Hi 👋 I'm Paul Kamau

I'm a multi-cloud certified architect currently working as a Technical Account Manager for Google Cloud with resposibilities focused on Cloud Strategy and Consultancy, implementation management, advocacy and thought leadership.


Google Professional Cloud Architect 🔗

Google Professional Data Engineer 🔗

AWS Solutions Architect - Associate 🔗

When something is important enough, you do it, even if the odds are not in your favor. -- Elon Musk.


I'm 31, a believer, was born and raised in Nairobi, 🇰🇪 and immigrated to the United States 🇺🇸 when is was 19, and became a citizen shortly after.

I began my journey in the technology field over 7 years ago, letting my curiosity run wild across the fields of Web programming, Application development and eventually Cloud technologies. I love this industry, the expectation of continuous learning, the patterns of thinking, and the reward it gives to those striving to achieve their goals.

I've consolidated my passion for technology by building Rugged I/O, a playground to freely try out different ideas and drag myself outside of my comfort zone. As a sole founder of the company, I do everything - from product ideation,coding, market research, design, implementation, to customer support and everything in between.

I enjoy paddle-boarding, Kayaking, Swimming, Coffee shop hangouts, Space, & becoming fluent in French.

Support my work ☕

Data Engineering enables data-driven decision making by collecting, transforming, and publishing data models.

A data engineer is able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability.

Data engineering techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.


Cloud Storage, Pub/Sub, Dataprep, Dataflow, DataProc, BigQuery, BQ BI Engine, Looker, Data Studio, AI Platform Notebooks

Data Engineering Solutions

“65% of businesses around the world run data analytics tools to improve their business strategies”

If your business doesn’t rely on data to solve problems, build revenue or track capital, then you will lose a lot

Impact to your business

Close to 82% of businesses rely on data visualization graphics to portray business related metrics.

Data first

If data scientists are life blood of today’s data driven enterprise then data engineers are the veins carrying clean blood for machine learning algorithms to be useful.

What is the value of Data Engineering?

There’s more data than ever before, and data is growing faster than ever before. 90% of the data that exists today has been created in the last two years.

Data is more valuable to companies, and across more business functions—sales, marketing, finance and others areas of the business are using data to be more innovative and more effective.

The technologies used for data are more complex. Most companies today create data in many systems and use a range of different technologies for their data, including relational databases, Hadoop and NoSQL.

Companies are finding more ways to benefit from data. They use data to understand the current state of the business, predict the future, model their customers, prevent threats and create new kinds of products. Data engineering is the linchpin in all these activities.

As data becomes more complex, this role will continue to grow in importance. And as the demands for data increase, data engineering will become even more critical.

Data is the oil of the 21st century

Data Engineering solutions can unlock previously untapped solutions for your business or ideas. Therefore, a knowledgable engineer is able to:

  1. Architecting distributed systems
  2. Creating reliable pipelines
  3. Combining data sources.
  4. Collaborating with data science teams and building the right solutions for them.

What exactly is Data Engineering?

Data analytics enables making conclusions while analyzing data from informational resources. Data analytics uses strategies via technological processes and algorithms that manipulate data for human utilization and understanding. Data analytics helps businesses optimize their capacities.

How is Data Engineering useful?

Businesses are using analytics to make more informed decisions and to plan ahead. It helps businesses to uncover opportunities which are visible only through an analytical lens. Analytics helps companies to decipher trends, patterns and relationships within data to explain, predict and react to a market phenomenon.

What is happening and what will happen? Why is it happening? What is the best strategy to address it?

Collecting large amounts of data about multiple business functions from internal and external sources is simple and easy using today’s advanced technologies.

The real challenge begins, when companies struggle to infer useful insights from this data to plan for the future. Using analytics businesses can improve their processes, increase profitability, reduce operating expenses and sustain the competitive edge for the longer run.

How much time and resources are required?

The resources and time required for a data analytics project is dependent on a number of factors. The key factors being the scope and scale of the project, readiness and availability of required data, understanding of the analysis tools, skills and knowledge of the analytical team and most importantly, acceptance and approval from the management team to carry on the analytics project.

Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind. At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.

Artificial Intelligence as a service, or AIaaS, is the on-demand delivery and use of AI and Deep learning capabilities towards an individual or business objective. AIaaS provides cognitive computing capabilities such as language detection, image analysis and detection, text recognition, Natural language processing, facial expression, sentiment analysis etc.


Pub/Sub, Dataflow, BigQuery, BQ BI Engine, Looker, Data Studio

AI and Smart Analytics Solutions

“40% of digital transformation initiatives use AI services”

Digital transformation has gripped the mentality of any business run today. AI services provide the output for that transformation.

