SDKs & Libraries

# 1. Install & Import import datapulse as dp # 2. Initialize Client client = dp.Client(api_key="your_api_key", region="us-east-1") # 3. Create Pipeline pipeline = client.create_pipeline( name="sales-forecast", source="s3://bucket/raw-data/", model="dp-ml/arboreal-forecast-v2" ) # 4. Execute & Monitor result = pipeline.run() print(f"Pipeline completed: {result.status}")

// 1. Import & Initialize const { DataPulse } = require("@datapulse/client"); const client = new DataPulse({ apiKey: "your_api_key", region: "us-east-1" }); // 2. Deploy Model const pipeline = await client.pipelines.create({ name: "user-churn-prediction", source: "postgres://db/production", model: "dp-ml/churn-xgb-v3" }); // 3. Stream Results await pipeline.execute(); console.log(`Status: ${pipeline.status}`);

// 1. Import & Initialize import ( "github.com/datapulse/go-sdk" ) func main() { client := dp.NewClient(dp.WithAPIKey("your_api_key")) // 2. Define Pipeline pipeline, err := client.Pipelines.Create(ctx, dp.PipelineConfig{ Name: "inventory-forecast", Source: "gcs://data-bucket/raw", Model: "dp-ml/prophet-time-v2", }) if err != nil { panic(err) } // 3. Run & Await res := pipeline.Run(ctx) fmt.Printf("Completed: %s\n", res.Status) }