2025-12-07 02:21:44
Step 1: Create a EC2 instance to run all the dependencies on the instance.
Step 2: Configure the AWS CLI, check the AWS version.
Step3: Install EKSCTL and Kubectl in the master machine(ie: EC2 instance).
*Step 4: Create an IAM user, give access to the user (administrator access) and create an access key to configure the AWS *
Step 5: Create a Cluster.
Step 6: Create an OIDC Provider.
_Now the Cluster is created in the EKS Services in AWS _
Step 7: Update Kubectl Context: so that local and remote are connected.
Step 8: Create a Folder and clone the app in the folder.
mkdir K8-practice
git clone app url
Step 9: Create a YAML file for the namespace in Kubernetes folder.
kubectl apply -f namespace.yml
kubectl get namespaces
Step 10: Create a yaml file for Pod in kubernetes folder.
kubectl apply -f pod.yml
kubectl get pods -n nginx
Step 11: Create a yaml for Deployment in kubernetes folder.
kubectl apply -f deployment.yml
kubectl get deployments -n nginx
Step 12: Create a Service yaml in kubernetes folder.
kubectl apply -f service.yml
kubectl get svc
kubectl port-forward service/my-service 8000:8000 --address=0.0.0.0
Reference:
Conclusion:
Overall, deploying Django on Amazon EKS helped me understand the complete workflow—from Dockerizing the application, pushing images to ECR, configuring Kubernetes manifests, and finally exposing the service through a load balancer. This approach not only improves performance and availability but also lays the foundation for implementing CI/CD and future scaling.
2025-12-07 02:16:44
Advertising is Evolving: Here’s Where It’s Heading in the AI Era
As developers, we’re witnessing an explosion of AI applications, each more innovative than the last. Yet despite their potential, many of these apps struggle with a crucial challenge: monetization. Traditional models like subscriptions and paywalls can disrupt user experience, leading to disengagement. So, how do we navigate this landscape without compromising our users’ enjoyment? Enter Monetzly—the first dual-earning platform tailored for AI conversations.
At its core, Monetzly is about more than just monetization; it’s a vision for sustainable AI innovation. Imagine a platform where you can not only monetize your app through user interactions but also earn from hosting relevant ads. This dual-earning model empowers developers while enhancing the user experience, creating a win-win scenario for everyone involved.
Why Dual-Earning Matters
Monetization without Compromise: Say goodbye to intrusive ads and subscription fatigue. With Monetzly, you can monetize your application without disrupting the flow of conversation. By integrating your app with our five-minute SDK, you can focus on user engagement while we handle the monetization.
Contextual Relevance: Our AI-powered contextual matching ensures that ads are relevant to the conversation happening in your app. This means that users see ads that matter to them, increasing engagement and making your application a more valuable experience.
Developer-First Approach: We’re committed to the developer community. Monetzly features an advertiser marketplace designed specifically for you. This means advertisers can easily find your app, ensuring that you benefit from every relevant interaction.
Let’s say you're building an AI-powered health assistant. A user interacts with your app, asking about nutrition. With Monetzly, not only can you monetize this interaction, but you can display contextually relevant ads for healthy meal plans or local grocery delivery services. This enriches user experience and increases the chances of conversion for advertisers.
For advertisers, Monetzly provides access to an engaged audience of AI app users. They benefit from contextually relevant ad placement, ensuring that their messages resonate with potential customers. This synergy not only enhances the effectiveness of campaigns but also drives revenue for developers.
As we move forward, the landscape of advertising in AI interactions will only continue to evolve. By embracing platforms like Monetzly, you’re not just adapting to change—you’re helping to shape it.
Ready to transform your monetization strategy? Check out Monetzly today and learn how you can become a part of this exciting journey towards sustainable AI innovation.
Let’s redefine the future of conversation commerce together.
2025-12-07 02:16:22
Contribute to tejiri-code/pulse-ai development by creating an account on GitHub.
github.com
2025-12-07 02:15:28
Avoiding Hidden Biases in Transfer Learning: A Practical Tip
As ML practitioners, we often rely on pre-trained models to speed up development and improve performance. However, transfer learning can also perpetuate biases if not applied thoughtfully. A practical tip to avoid hidden biases is to analyze the data distribution of the pre-trained model and compare it to your target dataset.
Step 1: Check the Pre-Trained Model's Data Distribution
Open the pre-trained model's documentation or source code and review the dataset used for training. Look for information on the data's geographic dispersion, population demographics, and any notable data curation processes.
Step 2: Compare with Your Target Dataset
Compare the pre-trained model's data distribution with your target dataset's characteristics. Are there significant differences in demographic representation, data density across regions, or other notable variations? These disparities can hint at potential biases being transferred to your model.
Step 3: Update the Pre-Trained Model
If you find discrepancies, consider updating the pre-trained model to better align with your target dataset. This can involve fine-tuning the model on your specific data, incorporating additional data from underrepresented groups, or using more inclusive data preprocessing techniques.
Example: A natural language processing (NLP) model trained on news articles from the United States might contain biases reflecting the regional media landscape. When applied to a dataset from a developing country, the model's biases in language, cultural, and geographical perspectives become apparent. Updating the model to account for these differences can improve its performance and fairness on the target dataset.
Takeaway: When using transfer learning, it's essential to examine the pre-trained model's data distribution and compare it to your target dataset. This simple yet crucial step can help identify and mitigate hidden biases, ensuring your model is fair, accurate, and effective.
Publicado automáticamente
2025-12-07 02:14:29
Learn how to eliminate technical debt, simplify upgrades, and modernize faster with Liferay DXP and Veriday’s proven upgrade-friendly engineering approach.
veriday.com
2025-12-07 02:12:32
Liferay upgrades shouldn’t feel like rebuilding your platform from scratch.
If your team is battling legacy customizations, fragile modules, or unpredictable upgrade cycles, this guide breaks down a clean, engineering-friendly approach to reducing technical debt before the next release hits.
It also shows how modern architectures, modular design, and upgrade-ready frameworks can simplify long-term maintenance.
Dive in here
