Simplifying AI implementations across all domains
Data is the new crude oil, and Blackstraw’s AI workbench is designed to reduce the time it takes to process this crude oil into a refined product. Our secure, cloud-based AI workbench is equipped with platforms that not only facilitate—but fast track—AI Implementations of all types!
Data Workbench
Sophisticated machine learning models can obscure the inherent unreliability of the data being fed into them. Blackstraw is taking data quality to new heights, developing tools to shine a harsh light on a broad range of data processes to build more accurate and usable aggregates.
Data Sourcing
Our AI workbench supports data sourcing initiatives by providing tools to fetch data from both online or field sources. Blackstraw’s web scraping supports data extraction for every conceivable AI use case and has robust tools to support dynamic, sitemap, category, and location scraping.
Data Ingestion
Our data ingestion workbench can use application programming interfaces (API’s) provided by the client, or ready-to-use Blackstraw custom APIs, to ingest data from Blob Storage, S3 Bucket, GCP Storage, any relational or non-relational database, FTP servers, or local folders.
Data Pre-Processing
Our data pre-processing workbench comes with a suite of tools for image segregation, blur detection, shadow removal, perspective correction, and noise removal that can be used for a variety of AI deployments.
Labeling Workbench
Data labelers play a crucial role in AI development and deployment. Our labeling workbench comes fastened with tools that enable data labelers in building accurate and diverse training datasets in a shortened time frame. This API-first, semi-automated workbench uses active learning to expedite the labeling life cycle, which saves your business valuable time and is easily scalable.
Image & Video Labeling
Blackstraw’s image and video tools are proven to meet tasks demanding high precision, such as object detection, localization, and image classification tasks. We have successfully trained models using partial labels, reducing the cost of distilling noisy multi-label datasets. Apart from the typical labeling toolset, our pixel selector natively handles fine-grained classes and allows for precise semantic and instance segmentation of objects.
Text Labeling Tool
Blackstraw’s multi-user text annotation tool supports both manual and automatic annotation, standoff, and inline annotation. The tool supports processes starting from the preparation of linguistic artifacts to solving entrenched problems in metadata extraction, sentiment analysis, content classification, context recognition, and conversational AI.
Modeling Workbench
Blackstraw’s modeling workbench is framework-agnostic and removes the complexity in constructing sophisticated models across a range of AI domains such as Natural Language Processing, Computer Vision, Reinforcement Learning, Predictive Analytics, and Statistical Inference.
Model Creation
The platform supports a simple drag and drop modeling workflow creation which recommends the best modeling architecture with default hyperparameters based on the problem intended to be solved, the data, and the expected output. For the advanced user, the workbench comes integrated with Jupyter notebook to customize existing machine learning models or build them from scratch.
Model Comparison
Simulations can be run to compare the quality of outputs among machine learning models and track how they perform under different input conditions. The system then recommends evaluation criteria that are best suited for the specific problem type and models being tested. One or more models can then be selected for further tuning.
Model Tuning
The selected model(s) hyperparameters can then be tuned to give greater accuracy. Dashboards help gauge the output of the changes in hyperparameters, which enables users to tune the model more quickly compared to a manual tuning without immediate access to outputs.
Operationalization Workbench
The complexity of building an AI model pales in comparison to scaling it to production. Our workbench is designed to tame this complexity by attaching equal importance to operationalization as for model building.
The Operationalization Workbench is seamlessly integrated with the Data & Labeling Modeling Workbench to efficiently manage concept drift and retrain models. In addition, a strong feedback loop monitors end-user adoption and usage patterns— ensuring you’re always on track to meet the business goals that really matter to you!
Deployment
The workbench uses popular deep learning model hosting servers and frameworks to make the model available for thousands of requests at the same time with high inference throughput. Deployment is a simple, one-click affair using CICD Pipelines which include Bitbucket, Jenkins, Docker, K8, and Terraform.
Model Monitoring
Blackstraw’s operationalization workbench comes with tools to monitor model degradation and corresponding trade-offs in memory, power, and speed. The result? An AI solution that truly works for your business.
Speak to a Specialist Now
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