Search Results for: machine learning

Video Highlights: Building Machine Learning Apps with Hugging Face: LLMs to Diffusion Modeling

In this video presentation from our friends over at FourthBrain we have a timely presentation by Jeff Boudier, Product Director at Hugging Face, to discuss building machine learning apps with Hugging Face from LLMs to diffusion modeling.

Lightning AI Releases PyTorch Lightning 2.0 and a New Open Source Library for Lightweight Scaling of Machine Learning Models 

Lightning AI, the company accelerating the development of an AI-powered world, today announced the general availability of PyTorch Lightning 2.0, the company’s flagship open source AI framework used by more than 10,000 organizations to quickly and cost-efficiently train and scale machine learning models. The new release introduces a stable API, offers a host of powerful features with a smaller footprint, and is easier to read and debug.

Databricks Launches Simplified Real-Time Machine Learning for the Lakehouse

Databricks, the lakehouse company, announced the launch of Databricks Model Serving to provide simplified production machine learning (ML) natively within the Databricks Lakehouse Platform. Model Serving removes the complexity of building and maintaining complicated infrastructure for intelligent applications. Now, organizations can leverage the Databricks Lakehouse Platform to integrate real-time machine learning systems across their business, from personalized recommendations to customer service chatbots, without the need to configure and manage the underlying infrastructure.

Civo Announces Launch of New Machine Learning Managed Service

Civo, the cloud native service provider, has announced its new Machine Learning (ML) managed service, “Kubeflow as a Service” aimed at improving the developer experience and reducing the resources and time required to gain insights from ML algorithms.

Comet Announces Convergence 2023, the Leading Conference to Explore the New Frontiers of Machine Learning

Comet, provider of a leading MLOps platform for machine learning (ML) teams from startup to enterprise, announced its second annual Convergence conference. The event, which is free to the ML community, will take place virtually March 7-8, 2023.

New Study: Amid ChatGPT Craze, MLOps Spend Will Surge in 2023 as 60% of Machine Learning Leaders Plan to Boost Investments by Over 25%

ClearML, a leading open source, end-to-end MLOps platform, announced wide availability of its new, in-depth research report, MLOps in 2023: What Does the Future Hold? Polling 200 U.S.-based machine learning decision makers, the report examines key trends, opportunities, and challenges in machine learning and MLOps (machine learning operations).

DataStax Acquires Machine Learning Company Kaskada to Unlock Real-Time AI

DataStax, the real-time AI company, announced it has acquired Kaskada, a machine learning (ML) company that first solved managing, storing and accessing time-based data to train behavioral ML models and deliver the instant, actionable insights that fuel artificial intelligence (AI). Both DataStax and Kaskada have a track record of contributing to open source communities. Datastax will open source the core Kaskada technology initially, and it plans to offer a new machine learning cloud service later this year.

Acquiring New Customers Using AI, Machine Learning and Customer Analytics

In this special guest feature, William Skelly, CEO, Causeway Solutions, provides a list of some of the modern customer acquisition technologies and techniques. You’ve probably heard the often-repeated business adage, “It is six to seven times more expensive to acquire a new customer than it is to keep a current one.” However, with the latest advancements in AI, machine learning and customer analytics, acquiring new clients can be much more strategic, streamlined, and cost-effective.

eBook: The Machine Learning Infrastructure Blueprint

Our friends over at cnvrg.io have released a new eBook, “The Machine Learning Infrastructure Blueprint,” answering common questions most machine learning teams are asking about best practices for building a scalable machine learning infrastructure.

AutoML- The Future of Machine Learning

In this contributed article, Ankush Gupta and Kavya Shree of FischerJordan, explore the scope, use cases and challenges of AutoML and how data scientists and AutoML can have a future together. The authors discuss the causes driving the use of AutoML, the benefits and challenges associated, and major providers in the space. They conclude by analyzing the parts of the data science and ML process that can/cannot be automated and if AutoML will replace data scientists / both will go hand-in-hand.