Hacker News

NOVEMBER 19, 2024

Here are a few of the things that you might do as an AI Engineer at TigerEye: - Design, develop, and validate statistical models to explain past behavior and to predict future behavior of our customers’ sales teams - Own training, integration, deployment, versioning, and monitoring of ML components - Improve TigerEye’s existing metrics collection and (..)

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Pickl AI

DECEMBER 26, 2024

Summary: “Data Science in a Cloud World” highlights how cloud computing transforms Data Science by providing scalable, cost-effective solutions for big data, Machine Learning, and real-time analytics. In Data Science in a Cloud World, we explore how cloud computing has revolutionised Data Science.

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Towards AI

DECEMBER 26, 2024

4 Things to Keep in Mind Before Deploying Your ML Models This member-only story is on us. Source: Image By Author As a Cloud Engineer, Ive recently collaborated with a number of project teams, and my primary contribution to these teams has been to do the DevOps duties required on the GCP Cloud. Upgrade to access all of Medium.

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Dataconomy

MAY 9, 2025

This service model eliminates the need for significant upfront investments in infrastructure and expertise, allowing companies to leverage AI technologies such as Natural Language Processing and Computer Vision without the complexities of traditional development processes. What is machine learning as a service (MLaaS)?

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Flipboard

FEBRUARY 11, 2025

By using Amazon Q Business, which simplifies the complexity of developing and managing ML infrastructure and models, the team rapidly deployed their chat solution. He is passionate about helping organizations leverage the full potential of cloud computing to drive innovation in generative AI and machine learning.

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Precisely

MAY 20, 2024

Cloud computing is more crucial than ever in 2024. With technology landscapes transforming at a breakneck pace, your ability to leverage cloud computing could be the game changer needed to boost efficiency and spark innovation in your business. Hybrid and multi-cloud adoption : The future is now, and it’s hybrid.

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Pickl AI

JANUARY 9, 2025

Summary: Cloud computing security architecture is essential for protecting sensitive data, ensuring compliance, and preventing threats. As technology advances, AI, machine learning, and blockchain play vital roles in strengthening cloud security frameworks to safeguard businesses against evolving risks. from 2024 to 2030.

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Pickl AI

JANUARY 31, 2025

Summary: In this cloud computing notes we offers the numerous advantages for businesses, such as cost savings, scalability, enhanced collaboration, and improved security. Embracing cloud solutions can significantly enhance operational efficiency and drive innovation in today’s competitive landscape.

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Flipboard

APRIL 23, 2025

In addition, he builds and deploys AI/ML models on the AWS Cloud. Dr. Ian Lunsford is an Aerospace Cloud Consultant at AWS Professional Services. He integrates cloud services into aerospace applications. Additionally, Ian focuses on building AI/ML solutions using AWS services.

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Pickl AI

APRIL 7, 2025

Summary: This blog explains the difference between cloud computing and grid computing in simple terms. Ideal for beginners and tech enthusiasts exploring modern computing trends. Introduction Welcome to our exploration, where we highlight the difference between cloud computing and grid computing.

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Towards AI

JANUARY 29, 2025

Programming Languages: Python (most widely used in AI/ML) R, Java, or C++ (optional but useful) 2. Cloud Computing: AWS, Google Cloud, Azure (for deploying AI models) Soft Skills: 1. Programming: Learn Python, as its the most widely used language in AI/ML. Problem-Solving and Critical Thinking 2.

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Data Science Dojo

FEBRUARY 20, 2023

Machine learning (ML) is the technology that automates tasks and provides insights. It comes in many forms, with a range of tools and platforms designed to make working with ML more efficient. It features an ML package with machine learning-specific APIs that enable the easy creation of ML models, training, and deployment.

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Pickl AI

MARCH 1, 2023

What is Cloud Computing? Cloud computing is a way to use the internet to access different types of technology services. The term “cloud computing” was first used in a paper by computer scientist and mathematician Ramnath Chellappa in 1997.

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Flipboard

MAY 16, 2025

AWS) is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals, companies, and governments, on a metered, pay-as-you-go basis. Statement: 'AWS is Amazon subsidiary that provides cloud computing services.' She is passionate about AI/ML, finance and software security topics.

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Dataconomy

MAY 2, 2023

What do machine learning engineers do: ML engineers design and develop machine learning models The responsibilities of a machine learning engineer entail developing, training, and maintaining machine learning systems, as well as performing statistical analyses to refine test results. Is ML engineering a stressful job?

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Dataversity

DECEMBER 3, 2021

As edge cloud computing, AI/ML, and IoT revolutionize computing, many enterprises are considering pulling back on data center operations in favor of cloud-based solutions.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

The AWS Neuron Monitor container , used with Prometheus and Grafana, provides advanced visualization of your ML application performance. To learn more about setting up and using these monitoring capabilities, see Scale and simplify ML workload monitoring on Amazon EKS with AWS Neuron Monitor container.

