Towards AI

FEBRUARY 15, 2024

I definitely recommend watching this one for all learners out here! Axer128 is looking for an HTML5/JavaScript/Python/C++ adept Programmer / DevOps. This week’s podcast episode is extremely useful if you are a student or want to switch to the AI space. I was also curious to know your thoughts on the events world.

AI 98

AI AI DevOps Neural Network 98

The MLOps Blog

JANUARY 23, 2023

Machine Learning Operations (MLOps): Overview, Definition, and Architecture” By Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl Great stuff. If you haven’t read it yet, definitely do so. Lived through the DevOps revolution. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. Some are my 3–4 year bets.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

AWS Machine Learning Blog

OCTOBER 2, 2023

For more details regarding the state machine definition itself, refer to the GitHub repository or check the state machine graph on the Step Functions console after you deploy this example in your account. So if you have a DevOps challenge or want to go for a run: let him know.

Automation 89

Automation DevOps ML Machine Learning 89

O'Reilly Media

OCTOBER 19, 2021

While there isn’t an authoritative definition for the term, it shares its ethos with its predecessor, the DevOps movement in software engineering: by adopting well-defined processes, modern tooling, and automated workflows, we can streamline the process of moving from development to robust production deployments.

DevOps 138

DevOps Software Engineer Data Scientist ML 138

AWS Machine Learning Blog

JANUARY 12, 2023

Finally, the Logstash service consists of a task definition containing a Logstash container and PII redaction container, ensuring the removal of PII prior to exporting to Elasticsearch. For the past 4 years, he has been part of the platform engineering team.

DevOps 106

DevOps Natural Language Processing Metadata Automation 106

AWS Machine Learning Blog

MARCH 26, 2024

The constructs and samples are a collection of components to enable definition of IDP processes on AWS and published to GitHub. His interests and experience include containers, serverless technology, and DevOps. The main concepts used are the AWS CDK constructs, the actual AWS CDK stacks , and AWS Step Functions.

IDP 90

IDP Metadata Data Extraction Automation 90

AWS Machine Learning Blog

MAY 16, 2024

Problem definition Traditionally, the recommendation service was mainly provided by identifying the relationship between products and providing products that were highly relevant to the product selected by the customer. We load tested it with Locust using five g4dn.2xlarge

Deep Learning 86

Deep Learning Auto-complete Algorithm ML 86

AWS Machine Learning Blog

SEPTEMBER 1, 2023

Generative AI definitions and differences to MLOps In classic ML, the preceding combination of people, processes, and technology can help you productize your ML use cases. AppDev and DevOps – They develop the front end (such as a website) of the generative AI application. Only prompt engineering is necessary for better results.

Generative AI 103

Generative AI Prompt Engineer Prompt Engineering AI 103

AWS Machine Learning Blog

JUNE 8, 2023

Solutions Architect at Amazon Web Services with specialization in DevOps and Observability. Prompts function as a form of context that helps direct the model toward generating relevant responses. Outside work, he enjoys fitness, cooking, and spending quality time with friends and family. Mahesh Birardar is a Sr.

Generative AI 73

Generative AI LLM Large Language Models Machine Learning 73

Towards AI

FEBRUARY 10, 2024

Definition of a full-stack data scientist The sibling relationship between data science and software development has led to the borrowing of many concepts from the software development domain into data science practice. It doesn’t even have a commonly agreed-upon definition yet. However, this concept has only been forged very recently.

Data Scientist 107

Data Scientist Software Development Data Science Machine Learning 107

IBM Journey to AI blog

NOVEMBER 29, 2023

To deploy applications onto these varying environments, we have developed a set of robust DevSecOps toolchains to build applications, deploy them to a Satellite location in a secure and consistent manner and monitor the environment using the best DevOps practices. DevSecOps workflows focus on a frequent and reliable software delivery process.

