Systems | Development | Analytics | API | Testing

Machine Learning

Qlik AutoML Series - Predict, Explain, Act - Explainer

Predict, Explain, Act with Qlik AutoML, a powerful tool that brings automated machine learning to the hands of business users and data analysts. In this all new series, revist and learn how Qlik AutoML allows you to build predictive models without needing deep technical expertise in data science. Follow along in the next few videos linked in the description as Mike Tarallo walks you through the key features, from experiment to deployment, and see how Explain-ability is defined and used to gain insights that drive decision-making. Perfect for those looking to enhance their analytics with AI-powered predictions!

Launch Jobs & Setup Online Development Environments Directly from CLI

When it comes to managing AI projects, the Command Line Interface (CLI) can be a powerful tool. With ClearML, the CLI becomes an essential resource for creating job templates, launching remote for JupyterLab, VS Code, or SSH development environments, and executing code on a remote machine that can better meet resource needs. Specifically designed for AI workloads, ClearML’s CLI offers seamless control and efficiency, empowering users to maximize their AI efforts.

Why Multi-tenancy is Critical for Optimizing Compute Utilization of Large Organizations

As compute gets increasingly powerful, the fact of the matter is: most AI workloads do not require the entire capacity of a single GPU. Computing power required across the model development lifecycle looks like a normal bell curve – with some compute required for data processing and ingestion, maximum firepower for model training and fine-tuning, and stepped-down requirements for ongoing inference.

ClearML Announces AI Infrastructure Control Plane

We are excited to announce the launch of our AI Infrastructure Control Plane, designed as a universal operating system for AI infrastructure. With this launch, we make it easier for IT teams and DevOps to gain ultimate control over their AI Infrastructure, manage complex environments, maximize compute utilization, and deliver an optimized self-serve experience for their AI Builders.

How to Implement Gen AI in Highly Regulated Environments: Financial Services and Telecommunications and More

If 2023 was the year of gen experimentation, 2024 is the year of gen AI implementation. As companies embark on their implementation journey, they need to deal with a host of challenges, like performance, GPU efficiency and LLM risks. These challenges are exacerbated in highly-regulated industries, such as financial services and telecommunication, adding further implementation complexities. Below, we discuss these challenges and present some best practices and solutions to take into consideration.

10 Best APIs for Machine Learning

Machine learning APIs provide developers with powerful tools to integrate complex algorithms and models into applications without building them from scratch. These APIs simplify the development process by offering pre-trained models and standardized methods for different tasks. These include image recognition, natural language processing, and predictive analytics. This accessibility democratizes machine learning so that developers of varying expertise can leverage cutting-edge technology efficiently.

Building and Scaling Gen AI Applications with Simplicity, Performance and Risk Mitigation in Mind Using Iguazio and MongoDB

AI and generative Al can lead to major enterprise advancements and productivity gains. By offering new capabilities, they open up opportunities for enhancing customer engagement, content creation, virtual experts, process automation and optimization, and more.

Swift Machine Learning: Using Apple Core ML

A sub-discipline of artificial intelligence (AI), machine learning (ML) focuses on the development of algorithms to build systems capable of learning from, and making decisions based on, data. In iOS development, ML allows us to create applications that can identify patterns and make predictions, adapting a user’s experience by learning from their behaviour.