IBM Journey to AI blog

SEPTEMBER 12, 2023

In today’s complex and dynamic environments, traditional manual approaches fall short in delivering the agility, accuracy and scalability demanded by site reliability engineering (SRE) and DevOps practices. “We’re accomplishing the goal that we set out to do,” Fite explains.

Automation 206

Automation DevOps Machine Learning Explainability 206

IBM Journey to AI blog

FEBRUARY 29, 2024

The system uses Docker images, which are read-only templates that are used for building containers, and Dockerfiles, which are text files that accompany and explain Docker images. Docker images and other container images require a space in which to run.

DevOps 267

DevOps Software Development Artificial Intelligence Artificial Intelligence 267

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 280

DevOps Automation Explainability 280

AWS Machine Learning Blog

NOVEMBER 7, 2024

Technical Info: Provide part specifications, features, and explain component functions. He has over 6 years of experience in helping customers architecting a DevOps strategy for their cloud workloads. Your main tasks are: Part Identification: Find specific parts based on vehicle details (make, model, year).

DevOps 129

DevOps Generative AI Python Automation 129

Unite.AI

SEPTEMBER 30, 2024

This article explains how AI in quality assurance streamlines software testing while improving product performance. AI-powered QA is also becoming central to DevOps. Unsurprisingly, Gartner reports that 88% of service leaders feel that today’s QA approaches don’t meet the mark. What is AI-powered Quality Assurance?

Automation 317

Automation AI AI DevOps 317

IBM Journey to AI blog

JUNE 26, 2023

Rob High explained the increasing importance of running applications in non-traditional places and on non-traditional devices, which we also often call “on the edge.” In short, hybrid cloud impacts every aspect of where and how we run IT solutions. The only guest I had who is not an IBM Fellow is Naeem Altaf.

DevOps 180

DevOps Explainability Automation AI 180

IBM Journey to AI blog

AUGUST 11, 2023

The paper suggested creating a systematic “MLOps” process that incorporated CI/CD methodology commonly used in DevOps to essentially create an assembly line for each step. MLOps vs. DevOps DevOps is the process of delivering software by combining and automating the work of software development and IT operations teams.

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

Unite.AI

JANUARY 15, 2024

In this CodePal review, I'll explain what CodePal is and who it's best for. From there, I'll briefly explain each of its tools so you know what it's capable of. However, I hope that by briefly explaining what each tool does, you'll better grasp what CodePal is capable of while finding the tool you're looking for more quickly.

Explainability 147

Explainability DevOps AI AI 147

The MLOps Blog

JANUARY 23, 2023

Lived through the DevOps revolution. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. Not a fork: – The MLOps team should consist of a DevOps engineer, a backend software engineer, a data scientist, + regular software folks. Model monitoring tools will merge with the DevOps monitoring stack. Not a fork.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

AWS Machine Learning Blog

DECEMBER 3, 2024

The analysis results in a summarized alert intelligence report that identifies and explains root causes of checkout service issues. It examines service performance metrics, forecasts of key indicators like error rates, error patterns and anomalies, security alerts, and overall system status and health.

DevOps 106

DevOps AI AI ML 106

Towards AI

JULY 20, 2023

Why I should have opted for Visual Studio Code for DevOps Visual Studio 2019 is one of the best tools on the market for building applications. From my own experience, this is overkill for a variety of reasons that I will explain in detail. I developed my very first web… Read the full blog for free on Medium.

DevOps 98

DevOps Explainability AI AI 98

Unite.AI

SEPTEMBER 25, 2024

The funding round was led by Flint Capital and Glilot Capital Partners , with notable industry figures such as Yochay Ettun, CEO of cnvrg.io (acquired by Intel), and Raz Shaked, Head of DevOps at Wiz, among the investors. Traditional Infrastructure as Code (IaC) tools, like Terraform, often struggle to scale efficiently in such setups.

DevOps 130

DevOps Automation Explainability Generative AI 130

IBM Journey to AI blog

SEPTEMBER 20, 2023

IBM watsonx.governance is an end-to-end automated AI lifecycle governance toolkit that is built to enable responsible, transparent and explainable AI workflows. IBM watsonx.data is a fit-for-purpose data store built on an open lakehouse architecture to scale AI workloads for all of your data, anywhere.

Large Language Models 238

Large Language Models DevOps Data Science AI Modeling 238

AWS Machine Learning Blog

FEBRUARY 27, 2024

Model explainability Model explainability is a pivotal part of ML deployments, because it ensures transparency in predictions. Pavel Maslov is a Senior DevOps and ML engineer in the Analytic Platforms team. For a detailed understanding, we use Amazon SageMaker Clarify.

