In today's data-driven business landscape, the ability to predict trends, explain drivers, and act on insights is no longer a luxury—it's a necessity. Yet for many organizations, the path from data to predictive intelligence remains challenging. Data science resources are scarce, and traditional tools often require specialized expertise that most analytics teams lack. At Qlik, we believe that the power of predictive analytics should be accessible to all.

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.

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.

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.

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.

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.

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.