Liberty Mutual is one of the most experienced and advanced cloud adopters in the nation. And that is in no small part thanks to the vision of James McGlennon, who in his role as CIO of Liberty Mutual for past 17 years has led the charge to the cloud, analytics, and AI with a budget north of $2 billion. Eight years ago, McGlennon hosted an off-site think tank with his staff and came up with a "technology manifesto document" that defined in those early days the importance of exploiting cloud-based services, becoming more agile, and instituting cultural changes to drive the company's digital transformation. Today, Liberty Mutual, which has 45,000 employees across 29 countries, has a robust hybrid cloud infrastructure built primarily on Amazon Web Services but with specific uses of Microsoft Azure and, lesser so, Google Cloud Platform. Liberty Mutual's cloud infrastructure runs an array of business applications and analytics dashboards that yield real-time insights and predictions, as well as machine learning models that streamline claims processing.
A recommender system is an important component of Internet services today: billion dollar revenue businesses are directly driven by recommendation services at big tech companies. The current landscape of production recommender systems is dominated by deep learning based approaches, where an embedding layer is first adopted to map extremely large-scale ID type features to fixed-length embedding vectors; then the embeddings are leveraged by complicated neural network architectures to generate recommendations. The continuing advancement of recommender models is often driven by increasing model sizes--several models have been previously released with billion parameters up to even trillion very recently. Every jump in the model capacity has brought in significant improvement on quality. The era of 100 trillion parameters is just around the corner.
Google I/O 2022, the most awaited developers' conference of the year, is around the corner. With more than 200 speakers, the summit will cover a broad spectrum of topics and will have a slew of announcements on the latest innovations in AI and ML. The I/O adventure also makes a comeback this year: Users can explore the platform to see product demos, chat with Googlers, earn Google Developer profile badges and virtual swag, engage with the developer community, create an avatar, and look for easter eggs. Seek out your next Adventure at Google I/O 2022! The conference is scheduled to start at 10:30 pm IST on May 11, 2022, and will kick off with Alphabet CEO Sundar Pichai's keynote speech.
Eden AI simplifies the use and deployment of AI technologies by providing a unique API connected to the best AI engines. Companies are increasingly using Artificial Intelligence services, especially when it comes to automating internal processes or improving their customers' experience. The strong development of AI makes it a commodity. These functionalities can be used in several fields: health, human resources, tech, etc. The big players in the cloud market (Amazon Web Services, Microsoft Azure or Google Cloud) offer solutions that provide access to this type of service, but there are also smaller providers that are already competing with them: Mindee, Dataleon, Deepgram, AssemblyAI, Rev.AI, Speechmatics, Lettria, etc.
Managing data has always been a challenge for businesses. With new sources and higher volumes of data coming in all the time, it's more important than ever to have the right tools in place. Predictive analytics tools and software are the best way to accomplish this task. Data scientists and business leaders must be able to organize data and clean it to get the process started. The next step is analyzing it and sharing the results with colleagues.
Enterprises know they want to do machine learning, but they also know they can't afford to think too long or too hard about it. They need to act, and they have specific business problems that they want to solve. And they know instinctively and anecdotally from the experience of the hyperscalers and the HPC centers of the world that machine learning techniques can be utterly transformative in augmenting existing applications, replacing hand-coded applications, or creating whole new classes of applications that were not possible before. They also have to decide if they want to run their AI workloads on-premise or on any one of a number of clouds where a lot of the software for creating models and training them are available as a service. And let's acknowledge that a lot of those models were created by the public cloud giants for internal workloads long before they were peddled as a service.
Are you a data scientist or AI practitioner who wants to understand cloud platforms? Are you a data scientist or AI practitioner who has worked on Azure or AWS and curious to know how ML activities can be done on GCP? If yes, this course is for you. This course will help you to understand the concepts of the cloud. In the interest of the wider audience, this course is designed for both beginners and advanced AI practitioners.
Then, we will start by loading the dataset on the devices in IID, non-IID, and non-IID and unbalanced settings followed by a quick tutorial on PySyft to show you how to send and receive the models and the datasets between the clients and the server. This course will teach you Federated Learning (FL) by looking at the original papers' techniques and algorithms then implement them line by line. In particular, we will implement FedAvg, FedSGD, FedProx, and FedDANE. You will learn about Differential Privacy (DP) and how to add it to FL, then we will implement FedAvg using DP. In this course, you will learn how to implement FL techniques locally and on the cloud. For the cloud setting, we will use Google Cloud Platform to create and configure all the instances that we will use in our experiments. By the end of this course, you will be able to implement different FL techniques and even build your own optimizer and technique. You will be able to run your experiments locally and on the cloud.
Google Cloud is offering users access to an AI platform that allows them to build, deploy, and manage AI projects in the cloud without needing extensive data science knowledge. Isik said the platform has been created to bring the benefits of AI and machine learning to smaller organisations, for whom adopting AI can be a daunting challenge if you lack the skills and resources available to Fortune 500 businesses. "My team of data scientists saw a real need for software that could democratize machine learning innovation by removing these common barriers," he said in a statement. The platform also includes lifecycle management capabilities to monitor infrastructure utilization and model behavior. According to Prevision.io, the intuitive user interface and predictive analytics in its platform allow users to get set up in minutes and have models up and running in three to four weeks, as opposed to months for existing ways to build and deploy machine learning models.