Broadening AI Adoption with AutoML

Rate this content

Adoption of AI has been slowed the challenges involved in obtaining performant models, which require significant expertise and effort, and the limited number of practitioners with machine learning expertise. Automated machine learning (AutoML) eliminates the routine steps in the machine learning workflow, thus empowering domain experts without machine learning background to build good initial models, and allowing experienced practitioners to focus additional manual model optimization. This talk describes the extent of automation available for the various steps and demonstrates AutoML with a classifier for human activities based on accelerometer sensor data.

9 min
02 Jul, 2021

Video Summary and Transcription

AutomL simplifies the complexity of building machine learning models, allowing engineers to focus on the hard problems and applications. It enables the solving of problems that wouldn't be feasible otherwise. The three-step AutomL approach by MathWorks includes wavelet scattering for feature extraction. AutoML also enables feature selection and model optimization for memory and power-limited embedded systems. MATLAB can translate to low-level code for deployment.

Available in Español

1. Introduction to AutomL and its Benefits

Short description:

Hello everyone, my name is Bernhard Suhm, I'm product manager for machine learning with the math works. Today I will focus on automel, automation that takes the routine iterative effort and most of the science out of building machine learning models. The point of AutomL is to simplify the complexity of building machine learning models, freeing up engineers to focus on the hard machine learning problems and on their applications. AutomL allows you to solve problems that otherwise wouldn't be feasible like use cases where you need to build many different models representing different variations or different environmental stages. We at MathWorks have developed a three-step AutomL approach that includes wavelet scattering to extract good features from signal and image data.

Hello everyone, my name is Bernhard Suhm, I'm product manager for machine learning with the math works. Let me motivate my topic with some questions to you. Where do you want to apply AI? Are you concerned with the lack of your experience in AI or with black box models? The community widely recognizes these as challenges and barriers to broader adoption of AI across many industries.

Today I will focus on automel, automation that takes the routine iterative effort and most of the science out of building machine learning models. So what exactly is automel? To understand that you need to know a bit about the typical workflow for building machine learning models, the focus of this talk, but building deep neural networks isn't that different. First, you need to process your raw data, deal with its messiness, and get it into a shape that is suitable for later stages, like dealing with missing data and outliers. Next, you need to engineer features, extract a few variables from your data that serve as input to your model, and capture the majority of the variability. That's fairly easy for numeric data, but a lot harder for signals. Next, you're faced with the choice of different machine learning models. And even to experts, it's not clear which model performs best on any given problem. So you have to try multiple, which leads to the model tuning stage, where you assess the performance of some initial models, optimize their hyperparameters, maybe select a subset of features to avoid overfitting. But that may not be enough to get really good performance. You may have to go back, replace some features with others, and do this all over again. If you're familiar with machine learning, you will know the most difficult and time-consuming stages are the feature engineering and the optimization. If your head is spinning now, don't despair because you don't have to know all this complexity. The point of AutomL is to simplify it. Ideally, to go directly from your initial data and your machine learning problem to a model you can deploy. However, really taking it seriously, that is not a realistic expectation. In a single step machine learning is not possible. However, what is realistic is, freeing up engineers like yourself to focus on the hard machine learning problems and on your application. Otherwise, without AutomL, you'll have to find that AI expertise either inside your team and organization or outside. Those data scientists are hard to find and expensive. As the first barrier that AutomL removes, it overcomes the lack of machine learning expertise. But even if you have that expertise, you are increased in productivity because AutomL takes away those time-consuming and iterative steps. Finally, AutomL allows you to solve problems that otherwise wouldn't be feasible like use cases where you need to build many different models representing different variations or different environmental stages.

So how do you apply AutomL for engineering? Most of those engineering applications are based on signal and image data and that's where the future engineering becomes critical for good performance and that's notoriously difficult. We at MathWorks brought our signal processing knowledge to bear and came up with the following three-step AutomL. First, you apply wavelet scattering. These wavelets are very suitable in their time-bounded shape to represent spikes and irregularities in your signal. Therefore, you get very good features.

2. Automated Feature Selection and Model Optimization

Short description:

Many engineering applications require deployment to memory and power-limited embedded systems. We apply automated feature selection to reduce the wavelet features and model size. AutoML empowers engineers to build optimized models without expertise. AutoML can be applied to signal applications with automated feature generation, selection, and model tuning. MATLAB can translate to low-level code for deployment. Join the longer session on automatic interpretability and the hands-on workshop on machine and deep learning using MATLAB online.

