GitHub Copilot preview offers hope
Copilot technical preview doesn’t always generate good, correct, or even running code, but it’s still somewhat useful. Future versions could be real time-savers.
Copilot technical preview doesn’t always generate good, correct, or even running code, but it’s still somewhat useful. Future versions could be real time-savers.
Some day machine learning models may be more ‘glass box‘ than black box. Until then, explainability tools and techniques can help us understand how a black box model makes its decisions.
Enterprise data warehouses are comprehensive structured data stores designed for analysis. They often serve as the data sources for BI systems and machine learning.
A good low-code development platform can help developers build apps faster at lower cost. A no-code platform allows non-programmers to contribute to development.
Dataiku’s end-to-end machine learning platform combines visual tools, notebooks, and code to address the needs of data scientists, data engineers, business analysts, and AI consumers.
Menial tasks rob workers of time they could spend on more productive activities. Done right, RPA can banish bucketfuls of mindless chores.
Hosting CI/CD in the cloud can both speed up interactions between development pipelines and source code repositories and make life easier for developers.
Major upgrade to the ever-evolving Oracle Database brings JavaScript support, graph optimisations, in-memory enhancements, and dramatic improvements to JSON operations and in-database machine learning.
Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out.
From AWS Lambda and Azure Functions to Knative and OpenFaaS, we have at least a dozen functions-as-a-service platforms to choose from. Here’s how to navigate the options.
Amazon Web Services provides an impressively broad and deep set of machine learning and AI services, rivalling Google Cloud and Microsoft Azure.
From exploratory data analysis to automated machine learning, look to these techniques to get data science projects moving and to build better models.
While approaches and capabilities differ, all of these databases allow users to build machine learning models right where data resides.
Microsoft Azure combines a wide range of cognitive services and a solid platform for machine learning that supports automated ML, no-code/low-code ML, and Python-based notebooks.
Google Cloud AI and Machine Learning Platform is missing some pieces, and much is still in beta, but its scope and quality are second to none.