Tags: production engineering*

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  1. • 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
  2. 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.
  3. Infrastructure observability companies such as New Relic, Datadog, Dynatrace, Elastic and Splunk are actively enhancing their platforms through the integration of LLMs.
  4. 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.
  5. Launched in 2007, Chess.com is a premium platform for online chess and one of the largest of its kind. A Cloud SQL for MySQL shop, it transitioned to Cloud SQL Enterprise Plus edition, improving the user experience, cutting costs, and significantly reducing response times, decreasing p99 latency response from 14ms to 4ms. Read on to learn more.
  6. With all the hype around AI/ML in observability, it's more likely than ever that companies benefit from storing and viewing data in one system and training ML models in another.

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