This article discusses causal inference, an emerging field in machine learning that goes beyond predicting what could happen to focus on understanding the cause-and-effect relationships in data. The author explains how to detect and fix errors in a directed acyclic graph (DAG) to make it a valid representation of the underlying data.
This article discusses the importance of empowering developers to take control over their applications across environments by adopting live debugging observability practices to reduce the disconnection between developers and their live applications in production.
In this article, we explore how to deploy and manage machine learning models using Google Kubernetes Engine (GKE), Google AI Platform, and TensorFlow Serving. We will cover the steps to create a machine learning model and deploy it on a Kubernetes cluster for inference.
This article explains the differences between monoliths and microservices, their benefits and tradeoffs, and provides heuristics to help you decide which architecture is best for your application.
PySpark for time-series data, discussing data ingestion, extraction, and visualization with practical implementation code.
• Continuous Integration (CI) and Continuous Deployment (CD) pipelines for Machine Learning (ML) applications
• Importance of CI/CD in ML lifecycle
• Designing CI/CD pipelines for ML models
• Automating model training, deployment, and monitoring
• Overview of tools and platforms used for CI/CD in ML
Service Development Kit that uses Terraform, AWS ECS, Rust, Actix App, Postgress RDS, LLM, RAG, Cloudflare
• step-by-step guide on how to set up the service development kit, including creating an SSL certificate, setting up Terraform, and configuring Cloudflare.
• Rust, LLM, and RAG in the service development kit.
Infrastructure observability companies such as New Relic, Datadog, Dynatrace, Elastic and Splunk are actively enhancing their platforms through the integration of LLMs.
Microsoft revealed a new AI tool called Infra Copilot, which uses its existing GitHub Copilot to create infrastructure code.
Infra Copilot is designed to understand the context of infrastructure tasks and generate appropriate code suggestions based on natural language prompts.
The tool can streamline the coding process, enabling professionals to focus on higher-level tasks.
It also provides standardized code snippets for consistency across different environments.
Infra Copilot is available now to programmers with a recent Visual Studio Code version and a GitHub Copilot license.
Microsoft has also launched GitHub Copilot Enterprise, using data from a company's own code repositories to generate code and answer questions, priced at $39 per month per user.
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