Both extremely promising and extremely risky, generative AI has distinct failure modes that we need to defend against to protect our users and our code. We’ve all seen the news, where chatbots are encouraged to be insulting or racist, or large language models (LLMs) are exploited for malicious purposes, and where outputs are at best fanciful and at worst dangerous. None of this is particularly surprising. It’s possible to craft complex prompts that force undesired outputs, pushing the input window past the guidelines and guardrails we’re using. At the same time, we can see outputs that go bey ..read more

Amazon SageMaker JumpStart provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including image, text, and tabular. This post introduces using the text classification and fill-mask models available on Hugging Face in SageMaker JumpStart for text classification on a custom dataset. We also demonstrate pe ..read more

Red Hat has unveiled Podman AI Lab, an extension to the Podman Desktop graphical interface that lets developers build generative AI-powered applications in containers. Announced May 7, Podman AI Lab is intended to make it easier to develop with AI in a local environment. The Podman AI Lab extension supports the adoption of generative AI for building intelligent applications or enhancing their workflow using AI-augmented development capabilities, Red Had said.  To read this article in full, please click here InfoWorld Cloud ComputingRead More ..read more

In order to build an application that interacts with the Bitcoin blockchain, whether it be a wallet, an Ordinals marketplace, or a BTC exchange, you must be able to reliably access the Bitcoin network. For example, you will need to read critical data from the blockchain that acts as input for properly constructed Bitcoin transactions. The most foundational of these data needs is retrieving data pertaining to transaction outputs for a given wallet from the Bitcoin network, which serves a variety of use cases. For example, retrieving unspent transaction outputs (UTXOs) to be consumed as inputs ..read more

Amazon Aurora MySQL-Compatible Edition offers multiple authentication methods to securely authenticate database user access and meet different security needs. The most common method of authentication is using a user name and password. This can create additional overhead for both users and database administrators to manage and rotate these credentials; it also requires additional investments in auditing and governance. Amazon Aurora MySQL now supports Microsoft Active Directory (AD) authentication using Kerberos for Amazon Aurora MySQL version 3.03 and higher. With support for AWS Directory Se ..read more

Amazon SageMaker is a fully managed machine learning (ML) service that provides a range of tools and features for building, training, and deploying ML models. Amazon DataZone is a data management service that makes it faster and easier for customers to catalog, discover, share, and govern data stored across AWS, on-premises, and third-party sources. Today, we are excited to announce an integration between Amazon SageMaker and Amazon DataZone to help you set up infrastructure with security controls, collaborate on machine learning (ML) projects, and govern access to data and ML assets. When so ..read more

The telecommunications industry is more competitive than ever before. With customers able to easily switch between providers, reducing customer churn is a crucial priority for telecom companies who want to stay ahead. To address this challenge, Dialog Axiata has pioneered a cutting-edge solution called the Home Broadband (HBB) Churn Prediction Model. This post explores the intricacies of Dialog Axiata’s approach, from the meticulous creation of nearly 100 features across ­10 distinct areas and the implementation of two essential models using Amazon SageMaker: A base model powered by CatBoost ..read more

Data is the lifeblood of the organization. Being able to make quick, accurate and actionable decisions based on authoritative data enables enterprises to offer differentiated services and improve customer satisfaction. The rise of generative AI has only further amplified this. It’s therefore important that your database provides near-real-time performance when interacting with operational data. For PostgreSQL databases, we offer Cloud SQL for PostgreSQL Enterprise Plus edition, which offers improved performance out of the box, improved data protection (35 days of PITR) and improved availabil ..read more

By default, Google Cloud Monitoring accepts and processes all well-formed metrics sent to a metric ingestion endpoint. However, under certain circumstances, metrics generation can be prolific, leading to a series of unnecessary expenses. This is especially true for verbose metrics of no particular utility. To control costs, platform users need a way to manage the flow of metrics prior to ingestion so that only relevant and useful metrics are processed and billed for. Managed Service for Prometheus, which uses Cloud Monitoring under the hood, charges on a per-sample basis. Therefore, controll ..read more

Data-driven companies are increasingly infusing real-time data into their applications and user experiences, especially with the advent of new technologies that make data capture more on-demand – and at a higher volume – than ever before. Change data capture (CDC) is a long-standing mechanism that data practitioners use to connect their transactional systems with their analytical warehouses. Historically, customers have had to manage temp tables and schedule merge statements to keep their systems in sync, which can be a lot of work and prone to failures. To solve these problems, BigQuery inc ..read more