Hey friends — 👋

I’m Paul. I'm a cloud technology futurist with a reasonable caffeine addiction appreciation. 😅

Keep scrolling to learn a little bit about me, my favorite coffee shops, books and my specialities in SaaS, Data, AI & Machine Learning solutions for smarter, strategic and more productive outcomes.

Industry certifications

Google Professional Data Engineer 🔗

Google Professional Cloud Architect 🔗

AWS Solutions Architect - Associate 🔗

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

Specialities

Google Professional Data Engineer certified

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.

Products

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.

Make analytics easier by bringing together data from multiple sources in BigQuery, for seamless analysis. You can upload data files from local sources, Google Drive, or Cloud Storage buckets, take advantage of BigQuery Data Transfer Service (DTS), Data Fusion plug-ins, or leverage Google's industry-leading data integration partnerships. You have ultimate flexibility in how you bring data into your data warehouse.

In computing, a data pipeline is a type of application that processes data through a sequence of connected processing steps. As a general concept, data pipelines can be applied, for example, to data transfer between information systems, extract, transform, and load (ETL), data enrichment, and real-time data analysis. Typically, data pipelines are operated as a batch process that executes and processes data when run, or as a streaming process that executes continuously and processes data as it becomes available to the pipeline.

Google’s stream analytics makes data more organized, useful, and accessible from the instant it’s generated. Built on Pub/Sub along with Dataflow and BigQuery, our streaming solution provisions the resources you need to ingest, process, and analyze fluctuating volumes of real-time data for real-time business insights. This abstracted provisioning reduces complexity and makes stream analytics accessible to both data analysts and data engineers.

Pub/Sub is a messaging and ingestion for event-driven systems and streaming analytics. Scalable, in-order message delivery with pull and push modes, Auto-scaling and auto-provisioning with support from zero to hundreds of GB/second, Independent quota and billing for publishers and subscribers, Global message routing to simplify multi-region systems.1

At-least-once delivery

Synchronous, cross-zone message replication and per-message receipt tracking ensures at-least-once delivery at any scale.

Exactly-once processing

Dataflow supports reliable, expressive, exactly-once processing of Pub/Sub streams.

Google Cloud–native integrations

Take advantage of integrations with multiple services, such as Cloud Storage and Gmail update events and Cloud Functions for serverless event-driven computing.

Dataprep by Trifacta is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis, reporting, and machine learning. 1

Predictive transformation

Dataprep uses a proprietary inference algorithm to interpret the data transformation intent of a user’s data selection.

Active profiling

See and explore your data through interactive visual distributions of your data to assist in discovery, cleansing, and transformation.

Data pipeline orchestration

Schedule and automate your data preparation jobs by chaining them together in sequential and conditional order.

Dataproc is a fully managed and highly scalable service for running Apache Spark, Apache Flink, Presto, and 30+ open source tools and frameworks. Use Dataproc for data lake modernization, ETL, and secure data science, at planet scale, fully integrated with Google Cloud, at a fraction of the cost.1

Modernize your open source data processing

Whether you need VMs or Kubernetes, extra memory for Presto, or even GPUs, Dataproc can help accelerate your data and analytics processing by spinning up purpose-built environments on-demand.

Containerize Apache Spark jobs with Kubernetes

Build your Apache Spark jobs using Dataproc on Kubernetes so you can use Dataproc with Google Kubernetes Engine (GKE) to provide job portability and isolation.

Autoscalling, resizable clusters.

Dataproc autoscaling & resizing provides a mechanism for automating cluster resource management and enables automatic addition and subtraction of cluster workers (nodes) & also create and scale clusters quickly with various virtual machine types, disk sizes, number of nodes, and networking options.

Dataflow is a Unified stream and batch data processing that's serverless, fast, and cost-effective. 1

Vertical & horizontal autoscaling

Horizontal autoscaling lets the Dataflow service automatically choose the appropriate number of worker instances required to run your job.

Streaming Engine

Streaming Engine separates compute from state storage and moves parts of pipeline execution out of the worker VMs and into the Dataflow service back end, significantly improving autoscaling and data latency.

