You saw from the last two videos that some AI products may require a large AI team, maybe you have a 100 engineers or sometimes many more than a 100 to build. What I would like to do in this video is share with you the typical roles and responsibilities of a large AI team like this so you can better understand the types of work needed to build these complex AI products. Now, even if you will be working in a much smaller team, maybe a one or two or five person team for the foreseeable future, I hope this video will still be useful to you because I hope it'll give you a sense of the different types of work that an AI team might need to do even if you end up executing on this type of work with a much smaller team. One caveat, because AI is evolving so fast, the job titles and various responsibilities are not yet a 100 percent defined, and they are a little bit different across different companies. So, your company may use their job titles differently than what I'm presenting here, but I want to share a view how job tittles are often used in many companies, so that if you're someday building up your own AI team, we hear about these roles you would have at least some deeper understanding of what these job titles mean. So, let's get started. Many AI teams will have Software Engineers in them. So, for example, for the smart speaker we needed to write specialized software to execute on the joke or to set a timer or to answer questions about today's weather. So, those are traditional software engineering tasks. Or you're building a self-driving car to make sure that your self-driving car software is reliable and doesn't crash. These are software engineering tasks. So, it's not uncommon for AI teams to have enlarged fractions sometimes 50 percent, sometimes much much much more than 50 percent of Software Engineers in them. The second common role is the Machine Learning Engineer. So, Machine Learning Engineer might write the software responsible for generating the A to B mapping or for building other machine learning algorithms needed for your product. So, they might gather the data of pictures of cars and positions of cars, train a neural network or train a deep learning algorithm and work iteratively to make sure that the learning algorithm is giving accurate outputs. Another role that you sometimes hear about is the Machine Learning Researcher. The typical row of the Machine Learning Researcher is to extend the state of the art in machine learning. Machine learning and AI more broadly are still advancing rapidly. So, many companies for profit and non-profit, more have Machine Learning Researchers responsible for extending the state-of-the-art. Some Machine Learning Researchers will publish papers, but many companies also have Machine Learning Researchers that do research, but are less focused on publishing. There's one other job title that's sort of in-between these two which is the Applied Machine Learning Scientists, which live somewhere between Machine Learning Engineer and Machine Learning Researcher. The Machine Learning Scientists kind of does a bit of both. They are often responsible for going to the academic literature or the research literature and finding the steady VR techniques and finding ways to adapt them to the problem they are facing such as how to take the most cutting edge, trigger where the wicker detection algorithm and adapt that to your smart speaker. Let's look at some more of those. Today, there are a lot of Data Scientists working in industries. The role of Data Scientist is not very well defined and the meaning is still evolving today. I think one of the primary responsibilities of Data Scientists is to examine data and provide insights, as well as to make presentations to teams or the executives in order to help drive business decision-making. There also Data Scientists today doing other tasks. So, there are also Data Scientists today whose work looks more like the Machine Learning Engineers, they are described on the previous slide. The meaning of this job title is still evolving today. With the rise of big data, there are also more and more Data Engineers whose main role is to help you organize your data, meaning, to make sure that your data is saved and is easily accessible, secure and cost-effective way. So, why is saving data such as a big deal? Can't you just save them in a hard disk and be done with it. In some companies, data volumes have gotten so big. There's actually quite a lot of work to manage them. To give you a sense of the scale, in computer science one MB stands for one megabytes and so a typical song on your music player like a typical MP3 file may be a few megabytes, say five megabytes would be a non unusual MP3 file size. A 1,000 megabytes is called a gigabyte. A million megabytes is called a terabyte and a billion megabytes is called a petabyte. With today's hard disk sizes, saving a few megabytes is not a big deal. It's just like a mere MP3 file, but storing a 1,000 megabytes, also called a gigabyte, starts to be a bit slower. A typical hour-long movie that you stream over the internet maybe above gigabytes. So, that's quite a lot of data. To give you a sense of the scale, a self-driving car may collect multiple gigabytes of information every single minute of operations. So, it's as if every minute the self-driving car generates enough data to store multiple hour-long movies. So, self-driving cars actually generate lot of data and saving the data for many days or weeks or months or years of operation starts to require serious data engineering. A terabyte is 1,000 times bigger than that and a petabyte is yet another 1,000 times bigger than that of that teams that were responsible for saving several petabytes of information per day, but other than pretty large internet companies is not that common for a company to generate multiple petabytes of information per day. As you move down this scale to bigger and bigger datasets, it becomes harder and harder to make sure that data is stored in a easy accessible, secure and cost-effective way, which is why Data Engineers become more and more important. Finally, you'll also hear some people referred to AI Product Managers whose job is to help decide what to build. In other words, they help to figure out what's feasible and valuable. Traditional Product Manager's job was already to decide what to build as well as sometimes some other roles, but the AI Product Manager now has to do this in the AI era and they're needing new skill sets to figure out what's feasible and valuable in light of what AI can and cannot do today. Because the field of AI is still evolving, none of these job titles are completely nailed down in the stone and different companies will use these job titles somewhat differently. But I hope this gives you a sense of some of the different types of work needed to build very complex AI products as well as where some of the job titles are evolving. To finish though, I want to re-emphasize that you can get started with a small team. You don't need a 100 people to do most AI projects. So, whether you just have one Software Engineer working with you, or just a single Machine Learning Engineer, or just a single Data Scientists, or maybe nobody, but yourself, if either you or an engineer working with you has taken some online courses on machine learning or deep learning or data science, that's often enough for you by yourself or for you and an engineer to start looking at some smaller volumes of data, start drawing some conclusions or start trading some machine learning models to get going. So, even though I've tried to paint here a vision for what a large AI team might look like, even if you have only a small AI team, could be nobody by yourself, I would still encourage you to get started and start exploring what the projects you could do. In this video, you saw what an AI team might look like, but when you look at a bigger company, an AI team doesn't live in isolation. So, how does an AI team fit into a bigger company to help the whole company become good at AI? You might remember that in week one I briefly alluded to an AI transformation playbook, which is a roadmap for you to help a company, to help maybe a great company become great at AI. Now that you've learned what is AI, how to do AI projects and even what AI teams in companies and the competency AI projects and coms may look like, let's return to their AI transmission playbook and go much deeper into the individual steps of the playbook so that you can understand what it takes to help a company over maybe a small number of years become good at AI and hopefully become much more valuable and much more effective along the way. Let's go into the AI transmission playbook in the next video.