Technology Tales

Decisions in the Cloud from SAS

SAS hosts an ongoing webinar series aimed at data, analytics and IT professionals, focusing on AI, cloud computing and deploying analytical models for improved outcomes. Built around the latest release of SAS Viya, the series covers topics including future AI trends, driving business value and the integration of SAS Viya with Microsoft Azure. Featured speakers include futurist Bernard Marr, IDC Vice President of AI Research Ritu Jyoti and SAS Chief Technology Officer Bryan Harris, alongside a broader group of SAS product managers, advisors and marketing directors. The content is available on demand in short segments and is designed to serve a range of professionals, from data scientists and developers through to business analysts and executives.

SAS Viya

A cloud-native data and AI platform designed to streamline the development and deployment of analytical models, SAS Viya offers tools that enhance collaboration across teams, reduce cloud costs and improve productivity through features such as automated model governance, real-time data processing and integrated decision-making capabilities. It supports industries by enabling faster insights, reducing diagnostic and operational delays and improving accuracy in areas like fraud detection and customer management, with organisations reporting significant efficiency gains and measurable business outcomes. The platform is positioned as a trusted solution by numerous industry leaders, with a focus on transparency, scalability and compliance, and has been recognised for its comprehensive approach to data science and machine learning over multiple years.

Microsoft Azure and SAS

The collaboration between SAS and Microsoft Azure combines advanced analytics, artificial intelligence and cloud infrastructure to deliver scalable, secure solutions for enterprises. This partnership enables organisations to leverage deep integrations, intuitive tools for data analysis and robust security measures, supporting applications across industries such as healthcare, banking and manufacturing.

Case studies highlight its use in improving patient outcomes, reducing recidivism and optimising logistics, while the platform's flexibility allows for migration of existing workloads and tailored deployment models. By utilising Azure's infrastructure, SAS provides tools that enhance decision-making, streamline operations and adapt to varying organisational needs through cloud-based analytics and AI capabilities.

The Quartz Guide to Bad Data

The Quartz guide to bad data is an extensive resource that helps journalists and data users recognise and address frequent issues found in real-world datasets. It details a wide variety of common data problems, including missing or duplicated values, inconsistent spellings, ambiguous fields, problematic categorisations and undocumented origins.

The guide categorises issues according to whom is best placed to resolve them, whether that is the user, the data provider, an external expert or a programmer. It also offers guidance for dealing with challenges such as human data entry errors, non-random or biased samples, unclear margins of error, manual editing, inflation, seasonal variations and manipulation of timeframes or reference points.

More complex problems, including those involving untrustworthy sources, opaque collection methods, unrealistic precision, outliers, misleading indices, statistical manipulation or poorly aggregated data, may require the input of specialists or programmers. Throughout, the guide emphasises a careful, questioning approach to data to help prevent mistakes and ensure more reliable analysis and reporting.

Elluminate Clinical Data Cloud from eClinical Solutions is a cloud-based platform that integrates various data streams, standardises complex information and provides analytics capabilities to support decision-making throughout the clinical research process. It consolidates clinical and operational data into a single repository, eliminating traditional data silos and facilitating cross-functional collaboration.

With built-in automation and study-agnostic machine learning, the platform supports AI integration and optimises data flow from initial acquisition through to regulatory submission. It also includes tools such as the Elluminate Mapper, which allows non-technical users to perform the intricate data transformations required for regulatory compliance.

A blog post from Dataiku published in November 2024 evaluates the performance of ChatGPT two years after its release, comparing its responses to those of AI professionals surveyed in May 2024. The survey involved 400 senior AI professionals from globally recognised companies, with a focus on AI deployment trends.

The analysis revealed that large organisations typically adopt a Hub and Spoke or Centralised Centre of Excellence model for AI initiatives, with most achieving a return of between $2 and $5 for each $1 spent on AI and data science. Access to quality data and a shortage of data talent were identified as the key barriers hindering AI value.

