IBM Journey to AI blog

DECEMBER 14, 2023

Another year has passed—it felt like the whole world was talking about and trying out tools powered by generative AI and Large Language Models (LLMs). From photo editing to writing emails, AI has become an indispensable tool in many settings. This year, IBM Cloud introduced projects and deployable architectures.

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AWS Machine Learning Blog

DECEMBER 19, 2023

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. Machine learning operations (MLOps) applies DevOps principles to ML systems.

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AWS Machine Learning Blog

MAY 9, 2024

The rapid advancements in artificial intelligence and machine learning (AI/ML) have made these technologies a transformative force across industries. According to a McKinsey study , across the financial services industry (FSI), generative AI is projected to deliver over $400 billion (5%) of industry revenue in productivity benefits.

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IBM Journey to AI blog

DECEMBER 20, 2023

By giving machines the growing capacity to learn, reason and make decisions, AI is impacting nearly every industry, from manufacturing to hospitality, healthcare and academia. Ethical considerations such as bias, transparency and regulatory concerns should also be addressed to support responsible deployment.

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Unite.AI

JUNE 15, 2023

It's also important to note that NLP Engineer salaries can differ from those of Data Scientists or Machine Learning Engineers , even though the roles share certain similarities. This requires a deep understanding of machine learning techniques, linguistic concepts, and relevant programming languages.

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IBM Journey to AI blog

JANUARY 15, 2024

Traditional AI primarily relies on algorithms and extensive labeled data sets to train models through machine learning. It can empower government workers with more powerful tools for answering questions and conducting research. Machine learning-trained generative AI might help in drafting new contracts based on this research.

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Towards AI

MARCH 3, 2024

Ah, the wild world of data science and machine learning, where algorithms roam free, and models hold the keys to unlocking the mysteries of the universe. But in this jungle of ones and zeros, there lies a crucial tool — MLOps, the superhero of streamlined development and deployment. But wait, there’s more!

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IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

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IBM Journey to AI blog

APRIL 25, 2024

BPR examples are not one-time projects, but rather examples of a continuous journey of innovation and change focused on optimizing end-to-end processes and eliminating redundancies. This blog outlines some BPR examples that benefit from a BPM methodology.

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IBM Journey to AI blog

APRIL 18, 2024

Imagine a world where machines aren’t confined to pre-programmed tasks but operate with human-like autonomy and competence. AGI, sometimes referred to as strong AI , is the science-fiction version of artificial intelligence (AI), where artificial machine intelligence achieves human-level learning, perception and cognitive flexibility.

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IBM Journey to AI blog

AUGUST 14, 2023

Under-deployed tools and solutions that do the minimal that’s “good enough” or that face other barriers like the risk aversion to fully automating processes that could have unintended consequences. However, AI and automation are often used in a limited fashion or only in certain security tools.

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IBM Journey to AI blog

JUNE 26, 2023

By infusing artificial intelligence (AI) into IT operations , you can leverage the considerable power of natural language processing and machine learning models to automate and streamline operational workflows. But where to start? Plus, we have practical next steps to guide your AIOps journey. of global electricity use.

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IBM Journey to AI blog

OCTOBER 16, 2023

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. An AI governance framework ensures the ethical, responsible and transparent use of AI and machine learning (ML).

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IBM Journey to AI blog

FEBRUARY 14, 2024

Using machine learning (ML), AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. Not only will this help scale the AOT tech across markets, but it will also help tackle integrations including additional languages, dialects and menu variations.

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Heartbeat

FEBRUARY 1, 2023

Machine learning has become an essential part of our lives because we interact with various applications of ML models, whether consciously or unconsciously. Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models.

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AWS Machine Learning Blog

DECEMBER 18, 2023

It establishes a framework for organizations to systematically address and control the risks related to the development and deployment of AI. Trust in AI is crucial and integrating standards such as ISO 42001, which promotes AI governance, is one way to help earn public trust by supporting a responsible use approach.

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IBM Journey to AI blog

MAY 6, 2024

Seamlessly integrating these elements is essential for leading-edge success in clinical development. Enter the age of data-driven protocol assessment: using benchmarking tools and predictive modeling to gauge protocol intricacies and forecast eligible patient numbers, which then inform protocol adjustments.

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AWS Machine Learning Blog

APRIL 29, 2024

Arcee’s innovative continual pre-training (CPT) and model merging techniques have brought a significant leap forward in the efficient training of LLMs, with particularly strong evaluations in the medical, legal, and financial verticals. In this post, we show you how efficient we make our continual pre-training by using Trainium chips.