DevOps 249

DevOps Automation Explainability 249

IBM Journey to AI blog

SEPTEMBER 20, 2023

Civil infrastructure enterprises can deploy these FMs at their edge locations and use drone imagery to detect defects in near real-time—accelerating the time-to-insight and reducing the cost of moving large volumes of high-definition data to and from the Cloud.

Large Language Models 225

Large Language Models DevOps AI Modeling Automation 225

Explosion

AUGUST 3, 2017

Most of those insights have been used to make spaCy better: AI DevOps was hard, so we made sure models could be installed via pip. By definition, you can’t directly control what the process returns. Keeping the issue tracker tidy is something many open source projects struggle with – so automated tools could definitely be helpful.

Convolutional Neural Networks 52

Convolutional Neural Networks Python Machine Learning Algorithm 52

AWS Machine Learning Blog

NOVEMBER 9, 2023

Under Advanced Project Options , for Definition , select Pipeline script from SCM. Saswata Dash is a DevOps Consultant with AWS Professional Services. Select This project is parameterized. On the Add Parameter menu, choose String Parameter. For Name , enter prodAccount. For Default Value , enter the prod account ID.

Data Drift 99

Data Drift Auto-complete ML Automation 99

Unite.AI

JUNE 23, 2023

There isn’t one definition of what “the edge” is, it’s actually a spectrum spanning out from the traditional data center or cloud at one end and extending all the way through to highly constrained devices.

DevOps 147

DevOps Automation AI AI 147

Towards AI

AUGUST 16, 2023

Thus, MLOps is the intersection of Machine Learning, DevOps, and Data Engineering (Figure 1). The ideal MLOps engineer would have some experience with several MLOps and/or DevOps platforms. A better definition would make use of the directed acyclic graph (DAG) since it may not be a linear process.

Machine Learning 94

Machine Learning DevOps Software Development ML 94

IBM Journey to AI blog

MARCH 20, 2024

As more integration instances are needed, or an integration needs to be updated, DevOps can then be used to seamlessly safeguard automated operations. This definition can be done through a graphical canvas, a webform or directly in YAML.

Automation 168

Automation DevOps AI AI 168

The MLOps Blog

MARCH 28, 2023

The DevOps and Automation Ops departments are under the infrastructure team. Each time they modify the code, the definition of the pipeline changes. The infrastructure team focuses on technology and delivers tools that other teams will adapt and use to work on their main deliverables. On top of the teams, they also have departments.

Machine Learning 59

Machine Learning Automation Data Scientist ML 59

The MLOps Blog

AUGUST 3, 2023

And then, we’re trying to boot out features of the platform and the open-source to be able to take Hamilton data flow definitions and help you auto-generate the Airflow tasks. So very, very broad, but it’s roots are feature engineering, but definitely very easy to extend to a lightweight end-to-end kind of machine learning model.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

AWS Machine Learning Blog

OCTOBER 16, 2023

Furthermore, DevOps were burdened with manually provisioning GPU instances in response to demand patterns. The ensemble model definition is in the screen_detection_pipeline directory, where inputs and outputs between steps are mapped in a configuration file.

Data Scientist 84

Data Scientist Computer Vision DevOps ML 84

The MLOps Blog

OCTOBER 3, 2023

Mikiko Bazeley: You definitely got the details correct. For me, it was a little bit of a longer journey because I kind of had data engineering and cloud engineering and DevOps engineering in between. I definitely don’t think I’m an influencer. And so what we do is version the definitions. For example, Feast.

ML 52

ML Data Scientist Machine Learning Data Science 52

ODSC - Open Data Science

JULY 19, 2023

Definition of project team users, their roles, and access controls to other resources. Security: We have included steps and best practices from GitHub’s advanced security scanning and credential scanning (also available in Azure DevOps) that can be incorporated into the workflow.

Machine Learning 52

Machine Learning Data Drift Data Science ML Engineer 52

Marktechpost

JULY 24, 2023

Versioned data and Docker enable data scientists and DevOps teams to deploy models confidently. Its XML-based changeset definitions let you operate the database schema on various platforms. .” Your execution environment is packaged by Pachyderm using Docker containers. There are two versions available: open-source and premium.

