As Indian companies move AI projects from pilot stage into full deployment, Salesforce executives outline what's changing for jobs, investment and the skills companies and workers will need next.

#June, a #startup backed by #MarcBenioff, aims to simplify #AIdeployment for #business|es. The company’s platform scans existing systems, optimises processes, and automates agent deployment, eliminating the need for extensive customisation and integration work. This approach allows businesses to leverage AI more efficiently and effectively. https://techcrunch.com/2026/08/03/a-marc-benioff-backed-startup-thinks-ai-can-solve-the-ai-deployment-problem/?eicker.news #tech #news #ainews

Tech leaders rapidly deploy agentic AI, boosting efficiency but highlighting security & data governance gaps. #AgenticAI #AIdeployment #AISecurity

Summa Health's experience with Sepsis Watch shows the difficulties in implementing AI in a community hospital.

My Linkedin

GoodVision AI outlines the AI Token Economy framework shaping inference infrastructure and future AI growth.

Unlock AI’s full potential by mastering orchestration-transform isolated pilots into scaled systems that boost efficiency by up to 40%, driving consistent, organization-wide innovation.

:::info This is the fourth article in our five-part series on agentic AI in the enterprise. In Part 3, we detailed the seven pillars of a solid AI agent architecture. Now we turn to how to use that architecture...

The journey from a groundbreaking machine learning model developed in a research lab to a live system that delivers tangible business value is often fraught with complexities. This crucial transition, known as ML Production Deployment, is where the true impact of artificial intelligence is realized. It's not merely about training a model; it's about building a resilient, scalable, and maintainable system that seamlessly integrates into existing operations.

In the rapidly evolving world of artificial intelligence, particularly in computer vision, deploying models with speed, efficiency, and minimal latency is paramount. While Python has become the go-to for rapid prototyping and development, when it comes to production-grade, high-performance C++ computer vision serving, the compiled power of C++ often emerges as the superior choice. This in-depth guide will walk you through the process of building a robust and efficient computer vision model serving solution using C++, leveraging key technologies like ONNX for model interoperability, OpenCV for image processing, Docker for streamlined dependency management, and Crow for building a lightweight REST API.