ChatGPT was tested against five questions presented to these AI leaders, achieving a score of 3.15 out of 5. This result demonstrates a close alignment with the survey findings, highlighting its potential as a useful tool for understanding industry trends, though the post notes some nuances it may miss.

Introduction to Meta AI's LLaMA

Meta AI's LLaMA models represent a significant advancement in open-source artificial intelligence, offering a range of foundation models that demonstrate competitive performance against leading closed-source systems. Trained on extensive publicly available data, they achieve state-of-the-art results with minimal computational resources and support multilingual capabilities, though performance on non-English languages may be comparatively lower due to the predominance of English text in their training data.

Whilst LLaMA models excel in general tasks and instruction-following, they face limitations in mathematical reasoning and domain-specific knowledge. Researchers are actively addressing these shortcomings through fine-tuning and other techniques. The models are primarily intended for research purposes under non-commercial licences, with a focus on evaluating and mitigating risks such as biases, hallucinations and the generation of harmful content.

Subsequent iterations such as LLaMA 2 and 3 have introduced improvements in context length and applicability, though challenges remain in ensuring robustness across diverse use cases. The release of these models has spurred innovation in the open-source community, fostering collaboration to enhance their reliability and expand their potential applications in fields such as data science, natural language processing and beyond.

Build and Deploy RAG-as-a-Service

Unwind AI walks developers through building a production-ready retrieval-augmented generation service using Claude 3.5 Sonnet and Ragie.ai, achievable in fewer than 50 lines of Python code. Unlike conventional RAG applications, a managed service approach abstracts the more complex elements of data ingestion, chunking and vector retrieval through APIs, reducing infrastructure overhead and allowing developers to focus on building features.

Ragie.ai handles the full pipeline, from document chunking to hybrid keyword and semantic searches, and offers connectors for services such as Google Drive, Notion and Confluence. The tutorial guides readers through setting up a development environment, building a RAGPipeline class that manages authentication and API endpoints, and creating a Streamlit interface through which users can upload documents via URL, select a processing mode and submit queries.

Queries are answered using information retrieved from the uploaded material, with relevant sections passed to Claude 3.5 Sonnet, which synthesises a response. The whole application can be launched locally using a single terminal command.

IBM Granite 3.0: Open, State-of-the-Art Enterprise Models

The release of IBM's Granite 3.0 models introduces a range of advancements in artificial intelligence, focusing on efficiency, safety and scalability. The range includes mixture of experts (MoE) variants, such as the 3B-A800M and 1B-A400M, which balance performance with low-latency inference, making them suitable for both on-device and server applications.

A speculative decoding technique applied to the 8B Instruct model achieves a 220% increase in tokens per step, enhancing inference speed without compromising accuracy. The suite also features Granite Guardian models, designed to detect and mitigate risks such as hallucinations, bias and harmful content, which outperform existing solutions in benchmark tests.

The models are available through platforms such as Hugging Face, Ollama and IBM watsonx.ai, and support diverse use cases ranging from agentic workflows to retrieval-augmented generation. To assist developers in deploying and optimising these tools for enterprise applications, IBM provides resources including tutorials, quantisation guides and integration frameworks.

Using Llama 3.2 Locally

Meta's Llama 3.2 models are available in two main variants, lightweight models suited to multilingual generation and tool calling, and vision models capable of image reasoning by processing images alongside prompts. Both can be run locally using Msty, a free desktop chatbot application that supports open-source models downloaded directly to a user's machine as well as remote models accessed via API keys.

To run the lightweight Llama 3.2 3B Instruct model locally, users download it from Hugging Face through Msty's model management interface in GGUF format, after which it can be used without an internet connection for tasks such as code generation and debugging. The vision variant, which currently lacks a GGUF release, is instead accessed remotely through the Groq API by creating a GroqCloud account, generating an API key and configuring it within Msty's remote provider settings, allowing users to submit images with prompts and receive detailed descriptive responses at considerable speed.