Machine Learning 52

Machine Learning Metadata Data Science DevOps 52

Bugra Akyildiz

NOVEMBER 20, 2022

AI for DevOps to infuse AI/ML into the entire software development lifecycle to achieve high productivity. DALL·E Flow is an interactive workflow for generating high-definition images from text prompt. Do you have legacy notebooks? Refactor them into modular pipelines with a single command. the prompt.

ML 52

ML ETL Data Science DevOps 52

Snorkel AI

JUNE 22, 2023

Tools like Bigeye, Deequ, and HoloClean are leading the charge here as we see the shift moving from data health and data monitoring to fixing data on the right side—changing data to improve ML and to improve operations, there are definitely risks. Most, most definitely. So, how do you tackle this? Where do we go next?

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JUNE 22, 2023

Tools like Bigeye, Deequ, and HoloClean are leading the charge here as we see the shift moving from data health and data monitoring to fixing data on the right side—changing data to improve ML and to improve operations, there are definitely risks. Most, most definitely. So, how do you tackle this? Where do we go next?

Data Quality 52

Data Quality Automation Data Scientist ML 52

The MLOps Blog

APRIL 17, 2023

Learn more Explainability and Auditability in ML: Definitions, Techniques, and Tools Monitor and evaluate experiment performance for effective decision-making Most of the time, it makes sense to compare the results of experiments done with different dataset versions and parameters. Some will only track the post-training phase.

Metadata 52

Metadata Data Scientist Explainability ML 52

Explosion

JUNE 18, 2021

How implement models ML fundamentals training and evaluation improve accuracy use library APIs Python and DevOps What when to use ML decide what models and components to train understand what application will use outputs for find best trade-offs select resources and libraries The “how” is everything that helps you execute the plan.

NLP 52

NLP Machine Learning Natural Language Processing DevOps 52

The MLOps Blog

MARCH 21, 2023

” — Isaac Vidas , Shopify’s ML Platform Lead, at Ray Summit 2022 Monitoring Monitoring is an essential DevOps practice, and MLOps should be no different. Collaboration The principles you have learned in this guide are mostly born out of DevOps principles. My Story DevOps Engineers Who they are? AIIA MLOps blueprints.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

NOVEMBER 29, 2023

For more information and related component definitions, refer to FMOps/LLMOps: Operationalize generative AI and differences with MLOps. He uses his passion for DevOps, GenAI and builder tools to help both system integrators and technology partners. Jagdeep Singh Soni is a Senior Partner Solutions Architect at AWS based in Netherlands.

LLM 83

LLM Data Scientist Algorithm Generative AI 83

Mlearning.ai

JUNE 16, 2023

Game changer ChatGPT in Software Engineering: A Glimpse Into the Future | HackerNoon Generative AI for DevOps: A Practical View - DZone ChatGPT for DevOps: Best Practices, Use Cases, and Warnings.

Software Development 52

Software Development Generative AI ChatGPT Prompt Engineer 52

AWS Machine Learning Blog

JANUARY 13, 2023

TR’s AI Platform microservices are built with Amazon SageMaker as the core engine, AWS serverless components for workflows, and AWS DevOps services for CI/CD practices. Proper AWS Identity and Access Management (IAM) role definition for the experimentation workspace was hard to define. Bring a single pane of glass for ML activities.

ML 75

ML Data Scientist Machine Learning Metadata 75

The MLOps Blog

JUNE 5, 2023

Stephen: Yeah, absolutely, we’ll definitely delve into that. Why do we have MLOps as opposed to DevOps? And you can rely on whether that’s MLOps or DevOps or whatever it is, building reliable computational flows. Definitely leave those in the show notes as well for the later podcast episode that will be released.

Large Language Models 52

Large Language Models Machine Learning Software Engineer ML 52