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Code, Build, Deploy: Nx Monorepo, Docker, and Kubernetes in Action Locally

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Veritas Automata Victor Redondo

Victor Redondo

Whether you’re just starting out or looking to enhance your current practices, this thought leadership is designed to empower you with the knowledge of integrating Nx Monorepo, Docker, and Kubernetes.
As developers, we often confine our coding to local environments, testing in a development server mode. However, understanding and implementing a local Docker + Kubernetes deployment process can significantly bridge the gap between development and production environments. Let’s dive into how these tools can transform your local development experience.
Before I dive into the technicalities, let’s familiarize ourselves with Nx Monorepo. Nx is a powerful tool that simplifies working with monorepos – repositories containing multiple projects. Unlike traditional setups, where each project resides in its own repository, Nx allows you to manage several related projects within a single repository. This setup is not only efficient but also enhances consistency across different applications.

What are the Key Benefits of Nx Monorepo? In a nutshell, Nx helps to: speed up your computation (e.g. builds, tests), locally and on CI, and to integrate and automate your tooling via its plugins.

Common functionalities can be shared across projects, reducing redundancy and improving maintainability.
Nx provides a suite of development tools that work across all projects in the monorepo, streamlining the development process.
Teams can work on different projects within the same repository, fostering better collaboration and integration.
The next step in your journey is understanding Docker. Docker is a platform that allows you to create, deploy, and run applications in containers. These containers package up the application with all the parts it needs, such as libraries and other dependencies, ensuring that the application runs consistently in any environment.

Why Docker?

Consistency: Docker containers ensure that your application works the same way in every environment.

Isolation: Each container runs independently, eliminating the “it works on my machine” problem.

Efficiency: Containers are lightweight and use resources more efficiently than traditional virtual machines.

Kubernetes: Orchestrating Containers. Interested in understanding Veritas Automata’s differentiator? Read more here. (Hint: We create Kubernetes clusters at the edge on bare metal!)

Having our applications containerized with Docker, the next step is to manage these containers effectively. This is where Kubernetes comes in – – Kubernetes is an open-source platform for automating the deployment, scaling, and management of containerized applications.

Kubernetes in a Local Development Setting:

Orchestration: Kubernetes helps in efficiently managing and scaling multiple containers.

Load Balancing: It automatically distributes container workloads, ensuring optimal resource utilization.

Self-healing: Kubernetes can restart failed containers, replace them, and even reschedule them when nodes die.

Integrating Nx Monorepo with Docker and Kubernetes

Step 1: Setting Up Nx Monorepo

Initialize a new Nx workspace.
Create and build your application within this workspace.

Step 2: Dockerizing Your Applications

Create Dockerfiles for each application in the monorepo.
Build Docker images for these applications

Step 3: Kubernetes Deployment

Define Kubernetes deployment manifests your applications.
Use Minikube to run Kubernetes locally.
Deploy your applications to the local Kubernetes cluster.

I have the honor of presenting this topic at a CNCF Kubernetes Community Day in Costa Rica. Kubernetes Day Costa Rica 2024, also known as Kubernetes Community Day (KCD) Costa Rica, is a community-driven event focused on Kubernetes and cloud-native technologies. This event brought together enthusiasts, developers, students, and experts to share knowledge, experiences, and best practices related to Kubernetes, its ecosystem, and its evolving technology.

By integrating Nx Monorepo with Docker and Kubernetes, you create a robust and efficient local development environment. This setup not only mirrors production-like conditions but also streamlines the development process, enhancing productivity and reliability. Embrace these tools and watch your workflow transform!

Remember, the key to mastering these tools is practice and experimentation. Don’t be afraid to dive in and try out different configurations and setups. Happy coding!

Want to discuss further? Add me on Linkedin!

Harnessing AI/ML for Enhanced Document Tagging and Internal Company Searchability

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In today’s fast-paced business world, organizations generate vast amounts of documents, ranging from reports and manuals to contracts and emails. Efficiently managing this deluge of information is essential for maintaining productivity and fostering informed decision-making.