Many engineering applications, however, require deployment to memory and power-limited embedded systems. For those, you cannot deploy large models. So, second, we apply automated feature selection to reduce the maybe hundreds of wavelet features to just a few very performant features and reduce the model size. Finally, and key is the model selection hyperparameter tuning step. You have a choice of different models, and for the model to perform well, the hyperparameters need to be set just right. Let's look at that stage in a little more detail.

How does that simultaneous optimization of model and hyperparameters work? Well, you can perform random search, but that's not efficient either because the search space is very large. We employ Bayesian optimization that builds a model of the search space. And here you can see how that Bayesian optimization switches between different types of models and optimizes the error over in the course of the iterations.

How do we know that AutoML works? We compared AutoML to the traditional manual process on two classification problems. First, we looked at human activity recognition, where you take auxiliary meter data from mobile phones. We have about 7K observations in the set we collected and we manually engineered 66 features using various signal processing functions. Second, we looked at heart sound classification. Think about being in your doctor's office with a stethoscope and listening to your heart sound. So those phonograms, we could have a set of 10K observations that's publicly available and engineered less than 30 features.

So what results did we get? You can see here with the manual process, we achieved accuracies in the high 90s as you would want to have for such an important application. For AutoML, for one application slightly lower, but the point is, without all that expertise and time-consuming iterative process, you get very good models in a few steps. So AutoML empowers engineers without AI expertise to build optimized models, including for signal applications where the feature extraction is notoriously difficult. We can apply AutoML to signal applications in a few steps. Automated feature generation with wavelets. Automated feature selection to reduce model size and make it fit on your hardware. And model selection along with hyperparameter tuning in an efficient way using Bayesian optimization. Finally, to deploy your AI model to the edge and embedded systems, you need low-level code like C. MATLAB, you can translate automatically to C, C++ code that can be deployed directly and thus another barrier to broader adoption of AI removed.

Thank you for your attention and if you want to know more, Monday afternoon or evening, I'll have a longer session on automatic interpretability, a seminar on those two topics one hour and two hours a hands-on workshop on machine and deep learning using MATLAB online.

Check out more articles and videos

We constantly think of articles and videos that might spark Git people interest / skill us up or help building a stellar career

JSNation 2023JSNation 2023
24 min
AI and Web Development: Hype or Reality
In this talk, we'll take a look at the growing intersection of AI and web development. There's a lot of buzz around the potential uses of AI in writing, understanding, and debugging code, and integrating it into our applications is becoming easier and more affordable. But there are also questions about the future of AI in app development, and whether it will make us more productive or take our jobs.
There's a lot of excitement, skepticism, and concern about the rise of AI in web development. We'll explore the real potential for AI in creating new web development frameworks, and separate fact from fiction.
So if you're interested in the future of web development and the role of AI in it, this talk is for you. Oh, and this talk abstract was written by AI after I gave it several of my unstructured thoughts.
6 min
Charlie Gerard's Career Advice: Be intentional about how you spend your time and effort
Featured Article
When it comes to career, Charlie has one trick: to focus. But that doesn’t mean that you shouldn’t try different things — currently a senior front-end developer at Netlify, she is also a sought-after speaker, mentor, and a machine learning trailblazer of the JavaScript universe. "Experiment with things, but build expertise in a specific area," she advises.

