Machine Learning, Accelerated

Manifold designs, builds, deploys, and manages complex machine learning applications at scale.

Key Team Members

Our team has a track record at companies like Google, HubSpot, Kayak, Qualcomm, Marqeta, and Uber. We speak at a variety of industry events—including Strata, ODSC, and Enterprise Data World—and have been featured in publications such as Entrepreneur, TheNextWeb, ReadWrite, and KD Nuggets.

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SOURAV DEY

Chief Technology Officer

  • Staff Engineer, Google
  • Principal Data Scientist, AutoGrid
  • Principal Engineer, Ingenu
  • Senior Engineer, Qualcomm
  • Multiple patents (intrusion detection, user similarity, wireless)
  • PhD, MS & BS, EECS – MIT

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RAJENDRA KOPPULA

Director, Machine Learning

  • Recent projects spanning computer vision, IoT, machine learning, and deep learning applications
  • Staff Engineer, 3G/4G Cellular Group, Qualcomm
  • MS, Statistics & MS, EE – Northern Illinois University; BS, EECS – Osmania University

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MATYAS TAMAS

Director, Data Science

  • Head of Supply Data Science, Uber
  • Founder of Data Science team, Quora
  • BS, Physics – Caltech

Approach

How We Harness 10x Engineering

Software product development is often described as a decision maze of product-market and technology choices. A 10x engineer is able to expertly navigate that maze.

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How We Produce Results with Lean AI

At its core, Lean AI is a set of mental models that help teams work together smoothly to deliver business value faster when implementing AI in their organization.

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How We Work with Clients

We can help accelerate your data projects. See how working with us reduces risk substantially, and explore what a project with Manifold looks like day to day.

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Perspectives

USING DASK IN MACHINE LEARNING: BEST PRACTICES
 

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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MACHINE LEARNING BACKGROUND AND TRAINING RESOURCES
 

The following resource list was compiled by the Manifold team. We hope to continue updating and improving this list, and may reshare it out periodically in the hopes that others who embark on this journey can have an even smoother and more fulfilling experience.

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EXPLORATION VS. EXPLOITATION IN REINFORCEMENT LEARNING

The last five years have seen many new developments in reinforcement learning (RL), a very interesting sub-field of machine learning (ML). As RL comes into its own, it's becoming clear that a key concept in all RL algorithms is the tradeoff between exploration and exploitation.

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WE NEED TO BUILD INTERACTIVE COMPUTER VISION SYSTEMS

We're in a time of transition, as we learn more and more about what AI is (and is not) capable of. We need to start designing more interactive algorithms with end use cases in mind—break our solution into modules with outputs that are understandable by regular users.

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HOW TO QUICKLY BUILD A GESTURE RECOGNITION SYSTEM
 

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. This future may not be as far-off as it seems.

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2018 PROVED THAT COMPUTER VISION IS THE MOST POWERFUL MANIFESTATION OF AI

Computer vision as we know it is at a tipping point. Thanks to industry-wide development efforts and advances in deep learning algorithms and graphics processors, we’re doing things that were unimaginable just a decade ago.

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