In this week’s real-time analytics news: A Microsoft-led $1 billion investment in Kenya seeks to broaden AI digital skills and spur local-language AI model development.
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Microsoft and G42 announced a comprehensive package of digital investments in Kenya as part of an initiative with the Republic of Kenya’s Ministry of Information, Communications, and the Digital Economy. In collaboration with Microsoft and others, G42 will lead the arrangement of an initial investment of $1 billion. One of the Kenyan investment priorities is a state-of-the-art green data center that will be built by G42 and its partners to run Microsoft Azure in a new East Africa Cloud Region.
The initiative will include four additional pillars that will be pursued with local partners:
- Local-language AI model development and research.
- An East Africa Innovation Lab coupled with broad AI digital skills training.
- International and local connectivity investments.
- Collaboration with the government of Kenya to support safe and secure cloud services across East Africa.
Additionally, G42 has begun work through its work in the United States to train an open-source large-language AI model in Swahili and English. To build on this and help accelerate advanced research in Kenya, Microsoft and G42 will increase their combined collaboration and support for local universities through the Microsoft Africa Research Institute, the Microsoft AI for Good Lab, the Mohammed Bin Zayed University of Artificial Intelligence in Abu Dhabi and select universities from Kenya and East Africa.
NVIDIA used this week’s COMPUTEX conference in Taipei to make a number of AI-related announcements. Some announcements were aimed at addressing workloads in supercomputing centers. But several others have appeal to business users. Items of interest include:
- The unveiling of the NVIDIA MGX server specification, which provides system manufacturers with a modular reference architecture to quickly and cost-effectively build more than 100 server variations to suit a wide range of AI and high performance computing. ASRock Rack, ASUS, GIGABYTE, Pegatron, QCT and Supermicro will adopt MGX, which can reduce development time by two-thirds to just six months.
- The announcement that the NVIDIA GH200 Grace Hopper Superchip is in full production, set to power systems coming online worldwide to run complex AI and HPC workloads. The GH200 Grace Hopper Superchip brings together the Arm-based NVIDIA Grace CPU and Hopper GPU architectures using NVIDIA NVLink-C2C interconnect technology.
Other real-time analytics news in brief
Aporia announced the launch of Aporia Guardrails for Multimodal AI Applications. The solution mitigates issues in video and audio-based AI applications, such as hallucinations, wrong responses, compliance violations, and jailbreak attempts. Specifically, the solution provides engineers with the ability to add a much-needed layer of security and control between the app and the user. These guardrails operate with a defined, fully customizable set of behavioral rules that work at sub-second latency. This fully managed, low-maintenance solution goes beyond what the common prompt engineering can do in just minutes of setup.
Dataiku announced the launch of its EU AI Act Readiness Program to help global organizations navigate the rapidly evolving landscape of AI regulations and drive Responsible AI innovation. With the recent signing of the EU AI Act and the U.S. Senate’s push for AI legislation, Dataiku’s offerings go beyond a simple compliance checklist. With Dataiku, organizations can stay ahead of the curve on existing and upcoming regulations worldwide, opening the doors to AI innovation without fear of non-compliance down the road.
DataRobot announced the integration of LLM evaluation measures that are aligned with a new initiative from the Singapore Government Agency, Infocomm Media Development Authority (IMDA). The “Project Moonshot” initiative offers new capabilities that help AI practitioners manage LLM deployment risks by providing a common framework for benchmarking and red teaming evaluation. The solution incorporates Project Moonshot’s testing toolkit and its benchmarking and evaluation tests. The result is that LLM evaluations are more accessible and help scale the responsible use of generative AI, enabling practitioners to turn on and configure guard models to change the behavior and responses of LLMs.
Dremio announced support for the Apache Iceberg REST Catalog Specification. The Iceberg REST Catalog Specification is the agreed-upon foundation for metadata accessibility across Iceberg catalogs. With this new capability, Dremio is able to seamlessly read from and write to any REST-compatible Iceberg catalog and provide customers with the open, flexible ecosystem needed for enterprise interoperability at scale.