What led you to software engineering?My background is in digital marketing, so I started my career as a project manager in advertising agencies. After a couple of years of doing that, I realized that I wasn't learning and growing as much as I wanted to. I was interested in learning more about building websites, so I quit my job and signed up for an intensive coding boot camp called General Assembly. I absolutely loved it and started my career in tech from there.
 What is the most impactful thing you ever did to boost your career?I think it might be public speaking. Going on stage to share knowledge about things I learned while building my side projects gave me the opportunity to meet a lot of people in the industry, learn a ton from watching other people's talks and, for lack of better words, build a personal brand.
 What would be your three tips for engineers to level up their career?Practice your communication skills. I can't stress enough how important it is to be able to explain things in a way anyone can understand, but also communicate in a way that's inclusive and creates an environment where team members feel safe and welcome to contribute ideas, ask questions, and give feedback. In addition, build some expertise in a specific area. I'm a huge fan of learning and experimenting with lots of technologies but as you grow in your career, there comes a time where you need to pick an area to focus on to build more profound knowledge. This could be in a specific language like JavaScript or Python or in a practice like accessibility or web performance. It doesn't mean you shouldn't keep in touch with anything else that's going on in the industry, but it means that you focus on an area you want to have more expertise in. If you could be the "go-to" person for something, what would you want it to be? 
 And lastly, be intentional about how you spend your time and effort. Saying yes to everything isn't always helpful if it doesn't serve your goals. No matter the job, there are always projects and tasks that will help you reach your goals and some that won't. If you can, try to focus on the tasks that will grow the skills you want to grow or help you get the next job you'd like to have.
 What are you working on right now?Recently I've taken a pretty big break from side projects, but the next one I'd like to work on is a prototype of a tool that would allow hands-free coding using gaze detection. 
 Do you have some rituals that keep you focused and goal-oriented?Usually, when I come up with a side project idea I'm really excited about, that excitement is enough to keep me motivated. That's why I tend to avoid spending time on things I'm not genuinely interested in. Otherwise, breaking down projects into smaller chunks allows me to fit them better in my schedule. I make sure to take enough breaks, so I maintain a certain level of energy and motivation to finish what I have in mind.
 You wrote a book called Practical Machine Learning in JavaScript. What got you so excited about the connection between JavaScript and ML?The release of TensorFlow.js opened up the world of ML to frontend devs, and this is what really got me excited. I had machine learning on my list of things I wanted to learn for a few years, but I didn't start looking into it before because I knew I'd have to learn another language as well, like Python, for example. As soon as I realized it was now available in JS, that removed a big barrier and made it a lot more approachable. Considering that you can use JavaScript to build lots of different applications, including augmented reality, virtual reality, and IoT, and combine them with machine learning as well as some fun web APIs felt super exciting to me.

Where do you see the fields going together in the future, near or far? I'd love to see more AI-powered web applications in the future, especially as machine learning models get smaller and more performant. However, it seems like the adoption of ML in JS is still rather low. Considering the amount of content we post online, there could be great opportunities to build tools that assist you in writing blog posts or that can automatically edit podcasts and videos. There are lots of tasks we do that feel cumbersome that could be made a bit easier with the help of machine learning.
 You are a frequent conference speaker. You have your own blog and even a newsletter. What made you start with content creation?I realized that I love learning new things because I love teaching. I think that if I kept what I know to myself, it would be pretty boring. If I'm excited about something, I want to share the knowledge I gained, and I'd like other people to feel the same excitement I feel. That's definitely what motivated me to start creating content.
 How has content affected your career?I don't track any metrics on my blog or likes and follows on Twitter, so I don't know what created different opportunities. Creating content to share something you built improves the chances of people stumbling upon it and learning more about you and what you like to do, but this is not something that's guaranteed. I think over time, I accumulated enough projects, blog posts, and conference talks that some conferences now invite me, so I don't always apply anymore. I sometimes get invited on podcasts and asked if I want to create video content and things like that. Having a backlog of content helps people better understand who you are and quickly decide if you're the right person for an opportunity.What pieces of your work are you most proud of?It is probably that I've managed to develop a mindset where I set myself hard challenges on my side project, and I'm not scared to fail and push the boundaries of what I think is possible. I don't prefer a particular project, it's more around the creative thinking I've developed over the years that I believe has become a big strength of mine.***Follow Charlie on Twitter
React Summit US 2023React Summit US 2023
30 min
The Rise of the AI Engineer
We are observing a once in a generation “shift right” of applied AI, fueled by the emergent capabilities and open source/API availability of Foundation Models. A wide range of AI tasks that used to take 5 years and a research team to accomplish in 2013, now just require API docs and a spare afternoon in 2023. Emergent capabilities are creating an emerging title: to wield them, we'll have to go beyond the Prompt Engineer and write *software*. Let's explore the wide array of new opportunities in the age of Software 3.0!
ML conf EU 2020ML conf EU 2020
41 min
TensorFlow.js 101: ML in the Browser and Beyond
Discover how to embrace machine learning in JavaScript using TensorFlow.js in the browser and beyond in this speedy talk. Get inspired through a whole bunch of creative prototypes that push the boundaries of what is possible in the modern web browser (things have come a long way) and then take your own first steps with machine learning in minutes. By the end of the talk everyone will understand how to recognize an object of their choice which could then be used in any creative way you can imagine. Familiarity with JavaScript is assumed, but no background in machine learning is required. Come take your first steps with TensorFlow.js!
JS GameDev Summit 2023JS GameDev Summit 2023
37 min
Building the AI for Athena Crisis
This talk will dive into how to build an AI for a turn based strategy game from scratch. When I started building Athena Crisis, I had no idea how to build an AI. All the available resources were too complex or confusing, so I just started building it based on how I would play the game. If you would like to learn how to build an AI, check out this talk!

