Deep automation in machine learning

[A version of this post appears on the O’Reilly Radar.]

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline.

By Ben Lorica and Mike Loukides

In a previous post, we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure. Since that time, Andrej Karpathy has made some more predictions about the fate of software development: he envisions a Software 2.0, in which the nature of software development has fundamentally changed. Humans no longer implement code that solves business problems; instead, they define desired behaviors and train algorithms to solve their problems. As he writes, “a neural network is a better piece of code than anything you or I can come up with in a large fraction of valuable verticals.” We won’t be writing code to optimize scheduling in a manufacturing plant; we’ll be training ML algorithms to find optimum performance based on historical data.

If humans are no longer needed to write enterprise applications, what do we do? Humans are still needed to write software, but that software is of a different type. Developers of Software 1.0 have a large body of tools to choose from: IDEs, CI/CD tools, automated testing tools, and so on. The tools for Software 2.0 are only starting to exist; one big task over the next two years is developing the IDEs for machine learning, plus other tools for data management, pipeline management, data cleaning, data provenance, and data lineage.
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Assessing progress in automation technologies

[A version of this post appears on the O’Reilly Radar.]

When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude.

In this post, I share slides and notes from a keynote Roger Chen and I gave at the Artificial Intelligence conference in London in October 2018. We presented an overview of the state of automation technologies: we tried to highlight the state of the key building block technologies and we described how these tools might evolve in the near future.

To assess the state of adoption of machine learning (ML) and AI, we recently conducted a survey that garnered more than 11,000 respondents. As I pointed out in previous posts, we learned many companies are still in the early stages of deploying machine learning:

Companies cite “lack of data” and “lack of skilled people” as the main factors holding back adoption. In many instances, “lack of data” is literally the state of affairs: companies have yet to collect and store the data needed to train the ML models they desire. The “skills gap” is real and persistent. Developers have taken heed of this growth in demand. In our own online learning platform, we are seeing strong growth in usage of content across AI topics, including 77% growth in consumption of content pertaining to deep learning:
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Tools for generating deep neural networks with efficient network architectures

[A version of this post appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Alex Wong on building human-in-the-loop automation solutions for enterprise machine learning.

In this episode of the Data Show, I spoke with Alex Wong, associate professor at the University of Waterloo, and co-founder of DarwinAI, a startup that uses AI to address foundational challenges with deep learning in the enterprise. As the use of machine learning and analytics become more widespread, we’re beginning to see tools that enable data scientists and data engineers to scale and tackle many more problems and maintain more systems. This includes automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection, and hyperparameter tuning, as well as tools for data engineering and data operations.

Wong and his collaborators are building solutions for enterprises, including tools for generating efficient neural networks and for the performance analysis of networks deployed to edge devices.

Here are some highlights from our conversation:

Using AI to democratize deep learning

Having worked in machine learning and deep learning for more than a decade, both in academia as well as industry, it really became very evident to me that there’s a significant barrier to widespread adoption. One of the main things is that it is very difficult to design, build, and explain deep neural networks. I especially wanted to meet operational requirements. The process just involves way too much guesswork, trial and error, so it’s hard to build systems that work in real-world industrial systems.
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