Impact to your business

Nearly 77% companies around the world have adopted AI-powered service.

Think of pipleines & railway tracks.

If data scientists are life blood of today’s data driven enterprise then data engineers are the veins carrying clean blood for machine learning algorithms to be useful.

Vision AI

Here businesses can enjoy facial recognition, weapons, alcohol & drugs, search, nudity detection, celebrities, and minors turning images into insights

Natural Language AI

It allows businesses to automate communication without compromising on interactivity. Think of chatbots and smart personal assistants.

Speech AI

It is similar to analytic AI – it also scans huge amounts of data and searches for patterns and dependencies in it. But, instead of giving recommendations, functional AI takes actions.

Video Intelligence AI

These are powered with machine learning. It scans tons of data for dependencies and patterns to ultimately produce recommendations or provide a business with insights.

Data Loss Prevention AI

These are powered with machine learning. It scans tons of data for dependencies and patterns to ultimately produce recommendations or provide a business with insights.

Personal AI

These are powered with machine learning. It scans tons of data for dependencies and patterns to ultimately produce recommendations or provide a business with insights.

What exactly is Artificial Intellience?

Artificial Intelligence (AI) is the concept of having machines think like humans. AI services are the performing of tasks such as reasoning, planning, learning, and understanding a particular language to scale a business further. AI services are in high demand. The brain behind artificial intelligence is a technology called Machine Learning, which is designed to make our jobs easier and more productive.

How is Artificial Intelligence useful?

A lot of things have aligned to make this an exciting time for major advancements in AI. The processing power has improved at an amazing rate — there’s been a trillion-fold increase in performance over the last 60 years.

The cost of data processing has become more affordable. There’s more data that needs to be analysed because businesses are capturing more signals from customer interactions.

Your business can use AI services to improve consumer apps significantly — leading to further expectations in making life easier, spurring the need for greater AI technical knowhow and R&D. All this can only be achieved by hiring a robust AI service agency.

What are the challenges faced by AI service agencies?

Let’s assume AI is an iceberg. What you see as a user is just the tip — but beneath the surface lurks a massive support system of our Services data scientists and engineers, along with massive amounts of data, labor-intensive extraction, data preparation, and a huge technological infrastructure.

It takes a specialized team to access the correct data, prepare the data, build the correct models, and then integrate the predictions back into an end-user Services application.

Machine learning is an application of Artificial Intelligence (AI) that enables systems to automatically learn and improve from experience without human intervention through manual programming. Much like future-forward movies in the 90s, machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

In machine learning, computers learn through observations or data, such as examples, direct experience, or instruction. They use this information to extract patterns in data and make better decisions in the future based on the examples that are provided. The main aim is to allow computers to learn automatically without any human intervention or assistance and to adapt accordingly.


Cloud Storage, Dataprep, Dataflow, DataProc, BigQuery, AI Hub, Data Labeling, Deep Learning VM, AI Platform, TensorFlow Tool, AI Prediction Platform

Key Phases of Machine Learning Projects

“Netflix saved $1 billion this year as a result of its machine learning algorithm”

The Machine Learning algorithm used by Netflix allows it to recommend personalized TV shows and movies to subscribers.

AI is better at ML than humans.

“AutoML, Google’s AI that helps the company create other AIs for new projects, learned to replicate itself in October of 2017".

Think Algorithms

You can have machine learning without sophisticated algorithms, but not without good data.

What is deep learning and how is it different from machine learning?

Deep learning, also known as deep neural networks, are set algorithms inspired by working principles of the human brain where it learns to identify patterns in data for decision making.

Deep learning is a subfield of representation learning, which in fact, is a subfield of machine learning.

What are the different types of Machine learning algorithms?

Typically, there are 4 types of Machine Learning:

  1. Supervised algorithms: Set of algorithms to learn from labelled data, e.g. images labelled with whether a human face exists in an image or not.
  2. Non-supervised algorithms: Set of algorithms to learn from data without labels or classes, e.g. set of images given to group similar images.
  3. Semi-supervised algorithms: algorithms that fall somewhere between above and uses both labelled and non-labelled data.
  4. Reinforcement learning algorithms: Set of algorithms to learn best actions to take given a current scenario that maximizes overall reward.

Use Cases has Machine Learning or Cognitive Computing become such a hot topic?

The reason for the increasing interest is due to the significant increase in data that is now available, which makes machine learning more relevant, accurate and more effective for more businesses than ever before. Machine Learning automates decisions by analysing large and diverse datasets at lightning speeds, predicting what would lead to a positive outcome and making or taking the recommended action.

What are the potential benefits of creating machines that can be programmed to learn?