Workshops on related topic

DevOps.js Conf 2024DevOps.js Conf 2024
163 min
AI on Demand: Serverless AI
Featured WorkshopFree
In this workshop, we discuss the merits of serverless architecture and how it can be applied to the AI space. We'll explore options around building serverless RAG applications for a more lambda-esque approach to AI. Next, we'll get hands on and build a sample CRUD app that allows you to store information and query it using an LLM with Workers AI, Vectorize, D1, and Cloudflare Workers.
React Advanced Conference 2023React Advanced Conference 2023
98 min
Working With OpenAI and Prompt Engineering for React Developers
In this workshop we'll take a tour of applied AI from the perspective of front end developers, zooming in on the emerging best practices when it comes to working with LLMs to build great products. This workshop is based on learnings from working with the OpenAI API from its debut last November to build out a working MVP which became PowerModeAI (A customer facing ideation and slide creation tool).
In the workshop they'll be a mix of presentation and hands on exercises to cover topics including:
- GPT fundamentals- Pitfalls of LLMs- Prompt engineering best practices and techniques- Using the playground effectively- Installing and configuring the OpenAI SDK- Approaches to working with the API and prompt management- Implementing the API to build an AI powered customer facing application- Fine tuning and embeddings- Emerging best practice on LLMOps
JSNation Live 2021JSNation Live 2021
81 min
Intro to AI for JavaScript Developers with Tensorflow.js
Have you wanted to explore AI, but didn't want to learn Python to do it? Tensorflow.js lets you use AI and deep learning in javascript – no python required!
We'll take a look at the different tasks AI can help solve, and how to use Tensorflow.js to solve them. You don't need to know any AI to get started - we'll start with the basics, but we'll still be able to see some neat demos, because Tensorflow.js has a bunch of functionality and pre-built models that you can use on the server or in the browser.
After this workshop, you should be able to set up and run pre-built Tensorflow.js models, or begin to write and train your own models on your own data.
ML conf EU 2020ML conf EU 2020
160 min
Hands on with TensorFlow.js
Come check out our workshop which will walk you through 3 common journeys when using TensorFlow.js. We will start with demonstrating how to use one of our pre-made models - super easy to use JS classes to get you working with ML fast. We will then look into how to retrain one of these models in minutes using in browser transfer learning via Teachable Machine and how that can be then used on your own custom website, and finally end with a hello world of writing your own model code from scratch to make a simple linear regression to predict fictional house prices based on their square footage.
ML conf EU 2020ML conf EU 2020
112 min
The Hitchhiker's Guide to the Machine Learning Engineering Galaxy
Are you a Software Engineer who got tasked to deploy a machine learning or deep learning model for the first time in your life? Are you wondering what steps to take and how AI-powered software is different from traditional software? Then it is the right workshop to attend.
The internet offers thousands of articles and free of charge courses, showing how it is easy to train and deploy a simple AI model. At the same time in reality it is difficult to integrate a real model into the current infrastructure, debug, test, deploy, and monitor it properly. In this workshop, I will guide you through this process sharing tips, tricks, and favorite open source tools that will make your life much easier. So, at the end of the workshop, you will know where to start your deployment journey, what tools to use, and what questions to ask.
ML conf EU 2020ML conf EU 2020
146 min
Introduction to Machine Learning on the Cloud
This workshop will be both a gentle introduction to Machine Learning, and a practical exercise of using the cloud to train simple and not-so-simple machine learning models. We will start with using Automatic ML to train the model to predict survival on Titanic, and then move to more complex machine learning tasks such as hyperparameter optimization and scheduling series of experiments on the compute cluster. Finally, I will show how Azure Machine Learning can be used to generate artificial paintings using Generative Adversarial Networks, and how to train language question-answering model on COVID papers to answer COVID-related questions.