Machine Learning Engineering

We develop custom data capabilities you own. Let us help you accelerate your digital transformation and build data capabilities that create operational efficiencies, reduce risk, invent differentiating features, or just predict the future.

Expertise

NLP

Computer Vision

Robotic Process Automation

API Development

Recommender Systems

Document AI

Dialog Systems

Anomaly Detection

Predictive Analytics

Data Augmentation

The success of artificial intelligence solutions depends on high quantities and quality of data. We provide data augmentation services to improve and expand existing data features. We survey adjacent internal and external data sources to join, creating additional opportunity for a solution development.

Data Brokering

If the data you need is for sale, KUNGFU.AI has the relationships to acquire it. We partner with leading data providers and have experience brokering data deals.

Crawling

Sometimes the data you need is not for sale. We automate structured data collection on the web and package it up using the latest web crawling technologies.

Sourcing

Sometime web crawling is not impossible and human intervention is key. KUNGFU.AI has built a team of hundreds of contract workers, all connected virtually, all tested and qualified, that are waiting for work RIGHT now.

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Assessment

For most businesses, augmentation opportunities are not obvious. Most AI journeys require upfront assessment to understand where data wealth, differentiation, and dependencies exist. Our data strategy assessment will take a deep look at all of the data that is important to your organization. This assessment helps identify gaps, quality issues, augmentation opportunities, and establish a data collection strategy.

Practical ML Engineering

One of the most important decisions for the future of your ML development is the selection of the right model that’s optimized for the business objective. Do you value performance or explainability? Precision or Recall? The correct approach is only correct if it accounts for your data and goals. Our senior, Ph.D. machine learning practice leads use their decades of experience to understand the tradeoffs between the bleeding edge and classic modeling techniques to achieve the best possible outcome.  

 

 

ML Engineering is not Software Engineering

Machine Learning Engineering is a different process than traditional software engineering, though they are both more successful as agile processes. Machine learning is less code, but needs to train on data. Software capabilities are static, but ML is dynamic and generalizes on new information. Models need to be continuously fed new data so they can improve over time. Without attention, the performance can drift. We’ve adopted an agile process that stacks strategy, data analysis, model experimentation, then moves on to train and test the selected models. Once we deploy the solution, we want to monitor, analyze, and re-train all over again. This enhances the data capability over time.