johnidm

BigQuery ML BQML is a powerful feature within Google BigQuery that allows users to create, train, and execute machine learning models directly using SQL queries.

This approach aims to democratize machine learning by enabling SQL practitioners to build models with their existing tools, eliminating the need to move large amounts of data to separate ML frameworks or services.

BigQuery ML supports a variety of machine learning models and algorithms, catering to different analytical needs. Here's a breakdown of the types you can use:

Internally Trained Models

These models can be built directly within BigQuery ML using SQL:

johnidm

Step-by-step: Initialize DVC in a repo for datasets and binary model versions

Below is a clean, practical workflow to add DVC to an existing Git repo and start versioning your data and model artifacts (e.g., large/binary files). Adjust remote choices (S3, GCS, Azure, SSH, local) to your environment.

Prerequisites:

• Git installed and repository initialized
• DVC installed with S3/GCS/Azure support as needed (https://dvc.org/doc/install)

Verify:

johnidm

KServe supports a wide variety of model formats across both predictive and generative AI inference.

Predictive AI Model Formats

spaCy models are not natively supported by KServe (as of 2025). However, you can serve spaCy models easily using KServe’s Python model server — the kserve SDK provides a way to deploy custom Python models (like spaCy NLP pipelines, HuggingFace models, or anything else in Python).

🧠 1. TensorFlow: TensorFlow SavedModel format (kserve-tensorflow-serving)

Formats:

johnidm

🔹 1. Use streaming instead of loading everything into memory

Instead of await file.read(), you can save the file temporarily to disk and then process it:

import tempfile
from fastapi import UploadFile, HTTPException, status

async def process_file(file: UploadFile, mode: str):
    if file.content_type != "application/pdf":
 raise HTTPException(

johnidm

You can give different weights to the fields in PostgreSQL full-text search by using setweight() inside your to_tsvector.

SELECT
    id,
    title,
    area,
    tags,
    content,
    url,

johnidm

Install the dependency

pip install ruff

Foramt files

johnidm

Prompt One

Design and develop a modern question and answer (Q&A) web application using Next.js and TypeScript. The application should prioritize a clean user experience, efficient data management, and adherence to current web development best practices.

Don't implement user Authentication:

# Core Features:

Question Management:

johnidm

https://community.openai.com/t/how-does-openai-charge-tokens-when-sending-pdf-content-in-a-prompt/1280985

OpenAI

from openai import OpenAI
import base64
from pydantic import BaseModel

johnidm

Prompt que executa uma analise no seu perfil do Linkedin com o objetivo de melhorar ele.

Passo 01

Salve o seu perfil em PDF.

Passo 02

Fazer o upload do PDF em alguma LLM e executar o prompt abaixo.

johnidm

Precise Prompting

from openai import OpenAI


api_key = "sk-proj-..."
client = OpenAI(api_key=api_key)