Manifold Blog

Manifold Blog

Manifold Welcomes Joseph Goldbeck to the Team

Posted by Manifold Team on Oct 22, 2018 7:00:00 AM

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.

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Topics: News

Manifold Welcomes Dr. Jakov Kucan to the Team

Posted by Manifold Team on Oct 15, 2018 7:00:00 AM

Dr. Jakov Kucan has recently joined Manifold as a Senior Architect. In this capacity, he will be assisting in project delivery for our clients. Jakov is a skilled architect and engineer, able to see through the details of implementations, keep track of the dependencies within a large design, and communicate the vision and ideas to both technical and non-technical audience.

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Topics: News

Intimidated by AI? Ask Yourself These 5 Questions, And You’re Halfway to Implementation

Posted by Vinay Seth Mohta on Oct 4, 2018 1:11:28 PM

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.

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Topics: Data science

Lean AI: A 6-Step Guide to Making a Tangible Business Impact as Efficiently as Possible

Posted by Vinay Seth Mohta on Oct 1, 2018 3:10:34 PM

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.

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Topics: Data science

Custom Loss Functions for Gradient Boosting

Posted by Prince Grover on Sep 28, 2018 3:27:51 PM

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.

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Topics: Data science, Data engineering

Manifold Welcomes Martin Davy as an Advisor

Posted by Manifold Team on Sep 24, 2018 7:00:00 AM
Martin Davy has recently joined Manifold as an Advisor on the FinTech Executive Council. Martin understands at a deep level what it means to embark on AI projects at enterprise scale, and has a wealth of experience in managing software platforms for multi-national companies in verticals from financial services to publishing.

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Topics: News

How to Meet Patient Demand for AI in Health Care

Posted by Vinay Seth Mohta on Aug 28, 2018 7:00:00 AM

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.

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Topics: Healthcare, AI at the edge

Manifold Welcomes Matyas Tamas to the Team

Posted by Manifold Team on Aug 10, 2018 7:00:00 AM
Matyas Tamas has recently joined Manifold as Director of Data Science. He uses his extensive background in developing machine learning models that have been deployed at scale in production to inform the design and development of models for all classes of problems faced by our clients.

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Topics: News

Manifold Welcomes Julie Steele to the Team

Posted by Manifold Team on Aug 1, 2018 7:00:00 AM
Julie Steele has recently joined Manifold as Director of Marketing. In this capacity, she is helping to build awareness of our brand and services and is also working with our engineering team to spread our ideas and approach to AI to a broader audience. Julie knows how to translate technical ideas for a variety of audiences in a way that builds real understanding and relationships.

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Topics: News

Applications of Matrix Decompositions for Machine Learning

Posted by Prince Grover on Jul 25, 2018 9:00:00 AM

In machine learning and statistics, we often have to deal with structural data, which is generally represented as a table of rows and columns, or a matrix. A lot of problems in machine learning can be solved using matrix algebra and vector calculus. In this blog, I’m going to discuss a few problems that can be solved using matrix decomposition techniques. I’m also going to talk about which particular decomposition techniques have been shown to work better for a number of ML problems.

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Topics: Data science

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