As Machine Learning professional, navigating the diverse landscape of ML roles within the industry can be confusing. Job titles are usually not a big help because they change depending on the company and also depending on the organization within a company. Job titles tend to change over time as well, as we've seen in the rebranding of data analysts to data scientists. In order to navigate the job market and find potential roles for yourself, you therefore need to have a list of probing question. Let's dive a little bit deeper into each of these 3 probing questions, and why they should always be on your mind when surveying the ML job market.

All the lecture and lab material is free forever. To be among the first to hear about future iterations of the course, simply enter your email below, follow us on Twitter, or subscribe to our YouTube channel. We released lecture videos on Mondays at 6pm Pacific and lab videos on Wednesdays at 6pm Pacific on YouTube. We review some prerequisites -- the DNN architectures we'll be using and basic model training with PyTorch -- and introduce PyTorch Lightning. We review the purpose of the course and consider when it's a good (or bad!) idea to use ML.

No matter how robust an algorithm or machine learning model is, it's only ever as competent as the data used to train it. Because without data, algorithms wouldn't function, and models wouldn't be built. It's an interlinked and symbiotic process, where one aspect relies on the other to serve its greater purpose and meaning in the ML development workflow. Acquiring the data that you will feed into and power ML algorithms is the first essential step to creating, what will hopefully be, an optimally programmed model and a successful AI application that operates as it was intended once deployed. Essentially, the performance of AI systems and applications is influenced and even determined as early on as this most basic and initial effort.

In speaking with many machine learning teams, we've found that implementing a model registry has become a priority for AI-first organizations in solving visibility and governance concerns. A model registry is a centralized model store to collaboratively manage the full lifecycle of ML models. This includes model lineage and versioning, moving models between stages from development to staging to production, and model annotations and discovery (i.e., timestamps, descriptions, labels, etc.). ML teams implement a model registry solution to get centralized visibility and management of their models. But there are challenges to adopting a model registry, making it hard to build an up-to-date model registry that contains everything an organization needs.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. A new report by Scale AI uncovers what's working and what's not working with AI implementation, and the best practices for ML teams to move from just testing to real-world deployment. The report explores every stage of the ML lifecycle – from data collection and annotation to model development, deployment, and monitoring – in order to understand where AI innovation is being bottlenecked, where breakdowns occur, and what approaches are helping companies find success. The report's goal is to continue to shed light on the realities of what it takes to unlock the full potential of AI for every business and help empower organizations and ML practitioners to clear their current hurdles, learn and implement best practices, and ultimately use AI as a strategic advantage. For ML practitioners, data quality is one of the most important factors in their success, and according to respondents, it's also the most difficult challenge to overcome.

As the machine learning market catches up with the competition, the ML engineers would need tools that can evolve beyond catering to the basic needs of an ML team, to make it easier and faster to develop models and enable collaboration. Iterative develops open-source tools for developers to build and deploy models to specialized software that can speed up the training process. Analytics Insight has engaged in an exclusive interview with Dmitry Petrov, Co-founder, and CEO of Iterative. Iterative's mission is to deliver the best developer experience for machine learning teams by creating an ecosystem of open, modular ML tools. Our tools are Git-native to bridge the gap between software engineering and machine learning so that these two sides of the ML to production pipeline can happen collaboratively, efficiently, and reproducibly.

Beat is one of the most exciting companies to ever come out of the ride-hailing space. One city at a time, all across the globe we make transportation affordable, convenient, and safe for everyone. We also help hundreds of thousands of people earn extra income as drivers. Today we are the fastest-growing ride-hailing service in Latin America. But serving millions of rides every day pales in comparison to what lies ahead.

Beat is the fastest growing ride hailing app in Latin America and a part of the international FreeNow Group, the multi-service mobility joint venture backed by BMW Group and Daimler AG. One city at a time, we are on a mission to develop seamless mobility for a safe and sustainable urban life. We are proud to say we have launched Beat Tesla / Loonshot, the first and largest private all-electric vehicle service in Latin America. As an organization, we are committed to our drivers with ethical practices and a safe working environment. To our customers, we differentiate ourselves from other ride-hailing apps with our super user-friendly app and excellent customer service.

Despite widespread interest in machine learning (ML), relatively few projects leave the proof-of-concept phase and enter production. In fact, in a 2020 report, Capgemini found that roughly 85% of all ML projects grind to a halt across Capgemini's client organizations--despite successful preliminary models and ample support from executive leaders. Further, the study found, only half of the world's leading AI-powered enterprises successfully roll out artificial intelligence projects, including ML models, and this number drops substantially among organizations without dedicated ML teams. In recent years, AI solutions have attracted the interest of executive leadership across industries. Machine-learning models, perhaps the leading subset of AI, have particularly interested enterprises racing to digitize in the modern market because of their ability to automatically "learn" and update.

In a recently published article, Team topology for machine learning, I suggested that organizations in their Machine Learning (ML) journey should adopt a team topology consisting of four types of teams as illustrated in Figure 1. The team types are Stream-aligned ML, ML enabling, Data/Infrastructure Subsystem, and ML platform teams. To get an overview of these teams, please check out the article. In this article, we do a deep dive into ML platform teams. In particular, we explain the following points in more detail.