Joseph Goldbeck has recently joined Manifold as a Data Engineer. In this capacity, he will help clients build production data pipelines to prepare for machine learning and flexibly answering business questions, which can involve migrating from legacy systems to modern, cutting-edge technologies. Joe is a backend engineer with extensive experience leading teams and collaborating closely with business stakeholders.
Dr. Jakov Kucan has recently joined Manifold as a Senior Architect. In this capacity, he will be assisting in project delivery for our clients. He understands at a deep level that realizing business value from AI applications means integrating those application into production systems. He helps our clients think through and engineer AI solutions as part of their broader production systems.
Do you ever feel like machine learning is moving so fast that it’s impossible to keep up? You’re not alone — that’s what the hype cycle has lots of people thinking.
Hype bubbles seem to build up every few years around a specific technology, like the cloud, big data, or, in this case, artificial intelligence.
Topics: Data science
Lean AI is a new, innovative practice and its principles should be widely recognizable. A number of existing systems inspired us in the development of Lean AI, including human-centered design at IDEO, the Lean Startup methodology, agile software development principles, and the CRISP-DM approach pioneered by the data-mining community.
Topics: Data science
By Prince Grover and Sourav Dey
Gradient boosting is widely used in industry and has won many Kaggle competitions. The internet already has many good explanations of gradient boosting (we've even shared some selected links in the references), but we've noticed a lack of information about custom loss functions: the why, when, and how. This post is our attempt to summarize the importance of custom loss functions in many real-world problems — and how to implement them with the LightGBM gradient boosting package.
The past 10 years have given us some truly innovative technology; now, healthcare providers are beginning to figure out the best ways to use it. They would do well to follow other industries by listening to consumers — in this case, patients—to determine the best way to incorporate this technology into their workflows.
Jason Carpenter has recently joined Manifold as a Machine Learning Engineer. In this capacity, and with a project range spanning from developing computer vision applications to traditional modeling approaches to building data pipelines, he will be contributing to many client projects as a jack-of-all-trades engineer.