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.

ML 108

ML DevOps Software Development Machine Learning 108

Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. Data scientists and ML engineers require capable tooling and sufficient compute for their work.

ML 154

ML DevOps Data Scientist ML Engineer 154

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.

ML 105

ML DevOps Software Development Machine Learning 105

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.

Software Engineer 182

Software Engineer DevOps Machine Learning ML 182

IBM Journey to AI blog

FEBRUARY 6, 2024

Cloud-based applications and services Cloud-based applications and services support myriad business use cases—from backup and disaster recovery to big data analytics to software development. Serverless computing allows software developers to devote more attention to the code and business logic specific to their applications.

DevOps 299

DevOps Automation Software Development Big Data 299

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. Today, 35% of companies report using AI in their business, which includes ML, and an additional 42% reported they are exploring AI, according to the IBM Global AI Adoption Index 2022. What is MLOps? Where they are deployed.

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

Flipboard

APRIL 21, 2023

A report by GitLab finds that AI and ML in software development workflows show promise, but challenges like toolchain complexity and security concerns persist. The post DevSecOps: AI is reshaping developer roles, but it’s not all smooth sailing appeared first on TechRepublic.

Software Development 171

Software Development ML AI AI 171

AWS Machine Learning Blog

NOVEMBER 19, 2024

This year, generative AI and machine learning (ML) will again be in focus, with exciting keynote announcements and a variety of sessions showcasing insights from AWS experts, customer stories, and hands-on experiences with AWS services. Visit the session catalog to learn about all our generative AI and ML sessions.

ML 88

ML Generative AI AI AI 88

Pickl AI

APRIL 17, 2023

How can a DevOps team take advantage of Artificial Intelligence (AI)? DevOps is mainly the practice of combining different teams including development and operations teams to make improvements in the software delivery processes. So now, how can a DevOps team take advantage of Artificial Intelligence (AI)?

DevOps 52

DevOps Artificial Intelligence Artificial Intelligence Machine Learning 52

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). Lived through the DevOps revolution. Came to ML from software. Founded two successful software services companies. If you’d like a TLDR, here it is: MLOps is an extension of DevOps.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

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.

ML 80

ML Computer Vision Data Scientist Machine Learning 80

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.

ML 100

ML Deep Learning Machine Learning DevOps 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?

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

AWS Machine Learning Blog

DECEMBER 12, 2023

By 2025, Vodafone plans to have 50% of its global workforce actively involved in software development, with an objective to deliver 60% of digital services in-house. In this post, we share how Vodafone is advancing its ML skills using AWS DeepRacer and Accenture. Why is machine learning important to Vodafone? Why AWS DeepRacer?

Machine Learning 119

Machine Learning ML DevOps Software Engineer 119

Towards AI

AUGUST 16, 2023

Pietro Jeng on Unsplash MLOps is a set of methods and techniques to deploy and maintain machine learning (ML) models in production reliably and efficiently. Thus, MLOps is the intersection of Machine Learning, DevOps, and Data Engineering (Figure 1). There is no central store to manage models (versions and stage transitions).

Machine Learning 90

Machine Learning DevOps Software Development ML 90

The MLOps Blog

APRIL 5, 2023

In this comprehensive guide, we’ll explore the key concepts, challenges, and best practices for ML model packaging, including the different types of packaging formats, techniques, and frameworks. These teams may include but are not limited to data scientists, software developers, machine learning engineers, and DevOps engineers.

ML 69

ML Machine Learning Deep Learning Metadata 69

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.

Python 97

Python DevOps Machine Learning Data Science 97

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.

Generative AI 113

Generative AI Metadata Python ML 113

IBM Journey to AI blog

AUGUST 6, 2024

Serverless, or serverless computing, is an approach to software development that empowers developers to build and run application code without having to worry about maintenance tasks like installing software updates, security, monitoring and more. How does serverless work?

DevOps 112

DevOps Big Data Chatbots Artificial Intelligence 112

Unite.AI

SEPTEMBER 4, 2024

Software Development and Code Management The native GitHub integration makes Claude a powerful ally for engineering teams. Taylor McCaslin, Product Lead for AI and ML Tech at GitLab, noted that Claude allowed them to take on more complex tasks while ensuring their intellectual property remained private and protected.