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Pickl AI

JULY 11, 2024

Summary: Platform as a Service (PaaS) offers a cloud development environment with tools, frameworks, and resources to streamline application creation. Introduction The cloud computing landscape has revolutionized the way businesses approach IT infrastructure and application development.

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AWS Machine Learning Blog

NOVEMBER 8, 2023

In this era of cloud computing, developers are now harnessing open source libraries and advanced processing power available to them to build out large-scale microservices that need to be operationally efficient, performant, and resilient. This can lead to higher latency and increased network bandwidth utilization.

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IBM Journey to AI blog

FEBRUARY 6, 2024

It’s hard to imagine a business world without cloud computing. There would be no e-commerce, remote work capabilities or the IT infrastructure framework needed to support emerging technologies like generative AI and quantum computing. What is cloud computing?

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Dataconomy

JUNE 16, 2023

Any organization’s cybersecurity plan must include data loss prevention (DLP), especially in the age of cloud computing and software as a service (SaaS). Customers can benefit from the people-centric security solutions offered by Gamma AI’s AI-powered cloud DLP solution. How to use Gamme AI?

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AWS Machine Learning Blog

APRIL 7, 2025

This approach allows for greater flexibility and integration with existing AI and machine learning (AI/ML) workflows and pipelines. By providing multiple access points, SageMaker JumpStart helps you seamlessly incorporate pre-trained models into your AI/ML development efforts, regardless of your preferred interface or workflow.

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Applied Data Science

AUGUST 2, 2021

Machine Learning In this section, we look beyond ‘standard’ ML practices and explore the 6 ML trends that will set you apart from the pack in 2021. Give this technique a try to take your team’s ML modelling to the next level. Explainable ML When modelling business process, the why is often more important than the what.

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Flipboard

FEBRUARY 13, 2025

AI/ML engineers would prefer to focus on model training and data engineering, but the reality is that we also need to understand the infrastructure and mechanics […]

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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Dataconomy

SEPTEMBER 1, 2024

In this era of modern business operations, cloud computing cannot be overlooked, thanks to its scalability, flexibility, and accessibility for data processing, storage, and application deployment. This raises a lot of security questions about the suitability of the cloud. These two intersect in many ways discussed below.

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IBM Journey to AI blog

MARCH 20, 2024

Most of us take for granted the countless ways public cloud-related services—social media sites (Instagram), video streaming services (Netflix), web-based email applications (Gmail), and more—permeate our lives. What is a public cloud? A public cloud is a type of cloud computing in which a third-party service provider (e.g.,

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AWS Machine Learning Blog

FEBRUARY 21, 2025

For this post, we have two active directory groups, ml-engineers and security-engineers. We test the access of two users, John Doe and Jane Smith, who are users of the ml-engineers group and security-engineers group, respectively. You can retrieve the user name and password for each user from Secrets Manager.

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The MLOps Blog

JUNE 27, 2023

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. Model monitoring and performance tracking : Platforms should include capabilities to monitor and track the performance of deployed ML models in real-time.

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IBM Journey to AI blog

JUNE 5, 2024

SaaS takes advantage of cloud computing infrastructure and economies of scale to provide clients a more streamlined approach to adopting, using and paying for software. SaaS offers businesses cloud-native app capabilities, but AI and ML turn the data generated by SaaS apps into actionable insights.

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Dataconomy

OCTOBER 23, 2024

Artificial intelligence (AI) and machine learning (ML) are arguably the frontiers of modern technology. AI and ML can streamline various business processes and help maximize your returns margins. But all that changed when cloud computing happened.

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Mlearning.ai

APRIL 28, 2023

But there are some strategies that artificial intelligence(AI) developers can implement to optimize and decrease execution time for Python machine learning (ML) models, for instance: Using binary formats for saving models Saving machine learning models in binary formats like .pkl, Photo by depositphotos Binary formats in ml models like .pkl,

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AWS Machine Learning Blog

MAY 3, 2023

New generations of CPUs offer a significant performance improvement in machine learning (ML) inference due to specialized built-in instructions. times the speed for BERT, making Graviton-based instances the fastest compute optimized instances on AWS for these models. inference for Arm-based processors. is up to 3.5

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Flipboard

APRIL 24, 2025

Create a connector for Amazon Bedrock in OpenSearch Service To use OpenSearch Service machine learning (ML) connectors with other AWS services, you need to set up an IAM role allowing access to that service. Familiarity with Python programming language. The code is open source and hosted on GitHub.

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AWS Machine Learning Blog

OCTOBER 30, 2023

Solution overview SageMaker JumpStart provides pre-trained, open-source models for a wide range of problem types to help you get started with machine learning (ML). JumpStart also provides solution templates that set up infrastructure for common use cases, and executable example notebooks for ML with Amazon SageMaker.

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AWS Machine Learning Blog

MAY 7, 2024

Hugging Face is an open-source machine learning (ML) platform that provides tools and resources for the development of AI projects. We work together with your team and your chosen member of the AWS Partner Network (APN) to implement your enterprise cloud computing initiatives.

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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. GitHub serves as a centralized location to store, version, and manage your ML code base.

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