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

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.

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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?

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Machine Learning ML DevOps Software Engineer 102

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 88

Machine Learning Software Development Data Drift AI 88

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 Automation 97

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.

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ML Deep Learning Python Auto-classification 75

Heartbeat

FEBRUARY 29, 2024

The software development landscape is constantly evolving, driven by technological advancements and the ever-growing demands of the digital age. Over the years, we’ve witnessed significant milestones in programming languages, each bringing about transformative changes in how we write code and build software systems.

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Software Engineer Software Development LLM Large Language Models 52

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.,

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Data Science DevOps Deep Learning Automation 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 98

Generative AI Chatbots Conversational AI Software Development 98

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.

Automation 89

Automation DevOps ML Machine Learning 89

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

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.

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DevOps Metadata Software Engineer Data Scientist 59

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 100

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.

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Automation DevOps Machine Learning Metadata 92

IBM Journey to AI blog

NOVEMBER 13, 2023

Overview of Kubernetes Containers —lightweight units of software that package code and all its dependencies to run in any environment—form the foundation of Kubernetes and are mission-critical for modern microservices, cloud-native software and DevOps workflows.

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DevOps Automation Software Development Large Language Models 291

ODSC - Open Data Science

DECEMBER 11, 2023

Piiano Flows focuses on PII and other sensitive fields used within the code and uses AI and ML to provide full and greater coverage. GitGurdian GitGuardian is a strong tool that maintains the safety of software development by keeping confidential data in code repositories. It also boasts of significant language support.

Software Development 52

Software Development Data Science DevOps Software Engineer 52

AWS Machine Learning Blog

OCTOBER 18, 2023

Purina used artificial intelligence (AI) and machine learning (ML) to automate animal breed detection at scale. The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring. DynamoDB is used to store the pet attributes.

Auto-complete 94

Auto-complete Auto-classification Automation ML 94

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. Can’t we just fold it into existing DevOps best practices? What does a modern technology stack for streamlined ML processes look like?

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DevOps Software Engineer Data Scientist ML 138

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.

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ML DevOps Data Scientist ML Engineer 95

Mlearning.ai

AUGUST 12, 2023

In the world of software development, it’s crucial to ensure that applications behave as expected in various environments. Docker, a… Continue reading on MLearning.ai »

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Software Development Python DevOps ML 52

AWS Machine Learning Blog

FEBRUARY 13, 2023

With the increasing use of artificial intelligence (AI) and machine learning (ML) for a vast majority of industries (ranging from healthcare to insurance, from manufacturing to marketing), the primary focus shifts to efficiency when building and training models at scale. On the AWS CloudFormation console, delete the CloudFormation stack.

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ML Automation Machine Learning Artificial Intelligence 73

Allen AI

APRIL 3, 2023

As we upgrade our server hardware to keep up with the latest GPUs needed for training large ML models, we’ve recently managed to max out the power capacity available in our offices! Everyday I learn more about IT, DevOps, and software development.

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AWS Machine Learning Blog

FEBRUARY 16, 2023

To learn about these possibilities and more, refer to the Amazon Kendra Developer Guide. About the authors Rishabh Yadav is a Partner Solutions architect at AWS with an extensive background in DevOps and Security offerings at AWS. Kruthi Jayasimha Rao is a Partner Solutions Architect with a focus in AI and ML.

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DevOps Natural Language Processing Software Development Algorithm 69

Mlearning.ai

MARCH 16, 2023

You have probably heard the term DevOps in conventional software development. While working on different ML research projects and ideas I realized that in every project I pretty much reinvented the wheel in regards to MLOps. Is there a way or a method or a tool that can help bring order to this chaotic situation?

Machine Learning 52

Machine Learning DevOps Software Development Data Scientist 52

AWS Machine Learning Blog

NOVEMBER 7, 2023

Amazon Kendra with foundational LLM Amazon Kendra is an advanced enterprise search service enhanced by machine learning (ML) that provides out-of-the-box semantic search capabilities. Semantic understanding – The ML capabilities of Amazon Kendra ensure that retrieval is based on deep semantic understanding and not just keyword matches.

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Large Language Models Generative AI LLM AI 94

AWS Machine Learning Blog

JUNE 8, 2023

This enables you to begin machine learning (ML) quickly. A SageMaker real-time inference endpoint enables fast, scalable deployment of ML models for predicting events. Victor Rojo is a highly experienced technologist who is passionate about the latest in AI, ML, and software development. Mahesh Birardar is a Sr.

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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.

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ML Machine Learning Deep Learning Metadata 69

Mlearning.ai

DECEMBER 21, 2023

Design patterns in software engineering are typical solutions to common problems in software design. They represent best practices, evolved over time, and are a toolkit for software developers to solve common problems efficiently. class Singleton: _instance = None def __new__(cls): if cls.

Software Engineer 52

Software Engineer Software Development DevOps Algorithm 52

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 94

Machine Learning DevOps Software Development ML 94

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 91

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. MLflow can be seen as a tool that fits within the MLOps (synonymous with DevOps) framework. To learn more, book a demo.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

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.

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DevOps Automation Software Development Big Data 273

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 98

Chatbots Automation Machine Learning DevOps 98

The MLOps Blog

SEPTEMBER 7, 2023

Writing legible, reusable, and efficient code has always been a challenge in the software development community. How to best adapt SOLID principles, how to make use of effective software patterns, how to give the most appropriate names to functions and classes, how to organize code modules, etc.

Software Engineer 52

Software Engineer Machine Learning ETL ML Engineer 52

Mlearning.ai

AUGUST 18, 2023

My interpretation to MLOps is similar to my interpretation of DevOps. As a software engineer your role is to write code for a certain cause. DevOps cover all of the rest, like deployment, scheduling of automatic tests on code change, scaling machines to demanding load, cloud permissions, db configuration and much more.

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DevOps Python ML ML Engineer 52

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.

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Data Ingestion DevOps Automation ML 84

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.

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Generative AI Prompt Engineer Prompt Engineering AI 112