with infrastructure. The 20-city global tour reflects a deliberate strategy to cultivate a worldwide community of AI builders, ensuring that expertise and resources are distributed globally rather than concentrated in traditional tech hubs.
- Key Takeaway 2: MLOps Is Entering the Governance Phase – The MLOps landscape is shifting from simply deploying models to governing them throughout their lifecycle. Versioning everything (data, models, code, environments), automated evaluation gates, shadow deployments, and drift monitoring are no longer optional – they are table stakes for production AI. The integration of security features like Workload Identity Federation and Bring Your Own Image into AI platforms signals that security is becoming a first-class concern in AI infrastructure, not an afterthought.
Prediction:
- +1 Global AI Builder Communities Will Drive Innovation – The 20-city tour and $50,000+ hackathon prize pool will accelerate the formation of local AI communities, creating distributed innovation networks that rival Silicon Valley’s historical dominance.
- +1 AI Infrastructure Will Become Increasingly Automated – Natural-language infrastructure management via tools like Nebius Echo will become standard, reducing the need for specialized DevOps expertise and enabling more developers to build production-grade AI systems.
- -1 Security and Compliance Challenges Will Intensify – As AI infrastructure becomes more accessible and automated, the attack surface expands. Organizations must invest in governance, monitoring, and security automation to prevent data breaches and compliance violations.
- +1 MLOps Best Practices Will Become Codified – The industry is converging on standard MLOps practices: versioning everything, gating changes with evaluations, and implementing safe rollout strategies (shadow, canary, blue-green). This standardization will reduce friction and increase the reliability of AI systems in production.
- -1 Skill Gaps Will Persist – Despite democratization, the demand for professionals who understand both AI and infrastructure will outpace supply. Events like Builders & Brews and certification programs like Nebius Academy are critical for closing this gap, but the shortage will remain a bottleneck for AI adoption in the near term.
▶️ Related Video (78% Match):

https://www.youtube.com/watch?v=17nQ74S5SfU

🎯Let’s Practice For Free:

🎓 Live Courses & Certifications:

Join Undercode Academy for Verified Certifications

🚀 Request a Custom Project:

Secure, high-velocity infrastructure and disruptive technological engineering. Contact our engineering team for high-tier development and proprietary systems:
[email protected]
💎 Smart Architecture | 🛡️ Secure by Design | ⭐ Trusted by Thousands

IT/Security Reporter URL:

Reported By: https://lnkd.in/p/e4WrbfQ2 – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

🔐JOIN OUR CYBER WORLD [ CVE News • HackMonitor • UndercodeNews ]

💬 Whatsapp | 💬 Telegram

📢 Follow UndercodeTesting & Stay Tuned:

𝕏 formerly Twitter 🐦 | @ Threads | 🔗 Linkedin | 🦋BlueSky
Share this:

Listen to this Post

Introduction:

Learning Objectives & Secrets:

You Should Know:

1. Setting Up Your Nebius AI Cloud Environment

Step‑by‑Step Guide:

Linux Command Example:

2. Integrating Tavily Search API for AI Agents

Step‑by‑Step Guide:

2. Install the Tavily SDK – For Python:

For JavaScript/TypeScript:

4. Integrate with LangChain:

3. Building an End-to-End MLOps Pipeline on Nebius

Step‑by‑Step Guide:

Kubernetes-1ative MLOps Command Example:

4. Security and Governance for AI Workloads

Step‑by‑Step Guide:

5. Participating in the Nebius Global AI Hackathon

Step‑by‑Step Guide:

What Undercode Say:

Prediction:

▶️ Related Video (78% Match):

🎯Let’s Practice For Free:

🎓 Live Courses & Certifications:

🚀 Request a Custom Project:

IT/Security Reporter URL:

🔐JOIN OUR CYBER WORLD [ CVE News • HackMonitor • UndercodeNews ]

📢 Follow UndercodeTesting & Stay Tuned:

Share this:

Related Posts: