David Regalado

AI Time Journal

AI Time Journal is a publishing platform that strives to aware users of and around the world of Artificial Intelligence, growing technology trends, and other industry news. Its final aim is to contribute to steering the development of AI for the benefit of humanity.

Ambassador at AI Time Journal

Data Engineering Latam

Data Engineering Latam is the largest, fastest growing, and coolest data community in Latin America whose mission is to promote the region's talent through the dissemination of talks, workshops, study groups, collaborative help and the creation of relevant content.

Founder

Stealth Startup

I spearhead the engineering and infrastructure area, leading the design, development, and maintenance of advanced data processing systems. My focus is on ensuring the robustness, scalability, maintainability, security, and efficiency of technological solutions, anticipating future challenges to address them proactively.

I foster a culture of collaboration and technical excellence among the data science and engineering teams, promoting best practices that drive innovation and a positive work environment.

I report directly to the company founders, explaining the work we do in simple terms.

Key achievements:

- I reduced costs by 90% by moving workloads to a combination of Dataform and Dataflow jobs.
- I introduced modern techniques such as RAG and semantic search to the company.
- Designed and implemented a cloud architecture tailored to the startup's needs.
- Parameterized the product code, enabling efficient customization for multiple clients.
- Spearheaded the adoption of key practices such as code versioning, infrastructure as code (IaC), and disaster recovery procedures, strengthening the organization's resilience and technological responsiveness.
- I delivered a custom industry data model tailored for retailers.

VP of Engineering

Nebula.io

Nebula is a well funded startup that develops cutting edge technology that combines proprietary algorithms with deep learning neural networks, natural language processing, and generative AI to connect talent with opportunity. 

I leveraged Apache Beam on Google Cloud Dataflow for building cost effective streaming and batch data pipelines. Other technologies include MongoDB, Elasticsearch, PubSub, BigQuery, and other GCP services.

Senior Data Engineer

University of Chicago

The University of Chicago is a top 10 U.S. University, with more than 100 associated Nobel Prize winners. 

I taught the Python for Data Science program. Some topics covered: data manipulation and analysis, training, evaluation and deployment of Machine Learning (ML) models. 

4 cohorts.

Data Scientist - Teaching Assistant

Anomali

Anomali is the leading cybersecurity company that develops and provides intelligence-driven cybersecurity solutions to enhance threat visibility, automate threat processing and detection, and accelerate threat remediation. 

I built data pipelines using Scala to serve high concurrency requests that enrich data about suspicious traffic sources in order to classify them as trustworthy or not.

Clevertech

Projects:

• MadHive – Responsible for the implementation of pipelines involving Google Cloud Platform to retrieve terabyte-scale data in sub-second response time. This enabled data scientist to be more efficient when doing exploratory data analysis with data across the country (U.S.).

 • DevOps — I spearheaded the implementation of modules in Terraform for the use of infrastructure as code on the GCP cloud.

In charge of a team of 2.

Senior GCP Data Engineer

Scotiabank

Projects:

• I spearheaded the Scotiabank's Data Hub. I did the architecture and design. Some features: ETL Automation, Data Observability, alerts, monitoring. In charge of a team of 5.

Data Architect

Indra

Projects 

• Responsible for Big Data projects for the IT area. of Telefónica del Perú. In charge of a team of 5.
• Responsible for the B.I. Factory for the I.T. area. of Telefónica del Perú. In charge of a team of 20.

Data Engineer Tech Lead

DWConsulware 

• Construction of campaign variables, risks, financial products, etc. to be used by the analytical models of data scientists.
• Preparation of reports in Excel with macros
• Monitoring and resolution of incidents in the production network

Data Engineer

Projects:	

●	Implementation of the Interbank Data Warehouse
●	Implementation of the Universidad Peruana de Ciencias Aplicadas Data Warehouse
●	Implementation of the Sura Data Warehouse

Senior Business Intelligence Consultant

Hi there!

Thank you for visiting my profile. I am passionate about data & AI. Throughout my 15 years of professional experience I have seen projects of various types, sizes and industries. 

Summary

In my country, I've worked in the biggest companies like:

- Financial Services: Scotiabank, Interbank
- Telecommunications: Telefónica del Perú, Claro
- Consulting: Teradata Perú, Indra, Encora
- Consumer Credit Reporting Agencies: Equifax
- Ad-Tech: Manya.pe

In those companies, I did the whole data lifecycle: data extraction, ingestion, data manipulation, data modeling (normalization, denormalization, etc), data analysis, data quality assessments. In short, the ETL part (data pipelines) + data analysis. 