Machine Learning automates decisions in a continuous improvement cycle. And machines automate actions. The combination of both greatly reduces the need for human intervention for a process to be completed. When we remove the reliance on human intervention we tend to achieve faster response times, a reduction in cost and a reduction in human error or bias for any process – from the granting of a loan to the landing of a plane.

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. Data Science is the scientific analysis of unstructured data for making it meaningful for developing strategies. These frequently asked questions are made up of queries that I get asked often.


Pub/Sub, Dataflow, BigQuery, BQ BI Engine, Looker, Data Studio

“The total data volume accessible will reach 4 trillion gigabytes by 2021”

With this much data available to analyse, access and implement, there is a science needed to mould them into reports or strategies.

Make sense out of data

Nearly 80% of data available over the internet is unstructured.

Data first

“7x – the number of times your business can scale faster.”.

What exactly is Data Science?

Data Science has transformed itself to become a more dynamic field. Data Science has rendered Business Intelligence to incorporate a wide range of business operations.

With the massive increase in the volume of data, businesses need data scientists to analyze and derive meaningful insights from the data.

How is Data Science useful?

The meaningful insights of Data Science will help companies to analyze information at a large scale and gain necessary decision-making strategies.

The process of decision making involves the evaluation and assessment of various factors involved in it. Factors such as, understanding the context and nature of the problem that we are required to solve, exploring and quantifying the quality of the data, implementation of the right algorithm and tools for finding a solution to the problems, can make Data Science work for you.

Business Intelligence (BI) basically analyzes the previous data to find hindsight and insight to describe business trends.

BI enables you to take data from external and internal sources, prepare it, run queries on it and create dashboards to answer questions like quarterly revenue analysis or business problems.

How does this process look like?

  1. Data science can be used to explore historicals, identify trends, make comparisons to competition, promote low-risk data-driven action plans, and analyze the market.
  2. We ensure that data is easily available at the fingertips of every decision maker.
  3. We allow businesses to learn which solutions to take for the best possible outcome and be prescribed logical
  4. We let them assess best-case scenarios to improve performances. We help record performance metrics and analyse them over time.
  5. We let your company become smarter and more efficient at making decisions based on recurring trends.

Software as a Service (SaaS) are applications Web or mobile based systems that run in the cloud and customers can easily access and enjoy them without any product installations or update maintenance. Think of youTube, Netflix and other online services.

Leveraging Laravel Web Framework, I built RUGGED SAAS as an way to provide businesses a fast, managed services experience where we design, build, deploy and manage a product leveraging the same building blocks that our own products are built on for rapid development and a faster go to market on a subscription basis.

This is the perfect start for your next great idea 🚀


Pub/Sub, Dataflow, BigQuery, BQ BI Engine, Looker, Data Studio

“65% of businesses around the world run data analytics tools to improve their business strategies”

If your business doesn’t rely on data to solve problems, build revenue or track capital, then you will lose a lot

Impact to your business

Close to 82% of businesses rely on data visualization graphics to portray business related metrics.

Data first

7x – the number of times your business can scale faster.

Laravel + React-Native.

I've always been a passionate web app developer but also loved the idea of bringing that creativity to the mobile space. With all the web apps being mobile friendly, i decided to begin with a webView wrapper on React-Native to expose the apps on PlayStore with future intents to leverage Laravel APIs with React-Native intergrations.

Do you really do all these amazing things?

Yes we do! We're dedicated to helping you focus on your product brand, growth and customers while we do all the heavy lifting building. You'll never have to think about servers, databases, monitoring and other technical implementations.

Are all the features listed available and ready to go?

Yes. We will never charge you for the baseline features that are ready and available for you.

Does it take a long time to have an MVP?

The scope of your project and requirements will influence the timeline. Typically, we can get you an MVP in less than 7 business days. Get ready to impress your prospects.

How much does it cost to build out the application?

Our Pay-as-you go model is super simple and affordable. Check out our subscription packages for MVPs, mid-level and complete Web app solutions.

I have a alot more questions 😋

That's what we love to hear! Hit us up on the contacts below and feel free to ask anything else you'd like 😊.

Follow my Medium Articles

I write full technical work related to Cloud, App Development and Ops technology on Medium.

A business and technical intro to Terraform.

The lifecycle of growth and adoption of the Cloud usually follows a similar pattern for many businesses and IT architects or developers working on them. For example, it is universally understood that when establishing the design of applications, at the most basic level is a networking layer, for security and access to your systems, the compute layer for the processing and decisioning, the storage layer for managing your assets and the data layer.

Read more

Terraform: Creating multiple S3 buckets with a single resource

Leveraging AI as a Service for Image Filters with Laravel

Deploying fast, serverless sites with AWS S3 and bitbucket.

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