MLOps.community

The Future of AI and ML in Process Automation // Slater Victoroff // MLOps Coffee Sessions #64

Nov 23, 2021

58:01

Creator website

Highlights

AI Chapters

Episode notes

Data Engineering in Machine Learning

01:59

The Cost of a Bespoke Change L Model

01:52

The Intuition of Machine Learning in Machine Learning

04:35

Introduction

2min

Martial Arts - What I Learned in High School

2min

What's Your Journey Into Machine Learning?

3min

Transfer Learning

3min

Is There a Future for Machine Learning?

4min

Structured Versus Unstructured

2min

Is It Machine Learning?

4min

Data Engineering in Machine Learning

2min

Unstructured Data Is Such a Challenge, Right?

4min

10.

Is There a Difference Between Supervised and Unsupervised?

2min

11.

Synthetic Data and the Roll of Synthetics in M L Os

4min

12.

Is the Machine Learning Model Failing on Prat?

1min

13.

The Intuition of Machine Learning in Machine Learning

5min

14.

An Information Flow That Is Overfitting, Right?

1min

15.

Howmanpos Is a Great Example of a Data Management System

2min

16.

The Process of Productionization of a Machine Learning Model Solution

3min

17.

Machine Learning and Data Drift

2min

18.

The Cost of a Bespoke Change L Model

2min

19.

Is It a Good Idea to Use Synthetic Data to Build a Model?

2min

20.

The Limiting Factor in Machine Learning

2min

21.

Machine Learning Engineering

5min

MLOps Coffee Sessions #64 with Slater Victoroff, The Future of AI and ML in Process Automation.

// Abstract
The Unstructured Imperative
Recent advances in AI have dramatically advanced the state of the art around unstructured data, especially in the spaces of NLP and computer vision. Despite this, the adoption of unstructured technologies has remained low. Why do you think that is? How have the dynamics changed in the last five years?

Multimodal AI
Historic AI approaches have generally been constrained to one data modality (i.e. text or image). Recently, a wide range of papers in image captioning and document understanding have emphasized the need for more sophisticated "multimodal" techniques which can fuse information from multiple modalities. What is multimodal learning, and why is it so promising? Why are we seeing such an explosion of activity? What is Indico doing in this space?

Machine Teaching
As methods of supervision become more complex and multi-faceted, many researchers have begun investigating the inverse problem. That is how do we design supervision systems that more naturally follow human processes? What are some interesting trends in "the space", and where can we expect this field to go in the next few years?

// Bio
Slater Victoroff is the Founder and CTO of Indico, an enterprise AI solution for unstructured content that emphasizes document understanding.

Slater has been building machine learning solutions for startups, governments, and Fortune 100 companies for the past seven years and is a frequent speaker at AI conferences.

Indico’s framework requires 1000x less data than traditional machine learning techniques, and they regularly beat the likes of AWS, Google, Microsoft, and IBM in head-to-head bake-offs.

// Relevant Links
https://indico.io

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