One way to address this challenge is by leveraging Artificial Intelligence (AI) and Machine Learning (ML) models to automatically tag and categorize documents, making them more accessible and searchable within the company’s internal systems. In this blog, we will explore how to build an AI/ML model for document tagging and discuss the benefits it brings to internal searchability.

The Challenge of Document Management

Before diving into the technical aspects of building an AI/ML model for document tagging, let’s understand the challenges organizations face when it comes to document management:

Volume: Businesses accumulate a substantial volume of documents over time, making it challenging to keep track of, organize, and retrieve them efficiently.

Diversity: Documents vary in format, content, and purpose. They can include text, images, PDFs, spreadsheets, and more, each requiring distinct approaches to categorization.

Human Error: Manual tagging and categorization are prone to human error, leading to inconsistent labels and misclassification of documents.

Time-Consuming: Traditional methods of document management require significant time and effort, diverting resources from more valuable tasks.

AI/ML for Document Tagging: A Solution

Implementing AI/ML models for document tagging can address these challenges effectively. Here’s a step-by-step guide to building such a system:

To train an AI/ML model, you need a labeled dataset of documents. Collect a diverse set of documents that represent the types of content your organization deals with. These documents should be labeled with appropriate tags or categories.
Prepare the data for model training by performing the following preprocessing steps:

Text extraction: Extract text from documents, converting images and PDFs into machine-readable text.

Text cleaning: Remove unnecessary characters, punctuation, and formatting.

Tokenization: Split text into individual words or tokens.

Stopword removal: Eliminate common words like “and,” “the,” or “in” that don’t carry significant meaning.

Choose a suitable machine learning algorithm for document tagging. Common choices include:

Text Classification: Use algorithms like Naïve Bayes, Support Vector Machines (SVM), or deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

Natural Language Processing (NLP): Utilize pre-trained models like BERT (Bidirectional Encoder Representations from Transformers) or GPT (Generative Pretrained Transformer) for advanced document understanding.

Create meaningful features from the preprocessed text data. You can use techniques like TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings to represent words and phrases in a numerical format that the model can understand.

Train the selected ML model using the labeled dataset. The model will learn to associate specific words or phrases with relevant tags or categories.
Assess the model’s performance using metrics like accuracy, precision, recall, and F1-score. Make adjustments to the model or data preprocessing as needed to improve performance.
Once the model performs satisfactorily, deploy it to your internal document management system. This can be an integrated solution or a standalone application that processes and tags documents as they are uploaded or created.
Implement mechanisms for continuous learning. The model should adapt to changes in document types and tags over time. Periodically retrain the model with new data to keep it up-to-date.

Benefits of AI/ML Document Tagging

Implementing an AI/ML model for document tagging offers numerous advantages for enhancing internal searchability:
Automated tagging significantly reduces the time and effort required to organize documents, allowing employees to focus on more valuable tasks.
AI/ML models provide consistent tagging, reducing the risk of human errors and ensuring uniform categorization.
Tagged documents become highly searchable, allowing employees to find the information they need quickly and easily.
AI/ML models can personalize document recommendations based on an individual’s search history and preferences.
The system can handle a growing volume of documents, ensuring scalability as your organization expands.
Automated tagging reduces the need for manual document management, resulting in cost savings over time.

Access to well-organized and tagged documents empowers better-informed decision-making across the organization.

Veritas Automata Bogota News

Real-World Application:
Veritas Automata's Document Tagging Solution

Veritas Automata, a leader in AI-driven solutions, offers an advanced Document Tagging Solution that combines the power of AI and ML to streamline document management within organizations. Our solution employs state-of-the-art NLP models for accurate tagging, ensuring documents are categorized appropriately and can be easily retrieved when needed.

With a focus on security and compliance, Veritas Automata’s Document Tagging Solution helps organizations optimize their document management processes while maintaining data privacy and security.