Batch Flexible scheduling

For processing with flexibility in job scheduling time, such as overnight jobs, flexible resource scheduling (FlexRS) offers a lower price for batch processing.

Serverless, highly scalable, and cost-effective multicloud data warehouse designed for business agility.1

Serverless

With serverless data warehousing, Google does all resource provisioning behind the scenes, so you can focus on data and analysis rather than worrying about upgrading, securing, or managing the infrastructure.

Multicloud capabilities

BigQuery Omni (Preview) allows you to analyze data across clouds using standard SQL and without leaving BigQuery’s familiar interface.

Built-in ML and AI integrations

Besides bringing ML to your data with BigQuery ML, integrations with Vertex AI and TensorFlow enable you to train and execute powerful models on structured data in minutes, with just SQL.

Looker is an enterprise platform for business intelligence, data applications, and embedded analytics.1

Integrated end-to-end multicloud platform.

Connect, analyze, and visualize data across Google Cloud, Azure, AWS, on-premises databases, or ISV SaaS applications with equal ease at high scale, with the reliability and trust of Google Cloud.

Common data model over all your data.

Operationalize BI for everyone with powerful data modeling that abstracts the underlying data complexity at any scale and helps to create a common data model for the entire organization.

Augmented analytics

Augment business intelligence from Looker with leading-edge artificial intelligence, machine learning, and advanced analytics capabilities built into Google Cloud Platform.

What exactly is Data Engineering?

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

How is Data Engineering useful?

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.

What are the key phases/stages of a Data Engineering data cycle?

The Data Engineering data cycle involves:

  1. Data Ingestion/Capture (PubSub)
  2. Data Processing (Dataprep, Dataproc, Dataflow)
  3. Data Storage (Cloud Storage, BigQuery Storage)
  4. Data Analysis (BigQuery, BigQuery ML)
  5. Data Visualization (Looker, Data Studio, AI Platform Notebooks)

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 engineering 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.

Machine Learning Engineer certified

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.

Products

Cloud Storage, BigQuery ML, AI Hub, Data Labeling, Deep Learning VM, AI Platform, TensorFlow Tool, AI Prediction Platform

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.

Delivering high-performing recommendations across channels

Detect faces, their position and expressions with deep analysis

Implement ML models, without writing code

Proatively detect weapons, violence and inappropriate content.

Driving increased customer engagement

Detect raw and partial nudity and filter uploads on images & videos

How it works

Vehicles can produce upwards of 560 GB data per vehicle, per day. This deluge of data represents opportunities to derive value from the continuous stream of data and challenges in processing and analyzing data at this scale. 1

Device Management

To connect devices to any platform, you must be able to authenticate, authorize, push updates, configure, and monitor software. These services must scale to millions of devices and provide persistent availability.

Predictive models

You must have predictive models based on current and historical data in order to predict business-level outcomes.

Data analytics

You can perform complex analysis of time-series data generated from devices to gain insights into events, tolerances, trends, and possible failures

How it works 1

Drive innovation and growth in your organization by unlocking valuable insights from your data 1

Data-driven healthcare future

The sheer volume of data in your organization presents an opportunity to provide deeper insights, distribute them to the right people, and then make better real-time decisions – all with the highest levels of security, compliance, and respect for user privacy

Data interoperability

Interoperability is key to achieving the transformational goals in healthcare and life sciences for everything from telemedicine to virtual trials to app-based healthcare ecosystems.

Near real-time decisions

Once your data is harmonized, unlock its value with analytics and AI. Make smart, near real-time decisions that can help you reach your business goals and improve health outcomes of patients and communities

How it works 1

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Face detection

Detect faces, their position and expressions with deep analysis

Nudity detection

Detect raw and partial nudity and filter uploads on images & videos

Weapons, alcohol, & drugs

Proatively detect weapons, violence and inappropriate content.

Celebrities

Detect and recognize celebrities on images & videos.

Search

Block scammers and prevent fake profile pic uploads.

Minors detection

Detect babies, children, and teenagers under 18 to enhance safety.

