Get Fired Up: Google Wins and AI Adventures!

April 11th 2025 by Kee

New Game Releases

April 12, 2025 by Kee

APRIL 2025*

• The Last of Us: Part II Remastered (PC)
• Indian Jones and the Great Circle (PS5)
• Fatal Fury: City of the Wolves (PC)(PS5)(PS4)(XBox X | S)
• Clair Obscur: Expedition 33 (PC)(PS5)(XBox X | S)
• The Talos Principle: Reawakened (PC)(PS5)(XBox X | S)
• Lunar Remastered Collection (All Platforms)
• South of Midnight (PC)(XBox X | S)
• Promise Mascot Agency (All Platforms)

What I'm playing and obsessed with:

• Nothing... Minecraft is too tough and I'm to busy to play the OG Call of Duty's
• Youtube Playables when waiting for processing there is one about Parking and a Farm game I like

Rewards and Challenges:

NONE, the state of gaming has me not interested until I can play Death stranding 2

Level Up Your AI Game

April 13, 2025 by Kee

Top 10 GCP Essentials (From the Trenches!)

Hey AI developers! Let's get real for a sec. We're in the trenches, building the future, and we need the right tools to make that happen. After countless hours of experimentations and real-world deployment on Google Cloud Platform (GCP), I've compiled my absolute must haves for super charging your workflow. These aren't just features; they're game changers!

Seriously, if you're missing out. This is where orchestration meets automation. Build, deploy, and manage your ML workflows with ease. It's like having a conductor for your AI symphony!

GKE is where the magic happens. Scale your AI models like a pro, handle complex deployments, and keep everything running smoothly. Trust me, GKE is your AI deployment powerhouse.

Vertex AI Workbench is the ultimate development environment, Notebooks, collaboration, and seamless integration with other GCP services - it's all there. It's the hub that makes development feel smooth and efficient.

Reliable, scalable, and secure. Cloud Storage is where you store your precious data. It's the bedrock of any serious AI project.

Need to analyze massive datasets? BigQuery is your weapon of choice. It's fast, powerful, and makes data exploration a breeze.

For those quick AI tasks or microservices, Cloud Functions are your go to. Serverless, scalable, and super efficient. It's like having AI on demand!

When you need serious AI acceleration, TPUs deliver. They're designed for machine learning, and they'll supercharge your training times.

Models drift. This is a fact. Vertex AI Model Monitoring helps you keep a close eye on your deployments, ensuring they stay accurate and effective.

Security is non-negotiable. IAM lets you control access to your resources with precision. Keep your data and models safe.

Debugging, performance tracking, and real time insights - Cloud Logging and Monitoring give you the viability you need to keep your AI projects running smoothly.

My Takeaway:

These are the tools that have made a real difference in my AI development journey. They're powerful, efficient, and they just work. If you're looking to level up your AI game on GCP, these essentials are where you need to focus.

Keep innovating, keep building, and let's push the boundaries of AI together!

Infrastructure Magic : LLMs and Vertex AI

April 14, 2025 by Kee

Hey AI enthusiasts! Ever felt the thrill of building something truly groundbreaking? That's exactly how I feel diving into the world of Large Language Models (LLMs) on Google Cloud's Vertex AI. Its like wielding a superpower, but instead of capes, we're using gcloud and Terraform! Let's embark on this journey together, exploring the ins and outs of setting up the perfect AI infrastructure.

Infrastructure with Terraform

First things first, let's lay a solid foundation infrastructure as Code (IaC) is our best friend, and Terraform makes it a breeze. Here's a snippet to get us stared, creating a Vertex AI Workbench instance:

resource "google_vertex_ai_workbench_instance" "llm_workbench" {
  provider = google-beta
  name =  "llm-workbench-instance"
  location = "us-central"
  machine_type = "n1-standard-4"
  owner = "email@example.com"


  vm_image{
    image_family = "tf-latest-cpu"
  }

  labels = {
    env = "development"
  }
}

This code spins up a Vertex AI Workbench instance, perfect for experimenting with LLMs. Notice how were using google-beta to access the latest features!

