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 170

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

AWS Machine Learning Blog

SEPTEMBER 23, 2024

Machine learning (ML) projects are inherently complex, involving multiple intricate steps—from data collection and preprocessing to model building, deployment, and maintenance. Admins can navigate to the IAM console, search for the SageMaker Studio role, and add the policy outlined in Set up Amazon Q Developer for your users.

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ML Computer Vision Data Scientist Machine Learning 89

AWS Machine Learning Blog

JUNE 25, 2024

This solution simplifies the integration of advanced monitoring tools such as Prometheus and Grafana, enabling you to set up and manage your machine learning (ML) workflows with AWS AI Chips. By deploying the Neuron Monitor DaemonSet across EKS nodes, developers can collect and analyze performance metrics from ML workload pods.

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ML Metadata Software Development Generative AI 94

Marktechpost

NOVEMBER 16, 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 Engineer Prompt Engineering 103

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 116

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.

Data Scientist 119

Data Scientist ML Engineer Machine Learning Data Science 119

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.

Artificial Intelligence 122

Artificial Intelligence Artificial Intelligence Prompt Engineer Prompt Engineering 122

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 141

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

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 ML 134

The MLOps Blog

JUNE 13, 2024

Data scientists often lack focus, time, or knowledge about software engineering principles. As a result, poor code quality and reliance on manual workflows are two of the main issues in ML development processes. I started as a full-stack developer but have gradually moved toward data and ML engineering.

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ML Data Scientist Automation Software Development 59

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 111

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 127

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 ML Engineer Software Development Machine Learning 98

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 103

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 127

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 153

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 124

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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Generative AI Metadata Python ML 127

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 Data Scientist Convolutional Neural Networks ML 98

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 Machine Learning 98

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

ML Engineer 52

ML Engineer Software Engineer Data Scientist Software Development 52

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

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 130

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 112

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

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 92

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 121

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 Data Scientist Software Development 100

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

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.

Machine Learning 52

Machine Learning ML Engineer Data Scientist ML 52

AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

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Machine Learning Data Scientist ML Python 133

AWS Machine Learning Blog

OCTOBER 2, 2023

A successful deployment of a machine learning (ML) model in a production environment heavily relies on an end-to-end ML pipeline. Although developing such a pipeline can be challenging, it becomes even more complex when dealing with an edge ML use case. Now it’s time to draft an architecture for our MLOps pipeline.

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DevOps Automation ML Computer Vision 121

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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Generative AI Machine Learning Artificial Intelligence Artificial Intelligence 124

Flipboard

MARCH 7, 2023

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Each image can host one or multiple kernels , which can be different virtual environments for development.

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Python Data Scientist Data Science Automation 123

AWS Machine Learning Blog

APRIL 25, 2023

Data scientists typically carry out several iterations of experimentation in data processing and training models while working on any ML problem. They want to run this ML code and carry out the experimentation with ease of use and minimal code change.

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Machine Learning Data Scientist Python ML 101