Sentiment analysis

Understand the overall opinion, feeling, or attitude sentiment expressed in a block of text.

Syntax analysis

Extract tokens and sentences, identify parts of speech and create dependency parse trees for each sentence.

Entity analysis

Identify entities within documents and label them.

Content classification

Classify documents in 700+ predefined categories.

Multi-language

Enables you to easily analyze text in multiple languages.

Spatial structure understanding

Use the structure and layout information in PDFs to improve custom entity extraction performance.

Cloud Speech-To-Text

Accurately convert speech into text using an API powered by Google’s AI technologies.

Cloud Text-To-Speech

Convert text into natural-sounding speech using an API powered by Google’s AI technologies.

Video Caption & audio generation

Generate captions or translated audio for your video content automatically in any supported language

Automatic text translation

Real-time text translations for cross-language communication

Multi-language chat

Chat with users in different languages with automatic translation

Real-time audio translation

Easy captions and language translation for your audio content.

Face detection

Detect faces, their position and expressions with deep analysis

Nudity detection

Detect raw and partial nudity and filter uploads on images & videos

Weapons, alcohol, & drugs

Proatively detect weapons, violence and inappropriate content.

Celebrities

Detect and recognize celebrities on images & videos.

Search

Block scammers and prevent fake profile pic uploads.

Minors detection

Detect babies, children, and teenagers under 18 to enhance safety.

Data discovery and classification

Harness gives you the power to scan, discover, classify, and report on data from virtually anywhere.

Automatically mask your data to safely unlock more of the cloud

Built in tools to classify, mask, tokenize, and transform sensitive elements to help you better manage the data that you collect, store, or use for business or analytics.

Simple and powerful redaction

De-identify your data: redact, mask, tokenize, and transform text and images to help ensure data privacy.

Secure data handling

The Data Loss API runs on Google Cloud, your data securely and undergoes several independent third-party audits to test for data safety, privacy, and security.

Custom rules

Add your own custom types, adjust detection thresholds, and create detection rules to fit your needs and reduce noise.

Advanced AI

Improve your call/chat containment rate with the latest BERT-based natural language understanding (NLU) models.

State-based data models

Reuse intents, intuitively define transitions and data conditions, and handle supplemental question.

End-to-end management

Take care of all your agent management needs including CI/CD, analytics, experiments, and bot evaluation inside Dialogflow

Visual flow builder

Reduce development time with interactive flow visualizations that allow builders to quickly see, understand, edit, and share their work.

Omnichannel implementation

Build once, deploy everywhere—in your contact centers and digital channels. Seamlessly integrate your agents across platforms

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 Engineer certified

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.

Products

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.

Identification is the foundation of machine learning use cases. It tends to be most helpful in workflow routing, auto-tagging, and trend analysis use cases: knowing what something is is the first step in deciding what to do with it. credit

Examples

  • I took a picture. Machine learning tells me it’s a dog.
  • I received an email. Machine learning tells me it’s about dog food.
  • I am on a website. Machine learning tells me it has a shopping plug-in.
  • I am looking at a bank statement. Machine learning tells me there is a grocery transaction.

This involves combining multiple classifications and making them relate to each other. credit

Examples

  • I have 1,000 pictures. Separate the dog and cat photos.
  • I have 10,000 emails. Group them into hierarchies: which ones are about pets, and of those, which ones are about dog food vs cat food.
  • I have 100,000 websites. Categorize them by brand tone.
  • I have 1M bank statements. Label each transaction as Retail, Services, or Other.

Now, we start to add contextual clues. Clues specific to that specific company or time or geography. We’re not just labeling one aspect of one feature; we’re generating a sense of urgency, ranking, and prioritization. credit

Examples

  • I have 1,000 pictures. Tell me which ones have a sick dog.
  • I have 10,000 emails. Tell me which ones are high priority.
  • I have 100,000 websites. Tell me which ones have illegal terms and conditions for their respective countries.
  • I have 1B banking transactions. Tell me which ones are fraudulent.