Achievements: The Data Warehouses/Lakes and dashboards were delivered to my clients, so they could start making data-driven decisions.

These recent years I've been working for U.S. clients as a contractor. Specifically:

- Academics: University of Chicago
- Cyber Security: Anomali
- Ad-Tech: Clevertech
- Startup: Nebula.io

Achievements: Data pipelines delivered and students gratuated from the University program.

Highlight

- Founder at Data Engineering Latam, the largest, fastest-growing data community in Latin America: https://www.linkedin.com/company/data-engineering-latam
- I taught Python for Data Science in the University of Chicago.
- Recognized as Champion Innovator by Google Cloud
- I led teams of 20 people in 3 different occasions.
- Recurrent speaker and jury in Big Data and Cloud Computing events.

Studies

- Systems Engineer from the Technological University of Peru (UTP), tenth superior.
- With a specialization in Data Science from Johns Hopkins University.
- With a specialization in Data Warehousing for Business Intelligence from Colorado University.
- With a specialization in Data Science and Big Data Analytics from MIT.
- With 4 certifications, 15 specializations and 46 Google Cloud Platform digital badges.
- With studies in Artificial Intelligence at UBA-IALAB

Tech stack

Cloud Computing: Google Cloud (GCP), AWS, Azure
Data Engineering: Apache Beam, Airflow, Spark, Databricks, Snowflake, dbt, Hadoop, Hive, Luigi
Databases: BigQuery, Teradata, MongoDB, Elasticsearch, Oracle, SQL Server, PostgreSQL, Mysql
Data Visualization: Power BI, Tableau, Looker, Data Studio
Programming languages: Python, Scala, R
DevOps & CI/CD: Terraform, Docker, Kubernetes, Git, GitHub

Roles 

CTO, CDO, CDAO, Data Science Manager, Data Engineering Manager, Head of Data, Tech Leader, Data Architect, Data Engineer, Data Scientist, BI Consultant.

This was four years ago. Young David studied as if there were no tomorrow.

At the time, I shared this achievement along with this message:

"If you hear a voice within you saying 'you cannot paint,' then paint, and that voice will be silenced."

—Van Gogh

It remains relevant.

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚𝐧𝐭𝐡𝐨𝐬?

Anthos is a hybrid and multi-cloud application platform from Google Cloud that allows you to modernize and manage applications across different environments, including on-premises servers and multiple public clouds like AWS and Azure. It provides a consistent development and operations model by extending Google Cloud's services and engineering practices to these diverse infrastructures, enabling consistent security, governance, and management. Built on Kubernetes, Anthos helps organizations build new apps, modernize existing ones, and run them securely anywhere. 

𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐚𝐧𝐝 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬

✅ Hybrid and multi-cloud consistency: Provides a unified platform for managing applications across different environments, reducing inconsistencies.
✅ Application modernization: Helps you modernize existing applications and build new cloud-native ones more quickly.
✅ Security and governance: Offers robust security features and allows for the implementation of security guardrails and policy enforcement across all your environments.
✅ Unified management: Enables you to view and manage multiple Kubernetes clusters from different locations through a single interface.
✅ Streamlined development: Supports consistent development and operations models, which can speed up deployment and improve quality through features like centralized logging and tracing.
✅ Infrastructure and service management: Provides tools for provisioning infrastructure, managing containers with Kubernetes, and managing the communication between services. 

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☁️🧑‍💻

👍 Like, 🔗 share, 💬 comment, 👉 follow

I post about Data & AI. I also share thoughts and resources along the way. Follow David Regalado and smash the 🛎 in my profile if you are interested in these topics. Let's connect on other social as well:

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💸📈Unlocking Business Potential with Data & Generative AI ╏ Startup Advisor ╏ Mentor Featured on Times Square ╏ International Speaker ╏ Google Developer Expert

3d isometric version of Lima!!!

(credits: Lesly Zerna & r/aicuriosity for the prompt idea!)

Try Image Prompt 👇

Present a clear, 45° top-down isometric miniature 3D cartoon scene of [CITY], featuring its most iconic landmarks and architectural elements. Use soft, refined textures with realistic PBR materials and gentle, lifelike lighting and shadows. Integrate the current weather conditions directly into the city environment to create an immersive atmospheric mood. Use a clean, minimalistic composition with a soft, solid-colored background. At the top-center, place the title “[CITY]” in large bold text, a prominent weather icon beneath it, then the date (small text) and temperature (medium text). All text must be centered with consistent spacing, and may subtly overlap the tops of the buildings. Square 1080x1080 dimension.