DevOps 147

DevOps Large Language Models Automation Software Development 147

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 108

Data Scientist ML Engineer Machine Learning Data Science 108

ODSC - Open Data Science

APRIL 17, 2023

This shift in thinking has led us to DevSecOps , a novel methodology that integrates security into the software development/ MLOps process. DevSecOps includes all the characteristics of DevOps, such as faster deployment, automated pipelines for build and deployment, extensive testing, etc.,

Data Science 97

Data Science DevOps Deep Learning Automation 97

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.

DevOps 109

DevOps Automation ML Computer Vision 109

Towards AI

AUGUST 16, 2023

Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps.

Machine Learning 83

Machine Learning Software Development Data Drift DevOps 83

AWS Machine Learning Blog

OCTOBER 2, 2023

Solution overview In Part 1 of this series, we laid out an architecture for our end-to-end MLOps pipeline that automates the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. In Part 2 , we showed how to automate the labeling and model training parts of the pipeline.

DevOps 107

DevOps Automation ML Machine Learning 107

AWS Machine Learning Blog

OCTOBER 16, 2023

As an AI-powered solution, Veriff needs to create and run dozens of machine learning (ML) models in a cost-effective way. Infrastructure and development challenges Veriff’s backend architecture is based on a microservices pattern, with services running on different Kubernetes clusters hosted on AWS infrastructure.

Data Scientist 102

Data Scientist Computer Vision DevOps Python 102

AWS Machine Learning Blog

APRIL 19, 2023

Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. We recently developed four more new models. These models are then pushed to an Amazon Simple Storage Service (Amazon S3) bucket using DVC, a version control tool for ML models.

ML 77

ML Deep Learning Python Auto-classification 77

AWS Machine Learning Blog

MAY 1, 2024

Deploy the solution with the AWS CDK The AWS Cloud Development Kit (AWS CDK) is an open source software development framework for defining cloud infrastructure in code and provisioning it through AWS CloudFormation. He holds a Masters degree in Software Engineering. file for deploying the solution using the AWS CDK.

Chatbots 117

Chatbots Automation Machine Learning DevOps 117

ODSC - Open Data Science

APRIL 12, 2023

Red Hat Since its start with the Red Hat® Enterprise Linux®, Red Hat has expanded its products to include agile integration, management and automation solutions, middleware, cloud-native application development, and hybrid cloud infrastructure. 25 years in, they remain committed to operating transparently, responsibly, and open source.

Data Science 97

Data Science DevOps Deep Learning Software Development 97

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.

Generative AI 92

Generative AI Chatbots Conversational AI Software Development 92

AWS Machine Learning Blog

FEBRUARY 1, 2024

She has a diverse background, having worked in many technical disciplines, including software development, agile leadership, and DevOps, and is an advocate for women in tech. Randy has held a variety of positions in the technology space, ranging from software engineering to product management.

Generative AI 125

Generative AI Prompt Engineer Prompt Engineering AI 125

AWS Machine Learning Blog

OCTOBER 2, 2023

It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. The quality of our labels will affect the quality of our ML model. This three-step process is generic and can be used for any model architecture and ML framework of your choice.

Automation 110

Automation DevOps Machine Learning Metadata 110

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.

ML 118

ML Automation Metadata Software Development 118

AWS Machine Learning Blog

JUNE 11, 2024

This allows machine learning (ML) practitioners to rapidly launch an Amazon Elastic Compute Cloud (Amazon EC2) instance with a ready-to-use deep learning environment, without having to spend time manually installing and configuring the required packages. You also need the ML job scripts ready with a command to invoke them.

Deep Learning 113

Deep Learning ML Automation Auto-complete 113

IBM Journey to AI blog

AUGUST 12, 2024

When enterprises need to build an application, one of the most important decisions their leaders must make is what kind of software development to use. While there are many software architectures to choose from, serverless and microservices architectures are increasingly popular due to their scalability, flexibility and performance.

Software Development 112

Software Development Artificial Intelligence DevOps Artificial Intelligence 112

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.

Python 123

Python Data Scientist Data Science Automation 123

AWS Machine Learning Blog

MARCH 1, 2023

This combination of great models and continuous adaptation is what will lead to a successful machine learning (ML) strategy. MLOps focuses on the intersection of data science and data engineering in combination with existing DevOps practices to streamline model delivery across the ML development lifecycle.

Data Ingestion 87

Data Ingestion DevOps ML Automation 87