At Level 4, we begin to incorporate AI/ML outputs into business workflows. The system has returned some sort of output (“this bank transaction is 91.7% likely to be fraudulent”), but deciding what to do with that output is where it gets really valuable (“cover up to $10,000 in fraudulent charges”). credit

Examples

  • I think I have a picture of sick dog. Now what?
  • I think I have an important email. Now what?
  • I think I have an illegal website. Now what?
  • I think I have a fraudulent bank transaction. Now what?

Prediction is the golden ticket of artificial intelligence. The holy grail. And I sometimes to refer to this as the “last column” problem. In each case, you want to analyze all of the factors (e.g., age, farm size, satellite images, customer emails) to best predict the last column of your spreadsheet.credit

Examples

  • Will my vet clinic see more sick dogs next month?
  • Which customers are most likely to purchase a couch in the next 60 days?
  • Can I predict a fraudulent bank transaction? How should I best notify the user/freeze the card?
  • Is a power outage likely to happen in San Francisco this month? How can we mitigate that?

BigQuery ML lets you create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by letting SQL practitioners build models using existing SQL tools and skills. BigQuery ML increases development speed by eliminating the need to move data.

A model in BigQuery ML represents what an ML system has learned from the training data. BigQuery ML supports the following types of models: 1

Linear regression

for forecasting; for example, the sales of an item on a given day. Labels are real-valued (they cannot be +/- infinity or NaN).

Binary logistic regression

for classification; for example, determining whether a customer will make a purchase. Labels must only have two possible values.

Multiclass logistic regression

for classification. These models can be used to predict multiple possible values such as whether an input is "low-value," "medium-value," or "high-value." Labels can have up to 50 unique values.

TensorFlow model importing.

This feature lets you create BigQuery ML models from previously trained TensorFlow models, then perform prediction in BigQuery ML.

Autoencoder

for creating Tensorflow-based BigQuery ML models with the support of sparse data representations.

K-means clustering

K-means is an unsupervised learning technique, so model training does not require labels nor split data for training or evaluation.

Multiclass logistic regression

These models can be used to predict multiple possible values such as whether an input is "low-value," "medium-value," or "high-value."

Matrix Factorization

You can create product recommendations using historical customer behavior, transactions, and product ratings and then use those recommendations for personalized customer experiences.

Time series

You can use this feature to create millions of time series models and use them for forecasting.

TensorFlow model importing.

This feature lets you create BigQuery ML models from previously trained TensorFlow models, then perform prediction in BigQuery ML.

Boosted Tree

for creating XGBoost based classification and regression models.

Deep Neural Network (DNN)

for creating TensorFlow-based Deep Neural Networks for classification and regression models.

AutoML Tables

to create best-in-class models without feature engineering or model selection.

AutoML enables developers with limited machine learning expertise to train high-quality models specific to their business needs. Build your own custom machine learning model in minutes.

Vertex AI

Unified platform to help you build, deploy and scale more AI models. Prepare and store your datasets Access the ML tools that power Google. Experiment and deploy more models faster. Manage your models with confidence

AutoML Vision

Derive insights from object detection and image classification, in the cloud or at the edge. Use REST and RPC APIs, Detect objects, where they are, and how many. Classify images using custom labels. Deploy ML models at the edge

AutoML Video Intelligence

Enable powerful content discovery and engaging video experiences. Annotate video using custom labels. Streaming video analysis. Shot change detection. Object detection and tracking

AutoML Natural Language

Reveal the structure and meaning of text through machine learning. Integrated REST API. Custom entity extraction. Custom sentiment analysis. Large dataset support

AutoML Translation

Dynamically detect and translate between languages. Integrated REST and gRPC APIs, Supports 50 language pairs. Translate with custom models.

AutoML Tables (beta)

Automatically build and deploy state-of-the-art machine learning models on structured data. Handles wide range of tabular data primitives. Easy to build models. Easy to deploy and scale models

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.

Why 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.

Products

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.
Google Professional Cloud Architect certified AWS Solutions Architect - Associate certified

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 🚀

“75% of business leaders cite improved business agility and 74% cite speed of implementation and deployment as the benefits that factored into their firm’s decision to move to pure SaaS.”

