Get Fired Up: Google Wins and AI Adventures!
April 11th 2025 by
Kee
... Seriously, its been a wild ride of cloud wizardry and AI breakthrough!
Alright, folks! Friday is here, and I'm buzzing with excitement to share what's
been happening this week. Seriously, its been a wild ride of cloud wizardry and AI breakthrough!
1. Cloud Migration: We Crushed It!
First off, we tackled our cloud migration optimization head on. And guess what?
We absolutely crushed it! We dug deep into the numbers, tweaked the processes, and
boom! We slashed timelines and saved a ton of resources. It's like finding hidden
treasure when you see those optimizations pay off.
2. GKE + AI: Model Magic in Action!
Then we dove into deploying our AI models on Google Kubernetes Engine (GKE).
Watching those models come to life in a scalable, rock solid environment? Pure
magic! GKE is seriously powerful, and seeing our AI in action is just...WOW.
3. FaaS: Serverless Superpowers!
Let's talk serverless! We got our hands dirty with Functions as a Service,
and its been a game-changer. Building those nimble, on demand functions has
streamlined so much of our workflow. Its like giving our microservices superpowers.
4. Terraform: infrastructure, Sorted!
And finally, we gave our infrastructure a serious upgrade with Terraform.
Automating everything? It's like having a well oiled machine. Consistency,
reliability, efficiency - Terraform delivers!
In a Nutshell:
This week? It's been an absolute blast! We've pushed boundaries, solved
tough problems, and witnessed some seriously cool tech in action. I'm so
stoked about the progress we've made, and I can't wait to see what next
week brings.
Have an awesome weekend, everyone! Let's keep innovating!
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
*SOURCES:
https://www.gamesradar.com/video-game-release-dates
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!
1. Vertex AI Pipelines: Your Workflow's Best Friend!
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!
2. Google Kubernetes Engine (GKE): AI Deployment Powerhouse!
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.
3. Vertex AI Workbench: Your Development Hub!
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.
4. Cloud Storage: Data's Safe Haven!
Reliable, scalable, and secure. Cloud Storage is where you store your
precious data. It's the bedrock of any serious AI project.
5. BigQuery: Data Analysis on Steroids!
Need to analyze massive datasets? BigQuery is your weapon of choice. It's
fast, powerful, and makes data exploration a breeze.
6. Cloud Functions: Serverless AI Snippets!
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!
7. Tensor processing Units (TPUs): AI Acceleration!
When you need serious AI acceleration, TPUs deliver. They're designed for
machine learning, and they'll supercharge your training times.
8. Vertex AI Model Monitoring: Keep Your Models Healthy!
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.
9. Identity and Access Management (IAM): Security First!
Security is non-negotiable. IAM lets you control access to your resources
with precision. Keep your data and models safe.
10. Cloud Logging and Monitoring: Insights at Your Fingertips!
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!
Youtube
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:
TERRAFORM: 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.
BASH
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:
PYTHON
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:
TERRAFORM : 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:
DOCKERFILE
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:
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.
BASH
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!
SOURCE:
https://gemini.google.com/app
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:
PYTHON
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:
PYTHON
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.
