Manifold Blog

Manifold Blog

Manifold Welcomes Rachel Lomasky to the Team

Posted by Manifold Team on Jun 3, 2019 6:00:00 AM

Dr. Rachel Lomasky has recently joined Manifold as a Director of Machine Learning. In this capacity, she will be helping clients train and productionalize their machine learning algorithms.

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Topics: News

Manifold Welcomes Meghan Blanchette to the Team

Posted by Manifold Team on May 30, 2019 6:00:00 AM

Meghan Blanchette has recently joined Manifold as Director of Communications. In this capacity, she will be creating and executing a communications plan that helps colleagues be as successful as possible during sales conversations, as well as building content relationships with outside companies.

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Topics: News

Manifold Welcomes Michael Stefferson to the Team

Posted by Manifold Team on May 28, 2019 6:00:00 AM

Dr. Michael Stefferson has recently joined Manifold as a Machine Learning Engineer. He will be building end-to-end machine learning pipelines for Manifold's customers — helping to improve, restructure, or design novel solutions for data-driven problems. Michael has extensive experience in numerical analysis, data science, and software engineering.

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Topics: News

Manifold Welcomes Dragos Velicanu to the Team

Posted by Manifold Team on May 24, 2019 6:00:00 AM

Dragos Velicanu has recently joined Manifold as a Data Engineer. In this capacity, he will be building production-level ETL pipelines, and designing and implementing new systems that can scale with large amounts of data. Dragos has a strong background in particle physics research, analysis, software engineering, and data processing workflows.

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Topics: News

Using Dask in Machine Learning: Preprocessing

Posted by Jason Carpenter on Apr 25, 2019 6:00:00 AM

Introduction

This is the second post in a five part series about using Dask in machine learning workflows:

  • Using Dask in Machine Learning: Best Practices
  • Using Dask in Machine Learning: Preprocessing
  • Using Dask in Machine Learning: Feature Engineering
  • Using Dask in Machine Learning: Model Training
  • Using Dask in Machine Learning: Model Evaluation

Starting with this post, each installment will have data snapshots and code snippets to give you an example of the problem we are working on. We have this public self-contained GitHub repo. You can pull that repo and run the code yourself and follow along more closely.

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Topics: Data engineering, Machine learning

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

This Is How to Get Started With AI When the Only Thing You Know Is the Acronym

Posted by Sourav Dey on Mar 27, 2019 5:42:46 AM

Unless you’ve been living under a rock, you’ve heard the buzz around artificial intelligence. So it might surprise you to learn that, according to a 2017 survey published by McKinsey Global Institute, out of 3,000 AI-aware executives, only one in five are using any AI-related technology in core areas of their businesses.

Why aren't entrepreneurs and executives jumping on what they know to be a market-changing technology? In a word, uncertainty. With AI still young, leaders aren't sure where to apply it, how to ensure a return on their investment or, most of all, how to implement it.

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Topics: Computer vision

Your Project Needs a Data Readiness Audit

Posted by Vinay Seth Mohta on Mar 21, 2019 6:00:00 AM

In the early phase of a new project, we dive into the “Understand” step of our Lean AI framework. There are two main forms of understanding we aim for — business understanding and data understanding.

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Topics: Data engineering

We Need to Build Interactive Computer Vision Systems

Posted by Ajay Mishra on Feb 28, 2019 6:00:00 AM

You hear strong proclamations about how AI is taking over the world. And then, you read about how sophisticated AI models are easily fooled by small perturbations in input (see the figure below).

Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects

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Topics: Computer vision

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