Computers have become adept at extracting patterns from very large collections of data. For example, shopping transactions can reveal consumers' preferences and message traffic on social networks can reveal political trends.
Leading the future of luxury mobility Lucid's mission is to inspire the adoption of sustainable energy by creating the most captivating luxury electric vehicles, centered around the human experience. Working at Lucid Motors means having a shared vision to power the future in revolutionary ways. Be part of a once-in-a-lifetime opportunity to transform the automotive industry. We are looking for a Hands-on Big Data Engineering Manager who is looking for a challenge, enjoys thinking big, and looking to make their mark on an extremely fast-growing company. If building large and building fast, working with a young and very talented team of engineers, and collaborating with the brightest mind in the Automotive industry is what you like, Lucid is the best to experience it.
Implement any project that requires PySpark knowledge from scratch. Know the theory and practical aspects of PySpark and AWS. People who are beginners and know absolutely nothing about PySpark and AWS. People who want to develop intelligent solutions. People who want to learn PySpark and AWS. People who love to learn the theoretical concepts first before implementing them using Python. People who want to learn PySpark along with its implementation in realistic projects.
Role Description: The Rackspace FinOps group enables cloud users to align their cloud technology adoption with their business strategies. We advise many of the world's largest AWS, GCP, Azure, and other Cloud consumers on topics ranging from cloud architecture to organizational governance to cloud economics, driving efficient cloud adoption and usage for our clients. The FinOps Data Engineer role is an exciting opportunity to build solutions that will help us and our clients turn complex multi-cloud cost and performance datasets into actionable insights. This is an opportunity to make an impact on a fast-growing team. You'll be instrumental in creating new and better analytics and ML solutions, and generally innovate to drive new value for our clients.
Team Description: Our computer vision team is a leader in the creation of cutting-edge algorithms and software for automated image and video analysis. Our solutions embrace deep learning and add measurable value to government agencies, commercial organizations, and academic institutions worldwide. We understand the difficulties in extracting, interpreting, and utilizing information across images, video, metadata, and text, and we recognize the need for robust, affordable solutions. We seek to advance the fields of computer vision and deep learning through research and development and through collaborative projects that build on our open source software platform, the Kitware Image and Video Exploitation and Retrieval (KWIVER) toolkit. About the projects:Kitware's employees have unique opportunities to interact and collaborate directly with customers, visit interesting customer sites, and participate in live field tests and demonstrations.
Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Have you ever found a new favourite series on Netflix, picked up groceries curbside at Walmart, or paid for something using Square? That's the power of data in motion in action--giving organisations instant access to the massive amounts of data that is constantly flowing throughout their business. Our cloud-native offering is designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organisation. With Confluent, organisations can create a central nervous system to innovate and win in a digital-first world.
AI budgets are up significantly over the past year as companies compete to survive and grow market share during the global pandemic, according to Appen, which published its State of AI and Machine Learning report this week. The study also detected a correlation between AI budget size and the likelihood that AI projects will actually be deployed on the one hand, and budgets and the use of external data providers on the other. Now in its seventh year, Appen's State of AI seeks to generate a broad snapshot of AI investments across the United States. The company contracted with Harris Poll to investigate various aspects of AI investments and project management at 500 companies, all of which had at least 100 employees. The growth in AI budgets was perhaps the most compelling result to come out of the study, which had a margin of error of 5%.
Systems driven by artificial intelligence (AI) and predictive analytics have the power to transform the customer experience (CX). We see this across all industry verticals and business processes, from retail to healthcare and from banking to media. AI-driven predictive models drive customer loyalty and increase revenue for any brand. AI has traditionally been used to predict customer buying patterns. It helps figure out what people will buy next or how much they are willing to pay for a product or service.
Artificial intelligence (AI) in cybersecurity was a popular topic at RSA's virtual conference this year, with good reason. Many tools rely on AI, using it for incident response, detecting spam and phishing and threat hunting. However, while AI security gets the session titles, digging deeper, it is clear that machine learning (ML) is really what makes it work. ML allows for "high-value predictions that can guide better decisions and smart actions in real-time without humans stepping in." Yet, for all ML can do to improve intelligence and help AI security do more, ML has its flaws.
Open source software is a critical resource in data science today, but integrating the various open source products together can be a complex task. This is what drove Red Hat to develop Open Data Hub, which brings over two dozen commonly used tools together into a single cohesive framework that simplifies access to AI and machine learning capabilities for data professionals. Open Data Hub (ODH) originated about five years ago as an internal Red Hat project to simply store large amounts of data so that it was accessible for data scientists to build models, according to Will McGrath, a senior principal product marketing manager at Red Hat. In Red Hat's case, the engineers chose Ceph, the S3-compatbile object storage system. After getting a handle on the storage aspect of the data, Red Hat's team then brought a handful of tools into the equation, starting with Jupyter, Apache Spark, and TensorFlow.
Network Analytics for Business Specialization Become the Best in Your Field with NBA. About this Specialization 1,628 recent views This Specialization is part of HSE University Master of Data and Network Analytics degree program. Learn more about admission into the program here and how your Coursera work can be leveraged if accepted into the program. The specialization is intended for a general audience of business analysts, seeking to augment their toolkit with the newest analytical methods. Specifically, they will get introduced to the analysis of networks and unstructured data (texts) – the two areas that are currently hailed as the "methods of the future."