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Databand.ai
Machine learning development orchestration platform
Business Overview
Dec 2017
Date Founded
Active
Operating Status
Revenue Generating
Revenue Stage
B2B
Business Model
20
Employees
Business Stage
Launched
Funding Rounds
$18.5 M
Total Funding
2
Funding Rounds
3.63x
Funding Growth
New wpDataTable
Date Announced
Funding Round
Amount Raised
Investors
AI Technology Stack
3
AI Team Size
Vertical AI
AI Application
AI Description
Databand.ai platform orchestrates ML creation and data processing within organizations and provides visibility to data scientists and engineers involved in the process. The platform streamlines the integration, productization, and testing of ML pipelines, thus enabling the different stakeholders to work together on ML projects in an efficient, frictionless, way.
These are some recent contributions we’ve made to Apache Airflow:
InProcess Executor
Together with our friends from Polidea we created a new executor useful for debugging and DAG development purposes. This executor executes single task instance at time and is able to work with SQLite and sensors.
Scheduler Optimizations
Working with Polidea, we’ve made major progress in optimizing Airflow scheduler performance. In total, tests are showing 10x faster query performance with over 2000 fewer queries by count. See the list below for some of the optimizations that have been pushed (and counting):
[AIRFLOW-6856] Bulk fetch paused_dag_ids
[AIRFLOW-6857] Bulk sync DAGs
[AIRFLOW-6862] Do not check the freshness of fresh DAG
[AIRFLOW-6869] Bulk fetch DAGRuns for _process_task_instances
[AIRFLOW-6881] Bulk fetch DAGRun for create_dag_run
[AIRFLOW-6887] Do not check the state of fresh DAGRun
AI Types
AI Tools
Azure Machine Learning, Databricks, Google Cloud, mlFlow, Spark
In The News
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STATUS
Active
FUNDING
$3 M
STATUS
Active
STATUS
Active
FUNDING STAGE
Seed
