#3 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 1, Lesson 3]

One of the successes of deep learning has been speech recognition. Deep learning has made speech recognition much more accurate than, you know, maybe a decade ago. And this is allowing many of us to use speech recognition in our smart speakers, on our smartphones, for voice search and in other contexts. You may have heard occasionally about the research work that goes into building better speech models, but what else is needed to actually build a valuable production deployment speech recognition system? Let's use the machine learning project lifecycle to step through a speech recognition example so you can understand all the steps needed to actually build and deploy such a system. I've worked on speech recognition systems in a commercial context before and so the first step of that was scoping. Have to first define the project and just make a decision to work on speech recognition, say for voice search. As part of defining the project, I'd also encourage you to try to estimate or maybe at least guesstimate the key metrics. This will be very problem dependent. Almost every application will have its own unique set of goals and metrics. But in the case of speech recognition, some things I cared about were how accurate is the speech system? What's the latency? How long does the system take to transcribe speech speech and what is the throughput? How many queries per second could we handle? And then if possible, you might also try to estimate the resources needed. So how much time, how much compute, how much budget as well as timeline, how long would it take to carry out this project? I'll have a lot more to say on scoping in week three of this course, so we'll come back to this topic and describe this in greater detail as well. The next step is the data stage, where you have to define the data and establish a baseline and also label and organize the data. What's hard about this, one of the challenges of practical speech recognition systems is is the data labeled consistently. Here's an audio clip of a fairly typical recording you might get if you're working on speech recognition for voice search. Let me play this audio clip. Today's weather. And the question is, given this audio clip that you just heard today's weather, would you want to transcribe it like that? Which if you have transcriptionist label the data, this would be a perfectly reasonable transcription. Or would you want to transcribe it like that, which is also a completely reasonable transcription? Or should the transcriptionist say, well, there's often a lot of noise in audio, you know, maybe there's a sound of a clunk if something fell down and you don't want to transcribe noise. So maybe it's just noise and you should transcribe it like that. It turns out that any of these three ways of transcribing the audio is just fine. I would probably prefer either the first or the second, not the third. But what would hurt your learning algorithm's performance is if one third of the transcriptionist use the first, 1/3 the second, and 1/3 the third, the third way of transcribing. Because then your data is inconsistent and confusing for the learning algorithm. Because how is the learning algorithm supposed to guess which one of these conventions a specific transcriptionist happened to use for an audio clip? So spotting correcting consistencies like that, maybe just asking everyone to standardize on this first convention, that can have a significant impact on your learning algorithms performance. So we'll come back later in this course to dive into some best practices for how to spot inconsistencies and how to address them. Other examples of data definition questions for an audio clip like today's weather, how much silence do you want before and after each clip? After a speaker has stopped speaking, do you want to include another 100 milliseconds of silence after that? Or 300 milliseconds, or 500 milliseconds? That's half a second. Or how do you perform volume normalization? Some speakers speak loudly, some are less loud, and then there's actually a tricky case of if you have a single audio clip with some really loud volume and some really soft volume all within the same audio clip. So how do you perform volume normalization? Questions like all of these are data definition questions. A lot of progress in machine learning, that is a lot of machine learning research, was driven by researchers working to improve performance on benchmark datasets. In that model, researchers might download the dataset and just work on that fixed dataset. And this mindset has led to tremendous progress in machine learning. So. So no complaints at all about this mindset. But if you are working on a production system, then you don't have to keep the data set fixed. I often edit the training set or even edit the test set if that's what's needed in order to improve the data quality to get a production system to work better. So what are practical ways to do this effectively? Not in an ad hoc way, but systematic frameworks for making sure you have high quality data. You learn more about this later in this course and later in the specialization as well. After you've collected your data set, the next step is modeling, in which you have to select and train A model and perform error analysis. The three key inputs that go into training a machine learning model are the code, that is the algorithm, or the neural network model architecture that you might choose. You also have to pick hyperparameters and then there's the data. And running the code with your hyperparameters on your data gives you the machine learning model, the accelerated machine learning model for learning from say, audio clips to text transcripts. I found that in a lot of research work or academic work, you tend to hold the data fixed and vary the code and maybe vary the hyper parameters in order to try to get good performance. In contrast, I found that for a lot of product teams, if your main goal is to just build and deploy a working, valuable machine learning system, I found that it can be even more effective to hold the code fixed and to instead focus on optimizing the data and maybe the hyperparameters in order to get a high performing model. A machine learning system includes both code and data and also hyperparameters, but they're maybe a bit easier to optimize than the code or data. And I found that rather than taking a model centric view of trying to optimize the code to your fixed data set, for many problems, you can use an open source implementation of something that you download off GitHub and instead just focus on optimizing the data. So during modeling, you have to select and train some model architecture, maybe some neural network architecture. Error analysis can then tell you where your model still falls short. And if you can use that error analysis to to tell you how to systematically improve your data, maybe improve the code too, that's okay. But often if error analysis can tell you how to systematically improve the data, that can be a very efficient way for you to get to a high accuracy model. And part of the trick is you don't want to just feel like you need to collect more data all the time, because we could always use more data. But rather than just trying to collect more and more and more data, which is helpful, but can be expensive, if error analysis can help you be more targeted in exactly what data to collect, that can help you be much more efficient in building an accurate model. Finally, when you have trained a model and when error analysis seems to suggest it's working well enough, you're then ready to go into deployment. Check speech recognition. This is how you might deploy a speech system. You have a mobile phone. This would be an edge device with software running locally on your phone. That software taps into the microphone to record what someone is saying. Maybe for voice search. And in a typical implementation of speech recognition, you would use a VAD module. VAD stands for voice activity detection. And it's usually a relatively simple algorithm, maybe a learning algorithm. And the job of the VAD allows the smartphone to select out just the audio that contains, hopefully someone speaking, so that you can send only that audio clip to your prediction server. And in this case, maybe the prediction server lives in the cloud. This will be a common deployment pattern. The prediction server then returns both the transcript so the user, so you can see what the system thinks you said. And it also returns the search results if you're doing voice search. And the transcript and search results are then displayed in the front end code running on your mobile phone. So implementing this type of system would be the work needed to deploy a speech model in production. Even after it's running, though, you still have to monitor and maintain the system. So here's something that happened to me. Once my team had built a speech recognition system and it was trained mainly on adult voices, we pushed it to production, ran an introduction, and we found that over time, more and more young individuals, kind of teenagers, you know, sometimes even younger, seem to be using our speech recognition system. And the voices of very young individuals just sound different. And so my speech system's performance started to degrade. We just were not that good at recognizing speech as spoken by younger voices. And so we had to go back and find a way, you know, collect more data, other things in order to fix it. So one of the key challenges when it comes to deployment is concept drift or data drift, which is what happens when the data distribution changes, such as there are more and more young voices being fed to the speech recognition system. And knowing how to put in place appropriate monitors to spot such problems, and then also how to fix them in a timely way is a key skill needed to make sure your production deployment creates the value you hope it will. To recap, in this video, you saw the full lifecycle of a machine learning project using speech recognition as the running example. So from scoping to data to modeling to deployment. Next, I want to briefly share with you the major concepts and sequencing you learn about in this course. So come with me to the next video.