Deploying an LLM

Now, lets get our hand dirty with gcloud. We'll deploy a pre-trained LLM using the Vertex AI API.

              
gcloud ai endpoints create llm-endpoint\
--region=us-central1

gcloud ai models deploy \
--model=text-bison@001 \
--endpoint=llm-endpoint \
--region=central1 \
--machine-type=n1-standard-4 \
--traffic-split=deployed-model=100

These commands create an endpoint and deploy the test-bison@001 model. It's amazing how quickly we go from zero to a fully deployed LLM!

Python Magic

Let's use the Vertex AI Python SDK to interact with our deployed model:

              
from google.cloud import aiplatform

aiplatform.init(project="my-project_id", location="us-central1")

endpoint = aiplatform.Endpoint("llm-endpoint")

response = endpoint.predict(
  instance[{"content": "Tell me a short story about a coding bird."}]
)

print(response.predictions[0]["content"])

BOOM! We're talking to an LLM. The possibilities are endless!

Keeping Things Robust

Building a robust AI system isn't just about deploying. It's about scaling and monitoring. Vertex AI makes this easy. We can use Terraform to configure autoscaling:

resource "google_vertex_ai_endpoint" "llm_endpoint" {
  provider = google-beta
  name =  "llm-endpoint"
  location = "us-central"

  deployed_models {
    model = "text-bison@001"
    traffic_split {
      "0" = 100
    }
    autoscaling_config {
      min_replica_count = 1
      max_replica_count = 5 
    }
  }
}

And for monitoring, Vertex AI provides build in tools to track performance and usage.

We are in the Future Now!

Working with LLMs on Vertex AI is like unlocking a new dimension of creativity and problem solving. It's more than just tech, it's about building tools that can change the world. If you're a seasoned AI engineer or jet staring, the power of Vertex AI is with reach.

What are you building with LLMs? Share your projects and experiences! Let's learn and grow together.

Kubernetes Kung Fu

April 15, 2025 by Kee

Containerizing AI Apps and Mastering GKE!

Hey Cloud Ninjas!

Ever felt like wrangling AI applications is like trying to tame a wild beast? Well, don't panic! Today, we're strapping on our containerization belts and diving headfirst into Google Kubernetes Engine (GKE) to build scalable, resilient AI applications. Think of it as giving your AI the ultimate power up!

The Secret Sauce

First, lets talk containers. It's like packing your AI application in to a neat, self contained box. Docker makes this easy. Here's a simple Dockerfile or a Python based AI app:

                
FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["python", "app.py"]

This Dockerfile sets up a Python environment, installs dependencies, and runs our AI application. Simple, right?

The Orchestration Maestro

Now, let's bring in the maestro: Google Kubernetes Engine (GKE). GKE lets us orchestrate our containers, ensuring our AI app scales smoothly. Here's a snippet of a Kubernetes deployment YAML:

                  
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-app-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ai-app
  template:
    metadata:
      labels:
        app: ai-app
    spec:
      containers:
      - name: ai-app-container
        image: gcr.io/your-project/ai-app-image:latest
        resources:
        request:
          cpu: "500m"
          memory: "1Gi"
        Limits: 
          cpu: "1000m"
          memory: "2Gi"

This YAML file create a deployment with three replicas of our AI application. We're also specifying resource requests and limits to keep things running smoothly.

Mastering Scalability

Here's the real magic: Kubernetes Horizontal Pod Autoscaling (HPA). This lets our AI app scale automatically based on resource usage.

kubectl autoscale deployment ai-app-deployment \
  --cpu-percent=80 \
  --min=3
  --max=10

This command sets up HPA to scale our deployment between 3 and 10 replicas, based on CPU usage. It's like giving our AI app a turbo boost!