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🇵🇪🧑‍💻

I post about Data & AI. I also share thoughts and resources along the way. Follow David Regalado and smash the 🛎 in my profile if you are interested in these topics.

👍 Like, 🔗 share, 💬 comment, 👉 follow

David Regalado @thecodemancer_ "Google Gemini Nano Banana Pro" Lima Peru

🤯 This is really an improvement!

I wrote about an earlier version of this image generation model here:
https://lnkd.in/etub9QZ2

Now I'm looking forward writing about this new one.

--
🧑‍💻

👍 Like, 🔗 share, 💬 comment, 👉 follow

I post about Data & AI. I also share thoughts and resources along the way. Follow David Regalado and smash the 🛎 in my profile if you are interested in these topics. Let's connect on other social as well:

Medium: https://lnkd.in/eNhy6CBB
YouTube: https://lnkd.in/d3Kc6PMc
TikTok: https://lnkd.in/d8RvZ6p5
Threads: https://lnkd.in/e285fJdt
Topmate: https://lnkd.in/eQjH3RWP 
Sessionize: https://lnkd.in/dipdM7Zs
X: https://lnkd.in/ezjm873T
Instagram: https://lnkd.in/eejcaqzE

There is a quiet part of data engineering that we rarely say out loud: The more maintenance a data architecture needs, the more it favors the external firm that built it.

👇

I've seen too many "Vendor Agnostic" architectures that are really just "Job Security" architectures. They really are Rube Goldberg machines.

I wrote my latest piece, "The Art of KISSing in Data Architecture," to push back against this entropy.

It's about the DIY Trap: Why serverless usually beats "managing it yourself" (unless you love waking up at 3 AM).

I break down the principles (and the traps) here:

https://lnkd.in/eiJQkHiv

💬 Do you trust your vendors? How can you make sure they're doing what's best for your business rather than their own? Share your thoughts in the comments.

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🧑‍💻

👍 Like, 🔗 share, 💬 comment, 👉 follow

I post about Data & AI. I also share thoughts and resources along the way. Follow David Regalado and smash the 🛎 in my profile if you are interested in these topics. Let's connect on other social as well:

Medium: https://lnkd.in/eNhy6CBB
YouTube: https://lnkd.in/d3Kc6PMc
TikTok: https://lnkd.in/d8RvZ6p5
Threads: https://lnkd.in/e285fJdt
Topmate: https://lnkd.in/eQjH3RWP 
Sessionize: https://lnkd.in/dipdM7Zs
X: https://lnkd.in/ezjm873T
Instagram: https://lnkd.in/eejcaqzE

Young David treated his homework like a quiet way to beat yesterday's version of him. This was four years ago. At the time, I shared this achievement along with this message:

"🛫 Fly high, not so the world can see you, but so you can see the whole world! 🌍"

𝐀𝐧𝐭𝐡𝐨𝐬 𝐰𝐡𝐚𝐭?

Anthos functions as a management platform that extends Google Cloud services to run applications consistently across Google Cloud, AWS, Azure, and on-premises data centers, providing a unified management and operational experience. Anthos is explicitly designed as a strong fit for multicloud environments.

𝐊𝐞𝐲 𝐑𝐞𝐚𝐬𝐨𝐧𝐬 𝐀𝐧𝐭𝐡𝐨𝐬 𝐢𝐬 𝐚 𝐆𝐨𝐨𝐝 𝐅𝐢𝐭 𝐟𝐨𝐫 𝐌𝐮𝐥𝐭𝐢𝐜𝐥𝐨𝐮𝐝

✅ Unified Management Console: Anthos provides a "single pane of glass" in the Google Cloud Console to view, monitor, and manage all your Kubernetes clusters and workloads, regardless of where they are deployed (e.g., AWS, Azure, or on-prem). This significantly reduces operational complexity that typically comes with managing disparate cloud platforms.

✅ Operational Consistency: By standardizing on open-source technologies, primarily Kubernetes (GKE), Istio, and GitOps, Anthos provides a consistent application platform and developer experience across all environments. This means application teams can build and deploy applications once ("build once, run anywhere") without worrying about platform-specific details.

✅ Centralized Security and Policy Enforcement: Security policies and configurations (like role-based access controls and resource quotas) can be automated and enforced uniformly across all clusters using Anthos Config Management. This is crucial for maintaining compliance and a strong security posture in a fragmented multi-cloud landscape.

