Blog
Ideas and opinions about data, AI, and applied technology.
How Much Does a Data Project with a Consultancy Cost? Variables, Ranges, and What Nobody Tells You
A complete guide to understanding the cost of a data project in a production environment and the variables that determine where the investment actually goes.
The difference between a PoC and an MVP (and why confusing them costs you)
Most AI projects fail because there's no clear conversation about whether you need to prove something can be done or that it actually works in production. Here's how to avoid that mistake.
Why most AI projects go nowhere
Most AI projects don't fail technically. They fail to ship. Here are four mistakes we've seen over and over, and what the projects that actually work have in common.
DBT in Action: Build the Marts Layer That Connects Your Data to the Business
Marts are the layer where your DBT models become truly useful: curated datasets, ready for analysis and business decisions.
How to use custom containers in SageMaker for inference with LightGBM (step-by-step guide)
Complete guide to running batch inference with LightGBM on AWS SageMaker: learn how to deploy massive, reproducible, and scalable processing using custom containers and predictive AI.
Vibe coding with AI: 10 best practices for coding fast (without losing control)
Vibe coding isn't about delegating everything to AI: it's about AI-assisted programming with good judgment. In this guide we share 10 practices for documenting in natural language, generating tests, applying incremental changes, modularizing, choosing tools, and versioning without drama.
Models, Sources, and Seeds in dbt: A Complete Tutorial for Transforming Data with Reproducible SQL
Master dbt with this hands-on guide: learn how to create models, declare sources, and manage seeds using Docker and Postgres. A step-by-step tutorial to accelerate your modern data stack and achieve reproducible, reliable data transformations.
Oil & Gas 4.0: key challenges the industry faces when adopting AI
Digital transformation in oil and gas promises gains in efficiency, safety, and cost, but corporate culture can stall progress. Resistance to change, digital talent gaps, and rigid hierarchies cause 70% of projects to stall.
How to Add Local Generative AI to Grafana and Turn Charts into Decisions
Integrate a local language model (LLM) into Grafana and turn your dashboards into intelligent decision-making tools. In this step-by-step guide, we show you how to visualize, analyze, and interpret metrics in real time without exposing data to external services.
AI and Human Resources: 6 Ways to Automate and Supercharge Your Company
Generative artificial intelligence (GenAI) is reshaping the HR space. This article explores how applied AI can optimize six key processes: talent acquisition, employee relations, training, performance management, administration, and compliance.
Coding with type hints: a simple practice that optimizes your business
Learn how using type hints in Python and tools like MyPy can help you write better code, prevent bugs, and improve your development team's efficiency. Simple best practices with real impact on your business.
The uncomfortable truth about AI: what if it's not for your business?
Many companies feel they need to adopt artificial intelligence, but it's not always the right call. In this post we help you figure out if your business is actually ready to benefit from AI, how to avoid misguided investments, and why a solid data strategy is still the real differentiator.
Modernize Your Data Stack with DBT: Less Tech Debt, Faster Decisions
DBT optimizes your data processes to generate faster insights, reduce technical debt, and improve the return on investment in analytics.
Data Science and Oil: A Unique Opportunity for Argentina
Argentina's oil & gas sector presents a unique strategic opportunity. Learn how to implement data science solutions to drive efficiency and profitability in this growing industry.
Machine Learning Discovery: 4 Weeks to Decide How to Apply ML in Your Business
At Deployr we designed a 4-week Discovery process to validate whether machine learning is the right solution for your business problem. Here's how it works and why it's critical before investing in an ML project.
An introduction to Data Quality tools in Python
Complete guide to Great Expectations and PyDeequ: Python-based data quality tools for building robust and reliable ETL pipelines.
Go serverless by building an automated Twitter scraper on AWS in five simple steps
Step-by-step tutorial to build a serverless scraper with Lambda, EventBridge, and S3: learn how to automate data extraction without managing servers.
WSL: How to install Linux inside Windows (the best of both worlds)
The Windows Subsystem for Linux (WSL) lets developers use a GNU/Linux environment directly on Windows. It's a simple and efficient alternative to dual booting.
Roles, Permissions, Identities, and Groups: How to Use AWS Securely Without Fear of Mistakes
A complete guide to understanding and configuring access control in AWS: learn how to create users, groups, roles, and security policies in a clean, scalable way.
How to Apply Feature Selection in Your Data Science Project
Learn everything about feature selection, a feature engineering technique used to select the most relevant characteristics for a machine learning model.
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