Tips for Kubernetes Mastery

• Use Namespaces: Organize your resources with namespaces. It's like having separate rooms in you Kubernetes house.
• Leverage Labels and Selectors: Use labels and selectors to manage and query your resources efficiently.
• Monitor and Alert: Setup monitoring and alerting with tools like Prometheus and Grafana to keep an eye on your cluster.
• Embrace Infrastructure as Code: Use tools like Terraform to manage your GKE cluster.

The Power of Scalable AI

With GKE, we can build AI applications that scale effortlessly, handling any workload. It's like having an AI army at your fingertips!

Turbocharged XR Pipelines with Serverless Sorcery

April 16, 2025 by Kee

Hey Data Explorers! Feeling that mid week slump? Let's kick it to the curb with some serious data engineering magic! We're diving into the world of streamlined data pipelines, BigQuery, Dataflow, and serverless Cloud Functions, all to power the immersive experiences of Virtual Reality (VR), Augmented Reality (AR), and Extreme Reality (XR). Imagine reducing processing times by a whopping 40% - that's the kind of boost we're talking about!

The XR Data Challenge

Building XR experiences generates massive amounts of data: sensor readings, user interactions, 3D model daa, and more. Processing this data efficiently is crucial for real-time insights and seamless user experiences.

Streamlining with Dataflow & BigQuery

Let's optimize our pipelines! Dataflow is our trusty sidekick for processing large datasets in parallel. Here's a glimpse of a Python Dataflow Pipeline:

              
import apache_beam as beam
from apache_beam.options.pipline_options import PipelineOptions

def process_xr_data(element):
# perform data transformations and enrichments
return element

with beam.PipeLine(option=PipelineOptions()) as p:
(p
| 'Read from Pub/Sub' >> beam.io.ReadFromPubSub(topic='projects/your-project/topics/xr-data')
| 'Process Data' >> beam.Map(process_xr-data)
| 'Write to BigQuery' >> beam.io.WriteToBigQuery(
    table='your-project:xr_dataset.xr_data_table',
    schema='your-schema_string',
    create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED,
    write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND) 
)

This pipeline reads data from Pub/Sub, processes it, and writes it to BigQuery. BigQuery then becomes our powerful data warehouse for analysis and visualization.

Cloud Functions for Real-Time Insights

To add real-time capabilities, let's bring in Cloud Functions. Imagine triggering a function every time a suer interacts with an AR Object:

from google.cloud import bigquery

def handle_ar_interaction(data, context):
  client = bigQuery.Client()
  table_id = "your-project.xr_dataset.ar_interactions"

  rows_to_insert = [
    {
      "user_id": data["user_id"],
      "object_id": data["object_id"],
      "timestamp": context.timestamp,
  }
]
errors = client.insert_rows_json(table_id, rows_to_insert)
  if error == []:
    print("New rows have been added.")
  else
    print("Encountered errors while inserting rows: {}" .format(errors))

This function inserts user interaction data into BigQuery in real-time. Serverless functions are perfect for event driven architectures!

The Impact

By optimizing our Dataflow pipelines and leveraging serverless functions, we've achieved a remarkable 40% reduction in processing time. This means faster insights, smoother user experiences, and more time for innovation in XR space.

Architecture for Immersive Experiences

Our architecture looks something like this:

• Data Ingestion: Senor data, user interactions, and 3D model data are ingested via Pub/Sub.
• Data Processing: Dataflow processes and transforms the data in parallel.
• Data Storage: BigQuery stores the processed data for analysis.
• Real-Time Insights: Cloud Functions provide real-time updates and triggers.
• Visualization: Looker Studio or custom dashboards visualize the data for XR developers.

Mid-Week Motivation Boost

Remember, data engineering is the backbone of groundbreaking technologies like VR/AR/XR. By streamlining our pipelines and leveraging serverless tools, we're not just moving data, we're powering the future of immersive experiences.