✅ Avoids Vendor Lock-in: Anthos is built on open standards, which gives organizations the flexibility to choose where to deploy their applications based on business needs (e.g., cost, performance, or data residency requirements) and move workloads between platforms if necessary.

✅ Application Modernization: It helps organizations modernize legacy applications by offering tools like Anthos Migrate, which can automatically transform VM-based workloads into containerized applications that can then run on any supported environment.

✅ Service Mesh for Cross-Cloud Communication: Anthos Service Mesh (based on Istio) helps manage, observe, and secure communication between microservices across different clouds, enabling robust, resilient distributed applications. 

𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬

Anthos is designed specifically to address the core challenges of managing multi-cloud environments, making it a very strong and suitable solution for organizations seeking unified operations, security, and flexibility across their cloud landscape. 

Anthos involves a learning curve and significant enterprise investment, so it's typically best suited for large organizations with complex, distributed environments.

We tend to overcomplicate data stacks for the wrong reasons:

❌ Because it looks good on a resume.
❌ Because a consultant sold a "bespoke" 6-month project.
❌ Because we are afraid of "Vendor Lock-in" so we create "Complexity Lock-in" instead.

I just published "KISSing the Data Architecture" to explore why the simplest solution is usually the most robust one.

I dive into a specific architectural showdown: The Rube Goldberg Machine vs. The Modern Semantic Layer. One requires a team of engineers to keep the lights on; the other just runs.

The goal isn't to be clever. The goal is to build systems that don't give you a nervous breakdown.

Check it out below 👇

https://lnkd.in/eyPsuHcA

--
🧑‍💻

👍 Like, 🔗 share, 💬 comment, 👉 follow

I post about Data & AI. I also share thoughts and resources along the way. Follow David Regalado and smash the 🛎 in my profile if you are interested in these topics. Let's connect on other social as well:

Medium: https://lnkd.in/eNhy6CBB
YouTube: https://lnkd.in/d3Kc6PMc
TikTok: https://lnkd.in/d8RvZ6p5
Threads: https://lnkd.in/e285fJdt
Topmate: https://lnkd.in/eQjH3RWP 
Sessionize: https://lnkd.in/dipdM7Zs
X: https://lnkd.in/ezjm873T
Instagram: https://lnkd.in/eejcaqzE

David Regalado KISSing the Data Architecture @thecodemancer_

Greg Coquillo

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

Building Agentic AI systems beyond connecting APIs or LLMs is complicated, but not impossible.

This architecture lays the foundation for how AI agents think, communicate, and improve, covering everything from testing and observability to deployment and memory management.

Here’s a breakdown of the key layers and components that make up a scalable Agentic AI Architecture :

1.🔸Decomposition
Break down complex systems by domain (e.g., Coding Agent, Data Agent), by cognitive capability (Reasoning, Planning, Execution), or by agent role (Planner, Executor, Memory Manager, Communicator).

2.🔸Communication
Enable message passing between agents using inter-agent protocols or A2A (Agent-to-Agent) orchestration.
Support both single-agent and multi-agent setups for small or distributed workflows.

3.🔸Deployment
Deploy agents in containerized or serverless environments using Docker or Modal.
Support orchestrators like CrewAI or AutoGen for collective intelligence in multi-agent workflows.

4.🔸Data & Discovery
Integrate knowledge bases (like vector databases for RAG), memory stores (FAISS, Redis, Pinecone), and APIs for dynamic data access.
Context is passed using Model Context Protocol (MCP) for structured and real-time reasoning.

5.🔸Testing & Observability
Validate workflows end-to-end, test reasoning logic, and evaluate performance under real conditions.
Monitor using Weights & Biases, LangFuse, and track metrics like latency and task success rate.

6.🔸UI & Style
Provide intuitive feedback loops through visualization layers, dashboards, and self-reflective modes.
Enable collaborative, proactive, and goal-driven reasoning among multiple agents.

7.🔸Security
Protect access with token-based authorization and data encryption.
Include Trust Layers for human-in-the-loop validation and Policy Enforcement for safe execution.

8.🔸Cross-Cutting Concerns
Handle configuration, secrets, and environment management.
Support flexible frameworks like LangChain, AutoGen, or CrewAI for runtime execution and modular design.

Agentic AI is the future of automation - where AI doesn’t just assist but collaborates and learns.