Kong introduced several new Kong AI Gateway capabilities in Kong Gateway 3.7 and Kong Gateway Enterprise 3.7, including enterprise-only and OSS improvements. With the new Kong Gateway 3.7 release, the company is promoting Kong AI Gateway to GA status. AI developers can now focus on building AI-specific use cases — like LLM RAG chatbots or AI integrations — without having to build the underlying infrastructure to establish a secure and observable lifecycle for AI applications in production. Kong AI Gateway can also be provisioned entirely in the cloud as a dedicated SaaS service with Kong’s new Konnect Dedicated Cloud Gateways offering.
Lenovo and Cisco announced a global strategic partnership to deliver fully integrated infrastructure and networking solutions designed to accelerate digital transformation for businesses of all sizes. The two companies have signed a Memorandum of Understanding (MoU) agreement to jointly establish design, engineering, and execution plans for accelerating digital transformation with turnkey solutions and purpose-built AI infrastructure solutions from edge to cloud. The collaboration includes integration of the Cisco Nexus networking ecosystem into Lenovo’s edge-to-cloud portfolio to deliver the needed network performance, fortified security, and scalability.
MLCommons and AI Verify signed a memorandum of intent to collaborate on developing a set of common safety testing benchmarks for generative AI models for the betterment of AI safety globally. The aim of the AI Safety benchmark effort that this agreement advances is to provide AI developers, integrators, purchasers, and policy makers with a globally accepted baseline approach to safety testing for generative AI. To that end, the MLCommons AI Safety working group recently announced a v0.5 AI Safety benchmark proof of concept (POC). AI Verify will develop interoperable AI testing tools that will inform an inclusive v1.0 release, which is expected to be delivered this fall. In addition, they are building a toolkit for interactive testing to support benchmarking and red teaming.
Ockam teamed up with Redpanda to launch Redpanda Connect with Ockam, a zero-trust streaming data platform. This is a natural partnership because both companies have the same ethos: to enable every developer to build distributed systems at scale with simple tools. Redpanda Connect with Ockam empowers a single developer to easily create end-to-end encrypted streaming pipelines that can connect hundreds of applications across clouds and hybrid infrastructure. It builds trust in data streams, eliminates sources of risk to critical data, and removes the operational complexities inherent in managing keys at scale.
Sinequa announced the availability of Sinequa Assistants, new enterprise-grade generative AI assistants that seamlessly integrate with all enterprise content and applications to augment and transform knowledge work. Sinequa Assistants work with any public or private generative LLM, including Cohere, OpenAI, Google Gemini, Microsoft Azure Open AI, Mistral, and others, allowing companies to choose which LLMs best meet their needs while controlling costs. The Sinequa Assistant framework powers a range of ready-to-go Assistants along with tools to define custom Assistant workflows so that customers can use an Assistant out of the box or tailor and manage multiple Assistants from a single platform.
Tonic.ai announced the launch of a secure data lakehouse for LLMs, Tonic Textual, to enable AI developers to seamlessly and securely leverage unstructured data for retrieval-augmented generation (RAG) systems and large language model (LLM) fine-tuning. Tonic Textual is an all-in-one data platform designed to eliminate integration and privacy challenges ahead of RAG ingestion or LLM training. Leveraging its expertise in data management and realistic synthesis, Tonic.ai has developed a solution to tame and protect siloed, messy, and complex unstructured data into AI-ready formats ahead of embedding, fine-tuning, or vector database ingestion.
VAST Data announced a collaboration with Arista Networks to offer optimized AI infrastructure combining low latency, high-bandwidth, lossless ethernet switching with a performant, secure data platform that can seamlessly scale to meet the needs of modern enterprises and large GPU cloud service providers. The VAST Data Platform’s innovative Disaggregated Shared-Everything (DASE) architecture, paired with Arista Networks’ high-speed, low-latency networking, ensures enterprises maintain resilient data access for optimized GPU-accelerated computing.
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In case you missed it, here are our most recent previous weekly real-time analytics news roundups:
- Real-time Analytics News for the Week Ending May 25
- Real-time Analytics News for the Week Ending May 18
- Real-time Analytics News for the Week Ending May 11
- Real-time Analytics News for the Week Ending May 4
- Real-time Analytics News for the Week Ending April 27
- Real-time Analytics News for the Week Ending April 20
- Real-time Analytics News for the Week Ending April 13
- Real-time Analytics News for the Week Ending April 6
- Real-time Analytics News for the Week Ending March 30