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



Impact to your business

Businesses today use an average of 80 IT-sanctioned SaaS apps, a 5x increase in just three years and a 10x increase since 2015.

Data first

80% of workers admit to using SaaS applications at work without getting approval from IT.

SaaS with Laravel Web Framework.

Bringing a love of art and creativity, i've built everything from social networks to productivity apps! For example, RuggedCaffeine is a social network for people who love coffee! How cool is that? RuggdProjects is an agile-based project management tool made to make collaborations easy and fun with additional social features included!

Authentication

Authentication, registration, and password reset are ready out of the box.

Emails

Transactional and service emails out of the box and ready for your design inputs.

Invite System

Organize into teams and collaborate with others to build the next big thing.

Encrypted passwords

System wide data encrpytion starting with your user's password.

2-factor Sign In

Extra extra layer of authentication security with email and text confirmation codes.

Registration confirmation

Require your users to confirm their email address before using the site

Recurring subscription

Super simple subscription billing without the hassle with monthly and yearly plans

Braintree Integration

Your users payment information never touches our servers! All the validation is handled by Braintree Payments, a trusted payments service processor.

Invoices

Allow your customers to download PDF copies of their invoices.

Trial periods

Trial period settings from limited to unlimited days for your special users

Single charges

Enable a "one-time" charge to your subscribed users with an accompanying invoice.

Team billing

Enable teams to have their own billing and manage each team member's subscription easily.

Secure site

Every application receives a free, auto-renewing SSL certificate during deployment.

Data Encryption

Triple layer encryption starting the Cloud infrastructure, application and data layer

Login throttle

Prevent brut force logins with automatic throttling and cooling period before retries.

Automatic backup

Scheduled backups of application and database that run like clock-work.

Security emails

Immediately your users of important security changes via email with reversal options.

Post

Create posts, share content, comment, reply and upload your videos and photos.

Forums

Join the discussion to ask questions, share insights, and discuss topics.

Follows

Super simple follows and connections for users ready out of the box

Notifications

You'll fall in love with the sleek, notification bubbles that pop and disappear like magic

Meetups

Turn your online friends into real-life buddies with super simple meetups

Chat & conversations

Start group chats, invite new participants and message in a really fun way.

Impersonation

Need to login as one of your user? Impersonate allows you to authenticate as any of your customers.

Metrics

RuggedSaaS provides beautiful charts showing you the throughput and peformance of your application.

User management

Manage users, register new ones, analyze logins, review reports and handle blocked users.

Site configurations

Manage everything from enabling/disabling registration to turning key features on and off.

Announcements

Easily create announncements with disappearing timers so that your users so they are always in the loop.

Application Events

Gain x-ray views logins, uploads, registrations, system updates, changes, and other events

Face detection

Detect faces, their position and expressions with deep analysis

Nudity detection

Detect raw and partial nudity and filter uploads on images & videos

Weapons, alcohol, & drugs

Proatively detect weapons, violence and inappropriate content.

Celebrities

Detect and recognize celebrities on images & videos.

Search

Block scammers and prevent fake profile pic uploads.

Minors detection

Detect babies, children, and teenagers under 18 to enhance safety.

Backups

Scheduled backups of application and database that run like clock-work

Cronjobs

Easily start and manage supervised Laravel Queue workers directly

Storage

Easily stream your file uploads directly to S3 like magic.

Queues

Increased application performance with queues abstraction and processing.

Error logging

Crystal clear insights and details of every error generated in your envrionments.

Monitoring

We monitor a variety of metrics about your applications, databases, and caches.

Do you really have a SaaS template with all these features?

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 😊.

A few reads
on Medium

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.

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Leveraging AI as a Service for Image Filters with Laravel

My wild journey through 50 books and some recommendations.

Deploying fast, serverless sites with AWS S3 and bitbucket.

Coffee shop
recommendations

Can't start a great day without a good cup of coffee.
Here are a few spots where I get my caffeine fix.

Xander Coffee

BUCKHEAD

Copper Coin Coffee

WOODSTOCK

Daily Dose Coffee

MIDTOWN

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