IBM Journey to AI blog

AUGUST 12, 2024

AIOPs refers to the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance and automate various aspects of IT operations (ITOps). ML technologies help computers achieve artificial intelligence. However, they differ fundamentally in their purpose and level of specialization in AI and ML environments.

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Big Data DevOps Automation Machine Learning 278

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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Data Science DevOps Machine Learning Data Scientist 188

Unite.AI

JUNE 29, 2023

You can also explore the Google Cloud Skills Boost program, specifically designed for ML APIs, which offers extra support and expertise in this field. Optimizing workloads and costs To address the challenges of expensive and complex ML infrastructure, many companies increasingly turn to cloud services.

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AWS Machine Learning Blog

JULY 8, 2024

As industries begin adopting processes dependent on machine learning (ML) technologies, it is critical to establish machine learning operations (MLOps) that scale to support growth and utilization of this technology. There were noticeable challenges when running ML workflows in the cloud.

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Data Scientist ML Engineer Machine Learning ML 112

AWS Machine Learning Blog

NOVEMBER 21, 2023

By taking care of the undifferentiated heavy lifting, SageMaker allows you to focus on working on your machine learning (ML) models, and not worry about things such as infrastructure. Prior to working at Amazon Music, Siddharth was working at companies like Meta, Walmart Labs, Rakuten on E-Commerce centric ML Problems.

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ML Deep Learning Machine Learning DevOps 103

Unite.AI

JUNE 19, 2024

In world of Artificial Intelligence (AI) and Machine Learning (ML), a new professionals has emerged, bridging the gap between cutting-edge algorithms and real-world deployment. Meet the MLOps Engineer: the orchestrating the seamless integration of ML models into production environments, ensuring scalability, reliability, and efficiency.

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Software Engineer DevOps Machine Learning ML 182

TheSequence

OCTOBER 13, 2023

Join the global machine learning community at this virtual event to network and share best practices with your peers on platforms and architectures for production ML. Event Details - Date: Tuesday, November 14 - Time: 9:30AM – 3:00PM PT - Location: Virtual REGISTER NOW

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ML Engineer Software Engineer Software Development Data Scientist 52

Marktechpost

JUNE 19, 2024

It includes prompt engineering techniques, ethical considerations, and hands-on labs using tools like IBM Watsonx and GPT. Generative AI for Software Developers Specialization This IBM specialization teaches software developers to leverage generative AI for writing high-quality code, enhancing productivity and efficiency.

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Artificial Intelligence Artificial Intelligence Prompt Engineering Prompt Engineer 118

The MLOps Blog

AUGUST 4, 2023

Situations described above arise way too often in ML teams, and their consequences vary from a single developer’s annoyance to the team’s inability to ship their code as needed. Let’s dive into the world of monorepos, an architecture widely adopted in major tech companies like Google, and how they can enhance your ML workflows.

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ML Machine Learning Python Software Engineer 52

Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. This is important because training ML models and then using the trained models to make predictions (inference) can be highly energy-intensive tasks.

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Machine Learning BERT Deep Learning ML Engineer 123

AWS Machine Learning Blog

SEPTEMBER 25, 2024

SageMaker JumpStart is a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. SageMaker Studio is a comprehensive IDE that offers a unified, web-based interface for performing all aspects of the ML development lifecycle. Deploy Llama 3.2

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Software Engineer Software Development ML Python 124

Marktechpost

OCTOBER 23, 2023

With a meticulously crafted design offering extension points for seamless third-party integration, this innovation accelerates the execution of machine learning (ML) models on specialized hardware. Exemplar ML models running on the platform. The creators of ExecuTorch have thoughtfully provided the following: Extensive documentation.

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ML Engineer ML Software Development Machine Learning 91

Pickl AI

APRIL 19, 2023

Different industries from education, healthcare to marketing, retail and ecommerce require Machine Learning Engineers. Job market will experience a rise of 13% by 2026 for ML Engineers Why is Machine Learning Important? Accordingly, an entry-level ML engineer will earn around 5.1 Consequently.

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Machine Learning ML Engineer Data Scientist ML 52

Towards AI

FEBRUARY 15, 2024

AI Engineers: Your Definitive Career Roadmap Become a professional certified AI engineer by enrolling in the best AI ML Engineer certifications that help you earn skills to get the highest-paying job. Experience working in data analysis, software development, and business is also crucial for an AI engineer.

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AI Engineer Software Engineer Artificial Intelligence Artificial Intelligence 89

O'Reilly Media

JANUARY 17, 2023

What would you say is the job of a software developer? A layperson, an entry-level developer, or even someone who hires developers will tell you that job is to … well … write software. They’d say that the job involves writing some software, sure. This mindset has followed me into my work in ML/AI.

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Automation Robotics Data Scientist Software Development 132

The MLOps Blog

SEPTEMBER 7, 2023

Have you ever talked to your Front-end or Back-end engineer peers and noticed how much they care about code quality? Writing legible, reusable, and efficient code has always been a challenge in the software development community. This situation is not different in the ML world.

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Software Engineer Machine Learning ETL Data Scientist 52

Unite.AI

FEBRUARY 15, 2024

The people aspect underlines the need for skilled AI/ML engineers, data scientists, and software developers. Data readiness emphasizes the importance of high-quality training data, while concerns about data privacy and security are also raised.

