Manifold Blog

Manifold Blog

How to Apply and Optimize Your Algorithm When You're Ready to Run With AI

Posted by Sourav Dey on Mar 29, 2019 5:50:00 AM

Amazon’s recently launched SageMaker artificial intelligenceservice is an exciting new development, but the program doesn’t do it all. There’s a distinct gap between innovative AI technology that exists and AI solutions that will help drive business results in your specific case. Using products such as SageMaker is like having a brand-new Tesla Model S: It’s an awesome car, but it’s a giant electric paperweight if you don’t know how to drive.

We discussed "walking" with AI in a prior Entrepreneur article; now it’s time to hit the ground running.

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Topics: Data science

Walking With AI: How to Spot, Store and Clean the Data You Need

Posted by Sourav Dey on Mar 28, 2019 5:46:10 AM

Last August, data science leader Monica Rogati unveiled a new way for entrepreneurs to think about artificial intelligence. Modeled after psychologist Abraham Maslow's five-tier hierarchy of psychological needs, her AI hierarchy of needs has become a conference favorite for illustrating how to incorporate AI into a business.

Despite entrepreneurs' excitement around AI, Rogati's hierarchy makes an uncomfortable point. Few companies are ready to adopt AI. Most are struggling to fulfill fundamental needs, such as reliable data flow and storage. The truth is that data literacy is lacking at most companies hoping to reap the rewards of AI.

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Topics: Data science

How to Quickly Build a Gesture Recognition System

Posted by Rajendra Koppula on Feb 14, 2019 7:00:00 AM

Gesture recognition is a key part of the future of design, and is poised to become the next inflection point in how we interact with devices.

Gesture-based interactions are already prevalent in AR and VR devices; for example, here are some available interactions from Microsoft HoloLens. But gestures have the potential to make a far-reaching impact beyond these specialized uses cases: imagine interacting with everyday objects and machines with just a motion of your hand, instead of pushing buttons or turning knobs. This future may not be as far-off as it seems. The principal driver behind the progress in this space is state-of-the-art computer vision technology that enables machines to recognize human gestures.

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Topics: Data science, Computer vision

Using Dask in Machine Learning: Best Practices

Posted by Jason Carpenter on Jan 31, 2019 6:00:00 AM


The Python ecosystem offers a number of incredibly useful open source tools for data scientists and machine learning (ML) practitioners. One such tool is Dask, available from Anaconda. At Manifold, we have used Dask extensively to build scalable ML pipelines.

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Topics: Data science, Data engineering, Machine learning

Preparing Your Data for Predictive Analytics

Posted by Sourav Dey on Nov 16, 2018 2:17:23 PM

By Kyle Seaman, Head of Product at Sentenai, and Sourav Dey, Co-Founder and CTO at Manifold

Predictive analytics is an undeniably valuable technology, with research indicating its market size could top $12 billion USD by 2022. Across a range of industries, businesses, and applications, using historical data to predict future outcomes can lead to greater operational efficiency in a variety of ways. Predictive analytics can enable organizations to streamline their operational processes, optimize their demand forecasting, drastically reduce downtime, and better understand their customers’ propensity to buy.

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Topics: Data science

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

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

Distance Matrix Vectorization Trick

Posted by Sourav Dey on Aug 15, 2016 7:00:00 AM

A common problem that comes up in machine learning is needing to find the l2-distance between two sets of vectors. For example, in implementing the k-nearest-neighbors algorithm, we have to find the l2-distance between the a set of test vectors, held in a matrix X (MxD), and a set of training vectors, held in a matrix X_train (NxD). Our goal is to create a distance matrix D (MxN) that contains the l2-distance from every test vector to every training vector. How can we do this efficiently?

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Topics: Data science

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