Save this post to understand the architecture that powers the next generation of AI systems

#AgenticAI

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AI agents are great at working alone. However, they can also be effective when operating within networks of collaboration.

The real power of AI lies in how these agents communicate, coordinate, and complement each other’s abilities.

Orchestration patterns define this collaboration which turns isolated agents into intelligent, goal-driven systems capable of solving complex tasks together.

Here are 6 orchestration patterns that power the next generation of AI systems :

1.🔸Sequential Orchestration
Agents work one after another - each passing its result to the next.
Perfect for structured, step-by-step tasks like data pipelines or analysis chains.

2.🔸Concurrent Orchestration
Multiple agents work in parallel, handling different parts of a problem simultaneously.
This boosts speed, scalability, and overall task efficiency.

3.🔸Group Chat Orchestration
Agents communicate in a shared conversation, refining ideas together.
Useful for brainstorming, collaborative decision-making, or generating consensus.

4.🔸Maker–Checker Loops
One agent creates or proposes; another verifies, corrects, or approves.
Ideal for maintaining accuracy, compliance, and quality assurance in workflows.

5.🔸Handoff Orchestration
Agents transfer control dynamically based on context or expertise.
Best suited for adaptive workflows like support systems or multi-step automation.

6.🔸Magnet Orchestration
A central “manager” agent dynamically assigns tasks to the right specialists.
Enables fluid, context-aware collaboration across multiple intelligent agents.

AI Agents are evolving fast and orchestration is what makes them powerful.

Learning how these patterns can help you build smarter, more collaborative AI systems can be a huge advantage.

#AIAgent

AI Agent Orchestration Patterns

📚LLMs in the Enterprise are finally getting the playbook they deserve… LLMs in Enterprise by Ahmed Menshawy and Mahmoud Fahmy provides practical guides for building real AI systems that operate at scale.

Most teams talk about LLMs in theory. This book focuses on execution. It bridges foundational concepts with the hands-on design patterns that matter when you are integrating models into production environments.

Here are the insights that stood out👇

1.🔸Enterprise LLM integration is a data architecture problem
The book breaks down how to design pipelines, tune retrieval, and structure data so models operate with consistency and low latency in real workloads.

2.🔸Scaling LLMs requires pattern-level thinking
They go deep on architectural patterns that reduce complexity, improve efficiency, and streamline deployment. This includes RAG frameworks, fine-tuning strategies, segmentation techniques, and evaluation patterns that teams often overlook.

3.🔸Performance is not just about bigger models
The authors show how to optimize model behavior with advanced inferencing engines, contextual model customization, and monitoring systems that keep applications predictable.

4.🔸Real enterprise value comes from operational rigor
Security, fairness, transparency, and accountability are not afterthoughts. They are part of the design process, especially when LLMs touch business workflows and customer data.

5.🔸AI teams win by mastering both concepts and impact
The flow of the book reflects the real enterprise lifecycle: Concept → Customization → Impact
A clear, structured way to think about turning LLM capabilities into business outcomes.

If you are building production AI systems, leading an LLM program, or preparing for the next wave of enterprise adoption, you should definitely get a copy of this book.

Enterprise AI is evolving fast. Understanding these design patterns early puts at a career advantage which allows you to shape the next generation of intelligent applications.

#LLM

Check out this simple breakdown of the four core LLM architectures shaping most of today’s advanced systems.

Not all LLMs reason the same way. Building reliable AI systems, or choosing the right model for a workflow requires a clear mental model of the architectures that drive them.

Let’s review:

1.🔸Decoder Only (GPT, LLaMA, Grok)
Generates text by predicting one token at a time using previous context.

Best for generative workloads, conversational systems, and multi step reasoning.

2.🔸Encoder Only (BERT, RoBERTa)
Processes text with full bidirectional context to learn dense semantic representations.

Best for classification tasks, retrieval, and embedding generation.

3.🔸Encoder Decoder (T5, FLAN T5, BART)
Separates understanding from generation. The encoder builds meaning, the decoder transforms it.

Best for translation, summarization, and any structured text to text transformation.

4.🔸Mixture of Experts (Mixtral, Gemini 1.5)
Routes each input to a subset of specialized expert networks.

Best for scaling model capacity while controlling compute cost.

These architectures go beyond academic details to determine latency profiles, context handling, fine tuning strategies, and how models behave under real workloads. A clear grasp of them helps you design better pipelines, choose the right foundation models, and understand why two LLMs can produce very different outcomes on the same task.

Let me know your thoughts!

#LLM

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