Conclusion

In the digital age, efficient document management is critical for organizations seeking to maximize productivity and decision-making. Leveraging AI/ML models for document tagging can revolutionize how businesses handle their documents, making them easily searchable and accessible.

By following the steps outlined in this blog and considering solutions like Veritas Automata’s Document Tagging Solution, organizations can streamline their document management processes and unlock the full potential of their valuable information assets. In doing so, they position themselves for enhanced competitiveness, agility, and success in today’s information-driven world.

Unlocking Innovation: The Power of Artificial Intelligence in Business Transformation

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In today’s rapidly evolving business landscape, innovation is the key to success. Companies that can harness the power of emerging technologies like Artificial Intelligence (AI) are at the forefront of change.

Among these pioneers stands Veritas Automata, a company that epitomizes “Truth in Automation.” Let’s explore how AI is driving innovation, efficiency, and growth in industries such as Life Sciences, Manufacturing, Supply Chain, and Transportation.

The 5 Benefits of Artificial Intelligence:

Veritas Automata is here to solve your toughest challenges, and AI does just that.

It empowers businesses to tackle complex problems with precision and speed, something that was unimaginable before.

Whether it’s optimizing supply chains or accelerating drug discovery, AI brings logical and intuitive solutions to the table.

In the quest for efficiency, AI is a game-changer.

With our expertise in using Rancher K3s Kubernetes and other cutting-edge technologies, Veritas Automata ensures that AI-driven solutions are designed to make your toughest tasks manageable.

This increased efficiency translates to reduced costs and improved profitability.

For ambitious leaders and executives, staying ahead of the competition is crucial.

Veritas Automata’s AI-powered solutions help businesses gain a competitive edge by providing real-time insights, predictive analytics, and automation capabilities that can transform the way they operate.

Trust, clarity, efficiency, and precision are encapsulated in our digital solutions.

AI plays a pivotal role in ensuring the accuracy and clarity of processes. It reduces errors, minimizes risks, and enhances decision-making, all while maintaining a clear digital chain of custody.

Veritas Automata’s mission is to innovate, improve, and inspire.

AI is a driving force behind innovation, enabling businesses to explore new possibilities, create disruptive products and services, and transform their industries.

With AI, the possibilities are limitless.

The 4 Advantages of AI

Automation

AI enables automation on a scale never seen before. With Veritas Automata’s expertise in smart contracts and blockchain, we create autonomous transaction processing platforms that streamline operations and reduce manual interventions.

Data-Driven Insights

AI processes vast amounts of data to provide actionable insights. For industries like Life Sciences, AI-driven analytics can accelerate drug discovery and clinical trials, leading to faster time-to-market and life-saving breakthroughs.

Personalization

AI helps businesses deliver personalized experiences to customers. In industries like Transportation, AI-powered recommendation engines can enhance passenger experiences and drive loyalty.

Continuous Improvement

AI-driven solutions continually adapt and improve over time. With GitOps-driven Continuous Delivery, Veritas Automata ensures that your AI systems evolve to meet changing business needs.

The 3 Uses of Artificial Intelligence

Veritas Automata leverages AI to predict future trends and outcomes, enabling businesses to make proactive decisions.

In Manufacturing, predictive maintenance powered by AI reduces downtime and increases productivity.

NLP is revolutionizing customer interactions.

AI-driven chatbots and virtual assistants enhance customer support and streamline communication across industries.

AI’s image and video analysis capabilities have numerous applications. 

In Supply Chain, AI can monitor and analyze video feeds to optimize inventory management and reduce losses.

Veritas Automata’s expertise in AI and automation is driving innovation, efficiency, and success in industries where trust, clarity, efficiency, and precision are paramount.

By leveraging AI, businesses can gain a competitive advantage, improve customer experiences, and navigate the ever-changing landscape of the modern workplace.

Embracing AI is not about world domination; it’s about innovating, improving, and inspiring.