Topic: ml

SD Times news digest: GrammaTech acquires JuliaSoft, Snyk announces prioritization capabilities, and TigerGraph makes updates to GSQL

Software assurance and cybersecurity company GrammaTech announced it will be acquiring code analysis company JuliaSoft. According to GrammaTech, the acquisition will help it expand the reach of the CodeSonar SAST platform to Java and C#. The new language support extends the automated detection of software vulnerabilities to enterprise use cases where safety and security are … continue reading

SD Times news digest: Planview acquires AI/ML company Aptage, AWS IoT SiteWise, and Docker teams up with AWS on bringing apps to the cloud

Planview announced the acquisition of Aptage, which focuses on the application of AI/ML to portfolio management and work management. “For Planview, the acquisition of Aptage brings data science and domain expertise in AI/ML. Not only will that accelerate the company’s current roadmap in AI/ML – a growing area of focus for the market and our … continue reading

Google to replace TensorFlow’s runtime with TFRT

Google has announced a new TensorFlow runtime designed to make it easier to build and deploy machine learning models across many different devices.  The company explained that ML ecosystems are vastly different than they were 4 or 5 years ago. Today, innovation in ML has led to more complex models and deployment scenarios that require … continue reading

White House announces call to action for the tech community to contribute to COVID-19 dataset

The White House is issuing a call to action for AI experts to develop new text and data mining techniques to analyze the newly released COVID-19 Open Research Dataset (CORD-19). The dataset is the most extensive machine-readable Coronavirus literature collection available was created with input from researchers and leaders from the Allen Institute for AI, … continue reading

8 top open-source community and data tools

As organizations wake up to the multitude of ways advanced technologies can augment their businesses, developers with relevant skills are becoming ever more valuable. Data is the key to a whole kingdom of opportunity, and when combined with AI and machine learning tools, the bounds of this kingdom are practically limitless.  Even for those without … continue reading

Google introduces Cloud AI Platform Pipelines

Google launched the beta of Cloud AI Platform Pipelines, which combines repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. It aims to deliver an enterprise-ready, easy to install, secure execution environment for ML workflows. AI Platform Pipelines were created because machine learning workflows can involve many steps with dependencies on each … continue reading

premium Software 3.0: Enterprise AI systems and the brave new economy

Machine learning (ML) and other artificial intelligence (AI) technologies are powerful tools with the potential to transform a wide range of processes for both consumers and companies.  Though many of these technologies are still commercially nascent, a number of startups have emerged that provide ML-based software solutions to enterprises. We believe that these “enterprise AI … continue reading

SD Times news digest: Parasoft C/C++test, Datical updates Liquibase, and Syncsort and Databricks partner on mainframe data

Parasoft introduced the new release of its C/C++test solution this week at Embedded World. The solution is a unified C and C++ development testing solution for real-time safety- and security- critical embedded applications and enterprise IT. “With the new release of C/C++test, we are bringing unique AI and ML capabilities to help organizations with the … continue reading

SD Times Open-Source Project of the Week: Manifold

Manifold is a visual debugging tool for machine learning developed by Uber. Machine learning is widely used across the Uber platform to support decision making and forecasting for features such as ETA prediction and fraud detection, the company explained. The tool aims to help engineers and scientists identify performance issues across ML data slices and … continue reading

Continuous Deployment for ML: The new software development life cycle

The new software development life cycle means working out ways to adapt the SDLC for your machine learning workflow and teams. With data scientists currently spending large chunks of their time on infrastructure and process instead of building models, finding ways to enable the SDLC to work effectively with machine learning is critical for not … continue reading

Implications and practical applications for AI and ML in embedded systems

“Civilization advances by extending the number of important operations we can perform without thinking about them.” —Alfred North Whitehead, British mathematician, 1919 Hailed as a truly transformational technology, artificial intelligence (AI) is positioned to disrupt businesses either by enabling new approaches to solving complex problems, or threatening the status quo for whole business sectors or … continue reading

Analyst Watch: Evaluating the ethics of software

In recent years, technology analysts have devoted much attention to the topic of developers and how the demographics of developers are changing. For starters, the International Data Corporation (IDC) has noted the growth of developer populations in China, India, Brazil, Russia, Indonesia and Turkey, as well as select countries in East Africa. In addition, IDC … continue reading

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