Category Added in a WPeMatico Campaign
Identifying, collecting, and transforming data is the foundation for machine learning (ML). According to a Forbes survey, there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model. In addition, many of our customers face several challenges during the model operationalization phase …
Amazon SageMaker Autopilot automatically builds, trains, and tunes the best custom machine learning (ML) models based on your data. It’s an automated machine learning (AutoML) solution that eliminates the heavy lifting of handwritten ML models that requires ML expertise. Data scientists need to only provide a tabular dataset and select the target column to predict, …
Tech companies are ramping up the search for highly skilled data analytics, AI and ML professionals, with the race to AI accelerating the crunch.1 They are looking for cloud experts who can successfully build, test, run, and manage complex tools and infrastructure, in roles such as data analysts, data engineers, data scientists, and ML engineers. …
Read more “Discover why leaders need to upskill teams in ML, AI and data”
Skycatch, a San Francisco-based startup, has been helping companies mine both data and minerals for nearly a decade. The software-maker is now digging into the creation of digital twins, with an initial focus on the mining and construction industry, using the NVIDIA Omniverse platform for connecting and building custom 3D pipelines. SkyVerse, which is a …
Read more “Unearthing Data: Vision AI Startup Digs Into Digital Twins for Mining and Construction”
Editor’s note: This is the first in a series of blogs on researchers advancing science in the expanding universe of high performance computing. A perpetual shower of random raindrops falls inside a three-foot metal ring Dale Durran erected outside his front door (shown above). It’s a symbol of his passion for finding order in the …
Read more “Stormy Weather? Scientist Sharpens Forecasts With AI”
Editor’s note: This is the third post in the Palantir RFx Blog Series, which explores how organizations can better craft RFIs and RFPs to evaluate digital transformation software. Each post focuses on one key capability area within a data ecosystem, with the goal of helping companies ask the right questions to better assess technology. Introduction …
Machine learning (ML) has proven to be one of the most successful and widespread applications of technology, affecting a wide range of industries and impacting billions of users every day. With this rapid adoption of ML into every industry, companies are facing challenges in supporting low-latency predictions and with high availability while maximizing resource utilization …
Read more “Cost efficient ML inference with multi-framework models on Amazon SageMaker “
Amazon SageMaker JumpStart provides pre-trained, open-source models for a wide range of problem types to help you get started with machine learning (ML). JumpStart also provides solution templates that set up infrastructure for common use cases, and executable example notebooks for ML with Amazon SageMaker. As a business user, you get to do the following …
In the pursuit of superior accuracy, deep learning models in areas such as natural language processing and computer vision have significantly grown in size in the past few years, frequently counted in tens to hundreds of billions of parameters. Training these gigantic models is challenging and requires complex distribution strategies. Data scientists and machine learning …
Business Problem UKG Ready primarily operates in the Small and Medium Business (SMB) space, so inherently many customers are forced to operate and make key business decisions with less Workforce Management (WFM) / Human Capital Management (HCM) data. In addition to volume, SMB lacks the variety of data needed to create a dynamic and agile organization. …