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IBM Journey to AI blog

AUGUST 1, 2023

Using multiple platforms to discover apps and manage laptops and mobile devices negatively impacts the IT and security team’s efficiency and productivity in continuous application patching. Trojans, worms and malware are no longer the only cyberthreats keeping IT and security professionals awake at night.

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AWS Machine Learning Blog

JANUARY 5, 2024

Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems.

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IBM Journey to AI blog

OCTOBER 9, 2023

Artificial intelligence (AI) can help usher in a new era of human resource management, where data analytics, machine learning and automation can work together to save people time and support higher-quality outcomes. These are some possible use cases: Professional learning and development: Where do workers see themselves in five years?

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AWS Machine Learning Blog

NOVEMBER 22, 2023

By using the Framework, you will learn operational and architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable workloads in the cloud. By reading this post, you will learn about the Operational Excellence pillar in the Well-Architected Framework with the IDP case study.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

This is a joint blog with AWS and Philips. Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. Philips is a health technology company focused on improving people’s lives through meaningful innovation.

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AWS Machine Learning Blog

APRIL 8, 2024

For more details on the model’s training process, safety considerations, learnings, and intended uses, refer to the paper Llama 2: Open Foundation and Fine-Tuned Chat Models. Llama 2 models are available on Amazon SageMaker JumpStart for a quick and straightforward deployment.

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AWS Machine Learning Blog

DECEMBER 1, 2023

Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. As a web application, SageMaker Studio has improved load time, faster IDE and kernel start up times, and automatic upgrades.

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AWS Machine Learning Blog

APRIL 16, 2024

By design, there is no mechanism for any Amazon employee to access a Nitro EC2 instance that customers use to run their workloads, or to access data that customers send to a machine learning (ML) accelerator or GPU. FMs and the applications built around them represent extremely valuable investments for our customers.

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ODSC - Open Data Science

OCTOBER 19, 2023

Trending AI GitHub Repos: Week of October 9, 2023 This week’s trending GitHub AI repos have been highlighted by tools that streamline LLMs, integrate GPT models, and store data. This tutorial walks you through adapting a pre-trained model to generate text that emulates chat conversations from a Slack channel. Register now for 30% off.

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IBM Journey to AI blog

MAY 26, 2023

Life sciences organizations are developing science/research/commercial clouds to automate mundane tasks associated with each of the areas, such as logging, monitoring, auditing, patching, and integration with an existing toolset, to name a few. In 2017, 94% of hospitals used electronic clinical data from their EHR.

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Heartbeat

JUNE 5, 2023

In recent years, machine learning has exploded in popularity because of its wide range of potential uses in fields including healthcare, finance, eCommerce, and the arts. However, managing machine learning projects can be challenging, especially as the size and complexity of the data and models increase.

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Heartbeat

OCTOBER 5, 2023

Unsurprisingly, Machine Learning (ML) has seen remarkable progress, revolutionizing industries and how we interact with technology. This is where the world of operations steps in, and while MLOps (Machine Learning Operations) has been a guiding light, a new paradigm is emerging — LLMOps (Large Language Model Operations).

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TheSequence

MARCH 11, 2024

Peter Hayes, who holds a PhD in Machine Learning from University College London, is one of the world’s leading experts on this topic. This post is a shortened version of Peter’s original blog, titled 'Evaluating LLM Applications '. Typical makeup of a test suite in software development CI.

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IBM Journey to AI blog

JUNE 29, 2023

Sensors collect data in real-time that is then fed into an enterprise asset management (EAM) or computerized maintenance management system (CMMS), where AI-enhanced data analysis tools and processes like machine learning (ML) spot issues and help resolve them.

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IBM Journey to AI blog

OCTOBER 9, 2023

As governments around the globe continue experimenting with the use of AI, and looking into ways to tap into foundational models offered by generative AI, one important question stands out: How will citizens benefit from the technology? The public sector is called just that for a reason: the public should always be the priority.

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IBM Journey to AI blog

JULY 25, 2023

Smart grid technology—an integral part of energy’s digital transformation—promises to modernize the traditional electrical system with an infusion of digital intelligence that helps energy providers transition to clean energy and reduce carbon emissions. Wi-Fi, Z-Wave, Zigbee and 4G/5G).

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AWS Machine Learning Blog

DECEMBER 13, 2023

Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. The SageMaker Model Registry centralizes model tracking, simplifying model deployment.

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Heartbeat

FEBRUARY 28, 2024

Experimentation is the lifeblood of machine learning. This article will explore how two powerful tools, Comet and Gradio, simplify and enhance your machine learning journey. This article will explore how two powerful tools, Comet and Gradio, simplify and enhance your machine learning journey.

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