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Generative AI ML Engineer AI AI 100

AWS Machine Learning Blog

JANUARY 10, 2024

Specialist Data Engineering at Merck, and Prabakaran Mathaiyan, Sr. ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. The input to the training pipeline is the features dataset.

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ML Machine Learning Data Scientist ETL 120

AWS Machine Learning Blog

JANUARY 17, 2023

Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) partly based on JupyterLab 3. Studio provides a web-based interface to interactively perform ML development tasks required to prepare data and build, train, and deploy ML models.

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Software Engineer ML Engineer ML Machine Learning 89

O'Reilly Media

OCTOBER 19, 2021

As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. What does a modern technology stack for streamlined ML processes look like? All ML projects are software projects.

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DevOps Software Engineer Data Scientist ML 140

AWS Machine Learning Blog

AUGUST 22, 2024

You can use Amazon SageMaker Model Building Pipelines to collaborate between multiple AI/ML teams. SageMaker Pipelines You can use SageMaker Pipelines to define and orchestrate the various steps involved in the ML lifecycle, such as data preprocessing, model training, evaluation, and deployment.

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AWS Machine Learning Blog

JULY 25, 2024

By accelerating the speed of issue detection and remediation, it increases the reliability of your ML training and reduces the wasted time and cost due to hardware failure. Ziwen Ning is a software development engineer at AWS. Geeta Gharpure is a senior software developer on the Annapurna ML engineering team.

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Auto-complete Software Development ML ML Engineer 111

AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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ML Auto-complete Machine Learning Auto-classification 90

Towards AI

AUGUST 24, 2023

Common mistakes and misconceptions about learning AI/ML Markus Spiske on Unsplash A common misconception of beginners is that they can learn AI/ML from a few tutorials that implement the latest algorithms, so I thought I would share some notes and advice on learning AI. Trying to code ML algorithms from scratch.

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Neural Network Software Engineer Algorithm AI Engineer 86

Chatbots Life

MAY 16, 2023

10CLOUDS Year Founded : 2009 HQ : Warsaw, Poland Team Size : 51–200 employees Clients : TrustStamp (Identity verification), Emergent Tech (G-Coin), AlephZero (Blockchain), Tapeke (BitCoin Software Development), Tagasauris (Crowdsourcing Software Development), CallerSmart.

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Generative AI Chatbots Conversational AI Software Development 98

AWS Machine Learning Blog

MARCH 9, 2023

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. This has led to the emergence of a data-centric approach to ML and various techniques to improve model performance by focusing on data requirements.

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ML Engineer Convolutional Neural Networks Data Scientist Machine Learning 85

Pickl AI

JANUARY 30, 2023

It’s a universal programming language that finds application in different technologies like AI, ML, Big Data and others. A Software Developer Uses Python: Backend Development : Python finds applications in developing server-side applications and APIs. The role of Python is not just limited to Data Science.

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Python DevOps Machine Learning Data Science 97

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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Data Scientist ML Data Science Automation 109

AWS Machine Learning Blog

OCTOBER 15, 2024

ML practitioners can deploy FMs to dedicated SageMaker instances from a network-isolated environment and customize models using SageMaker for model training and deployment. About the Authors Bar Fingerman is the Head of AI/ML Engineering at Bria. She helps key customer accounts on their data, generative AI, and AI/ML journeys.

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Generative AI ML Python Responsible AI 81

AWS Machine Learning Blog

MAY 15, 2024

Next, we present the solution architecture and process flows for machine learning (ML) model building, deployment, and inferencing. Here, Amazon SageMaker Ground Truth allowed ML engineers to easily build the human-in-the-loop workflow (step v). We end with lessons learned. Burak is still a research affiliate in MIT.

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Generative AI AI AI Machine Learning 123

Flipboard

SEPTEMBER 4, 2023

Envision yourself as an ML Engineer at one of the world’s largest companies. You make a Machine Learning (ML) pipeline that does everything, from gathering and preparing data to making predictions. What if we decouple the dependencies of the software we write from the hardware it works on? Enter the concept of Containers.

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Machine Learning Computer Vision Deep Learning Python 148

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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ML Automation Metadata Software Development 122

AWS Machine Learning Blog

AUGUST 20, 2024

About the Authors Sundar Raghavan is an AI/ML Specialist Solutions Architect at AWS, helping customers build scalable and cost-efficient AI/ML pipelines with Human in the Loop services. Jacky Shum is a Software Engineer at AWS in the SageMaker Ground Truth team.

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Software Engineer ML Machine Learning Metadata 107

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. What is MLOps?

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DevOps Machine Learning Data Scientist Software Engineer 96

The MLOps Blog

JANUARY 23, 2023

Just so you know where I am coming from: I have a heavy software development background (15+ years in software). Came to ML from software. Founded two successful software services companies. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”.

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DevOps Metadata Software Engineer Data Scientist 59

AWS Machine Learning Blog

AUGUST 1, 2024

By demonstrating the process of deploying fine-tuned models, we aim to empower data scientists, ML engineers, and application developers to harness the full potential of FMs while addressing unique application requirements. SageMaker Studio is a single web-based interface for end-to-end machine learning (ML) development.

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Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. MLOps aims to automate and operationalize ML models, enabling smoother transitions to production and deployment.

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Machine Learning ML Automation Data Scientist 52