Machine Learning, Accelerated

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

Key Team Members

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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 Accelerate AI Roadmaps

You've probably been given a mandate for a significant data transformation. At Manifold, we understand the position you're in, and you're not alone.

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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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