So far, you've seen the infrastructure, the software, and the customers who are already using GCP. But the most critical factor to the success of your future big data and ML projects, is your team itself. The people and the core skill sets required, will make or break your next innovation. A common mistake that companies make is that they go out and hire 10 PhD machine learning scientists, and expect magic to happen. I see this a lot with companies who are new to building data science and ML teams. They focus on the ML researchers, and forget about all the help and guidance that the ML researchers will need. The reality as my colleague Cassie has noted in a blog post, looks more like this. You need data engineers to build the pipelines and get you clean data. Decision makers, to decide how deep you want to invest in a data-driven opportunity while weighing the benefits for the organization. Analysts, to explore the data for insights and potential relationships that could be useful as features in a machine learning model. Statisticians, to help make your data-inspired decisions become true data-driven decisions, with their added rigor. Applied machine learning engineers, who have real-world experience building production machine learning models from the latest and best information and research by the researchers. Data scientists, who have the mastery over analysis, statistics, and machine learning. Analytics managers to lead the team. Social scientists and ethicists to ensure that the quantitative impact is there for your project and, it's the right thing to do. As I've written in a blog post on this subject, it's linked below, a single person might have a combination of these roles, but this depends on the size of your organization. Your team size is one of the biggest drivers in whether you should hire for a specific skill set, up-skill from within, or combine the two. Do you remember these big data challenges? Can you see how different roles would map to this? Within Google Cloud training, my team and I have thought about the different types of data science teams and roles that are using Google Cloud, so that we can best tailor our data in ML courses and labs. One of the core challenges we face, is how different types of users engage with our GCP big data and AI products. We'll be using a few personas in this course. Their backgrounds, goals, and challenges, might be similar to yours. Let's meet them now, and you'll see them again later. Brittany and Theo lead their data engineering team in managing their Hadoop cluster for the organization's data pipelines and compute jobs. Their organization was an early adopter of Hadoop for distributed computing back in 2007, and they've built up Hadoop jobs over time. Brittany and Theo's job is to actively ensure that the Hadoop jobs are all run and that the cluster is well maintained. They say, "Our CTO has challenged our data engineering team to find ways we can spend less on managing our on-prem cluster. Right now, we just want to show her options that don't require any code changes to our 100+ Hadoop jobs." Brittany and Theo are data engineers who manage a company's data platform, and are focused on reducing maintenance burden. Jacob is a data analyst who has a background in building and querying his company's MySQL transactional and reporting database. As the company grows, the reporting tables in his RDBMS are already starting to slow down. Users are reporting long query and dashboard loading times. He wants to find an easy path for scaling his company's data reporting and not have to manage another data system, as the data needs to grow. Jacob is a data analyst who wants to be able to derive insights from data and disseminate them with as little friction as possible. Rebecca is a data engineer, whose company specializes in harnessing data from the Internet of Things or IoT devices. She says, "I really want to design our data pipelines for the future. For us, that means lots and lots of streaming data from our IoT devices with low latency." Her team lead has asked her to come up with a plan to handle the expected 10X growth in streaming data volumes this year. She wants to future proof her team's pipelines, but doesn't want to spend hours manually scaling hardware up and down as streaming volume changes. Additionally, her business stakeholder team wants insights from all the IoT devices in the field on their dashboards, with minimal delay. Vishal says, "I pitched my team on the value machine learning can add, and I've got buy-in to build a prototype. But now I've got to build a prototype. What are some of the easiest ways I can see whether machine learning is feasible for my data?" Vishal is an applied machine learning engineer, who has a background in building machine learning models in TensorFlow and Keras. His team is growing rapidly, and he's often asked by his leadership, to assess the feasibility of machine learning for a wide variety of projects. He doesn't have the time to train and test all of the ideas with custom models. He wants to empower his data analysts and data engineering teams, by teaching them machine learning. Do these personas sound familiar to your role and your team? Next, we'll learn more about the Google Cloud Platform, big data and machine learning approaches and solutions, so that we can address each of these challenges.