Machine Learning 137

Machine Learning DevOps Data Scientist Data Quality 137

IBM Journey to AI blog

OCTOBER 25, 2023

It can also aid in platform engineering, for example by generating DevOps pipelines and middleware automation scripts. The tools in watsonx.governance will also help organizations efficiently drive responsible, transparent and explainable workflows across the business.

Generative AI 243

Generative AI Automation ESG AI 243

Unite.AI

NOVEMBER 9, 2023

Our platform today leverages the power of AI to enhance detection of risks, simplify investigations, and speed up remediation – saving cloud security, DevOps, and development teams time and effort, while significantly improving security outcomes. Can you explain how Orca leverages AI and what benefits it brings?

DevOps 162

DevOps Explainability Responsible AI Automation 162

O'Reilly Media

FEBRUARY 25, 2025

So instead I spent all those years working on a versatile code visualizer that could be *used* by human tutors to explain code execution. In particular, theyre great at generating and explaining small pieces of self-contained code (e.g., Add code comments to explain your changes. and Explain what this code does line-by-line.

Generative AI 60

Generative AI Python LLM AI 60

AWS Machine Learning Blog

JANUARY 5, 2024

Data science and DevOps teams may face challenges managing these isolated tool stacks and systems. AWS also helps data science and DevOps teams to collaborate and streamlines the overall model lifecycle process. This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice.

Data Science 133

Data Science Data Drift DevOps Auto-complete 133

Unite.AI

DECEMBER 7, 2023

It offers coding guidance, explaining the why behind a flagged issue, and how to fix it, to ensure that the code being written is MISRA-compliant. Consistent code quality is something every manager or technical director aims to maintain. What is your vision for how AI will transform coding in the future?

Software Development 243

Software Development DevOps Explainability AI 243

Becoming Human

MAY 11, 2023

DevOps and DataOps: DevOps and DataOps are related approaches that emphasize collaboration between software developers and IT operations teams. DevOps focuses on automating the software development and deployment process, while DataOps focuses on the data management process.

Machine Learning 78

Machine Learning DevOps Software Development Automation 78

Heartbeat

FEBRUARY 1, 2023

Comparing MLOps and DevOps DevOps is a software development method that brings together multiple teams to organize and conspire to create more efficient and reliable products. One thing that DevOps and MLOps have in common is that they both emphasize process automation. Learn more lessons from the field with Comet experts.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

AWS Machine Learning Blog

DECEMBER 19, 2023

Machine learning operations (MLOps) applies DevOps principles to ML systems. Just like DevOps combines development and operations for software engineering, MLOps combines ML engineering and IT operations. Conclusion In summary, MLOps is critical for any organization that aims to deploy ML models in production systems at scale.

Machine Learning 132

Machine Learning ML Engineer DevOps ML 132

AWS Machine Learning Blog

JUNE 27, 2023

That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in. They needed a cloud platform and a strategic partner with proven expertise in delivering production-ready AI/ML solutions, to quickly bring EarthSnap to the market.

DevOps 115

DevOps ML Machine Learning ML Engineer 115

AWS Machine Learning Blog

MARCH 3, 2025

Prior to AWS, he worked as a DevOps architect in the e-commerce industry for over 5 years, following a decade of R&D work in mobile internet technologies. Before migrating any of the provided architecture to production, we recommend following the AWS Well-Architected Framework. He serves as a technical advisor to startups building on AWS.

Conversational AI 72

Conversational AI Deep Learning AI AI 72

AWS Machine Learning Blog

MARCH 7, 2024

We also explained each element of the solution in detail. He is a technology enthusiast and a builder with a core area of interest on generative AI, serverless, and DevOps. Outside of work, he enjoys watching shows, traveling, and music.

Automation 142

Automation Generative AI DevOps Explainability 142

AWS Machine Learning Blog

OCTOBER 9, 2023

Next, we explain how to review the trained model for performance. Finally, we explain how to use the trained model to perform predictions. His expertise spans application architecture, DevOps, serverless, and machine learning. Aaqib Bickiya is a Solutions Architect at Amazon Web Services based in Southern California.

Machine Learning 113

Machine Learning Categorization ML DevOps 113

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 98

Machine Learning Software Development Data Drift Data Science 98

IBM Journey to AI blog

AUGUST 1, 2023

This explains the existence of both incident and problem management, two important processes for issue and error control, maintaining uptime, and ultimately, delivering a great service to customers and other stakeholders.

Automation 179

Automation Categorization DevOps Explainability 179

ODSC - Open Data Science

SEPTEMBER 7, 2023

Let me explain using an easy analogy. He was a member of the Teqnation program committee, did a presentation on Kafka and Hue usage during football, developing and deploying on Hololens, Total Devops using Gitlab, Evolution of a data science product, using the elastic stack from PoC to Production, Xbox Kinect on a bike at Devoxx London.

Data Science 97

Data Science DevOps Explainability AI 97

AWS Machine Learning Blog

JULY 8, 2024

These ML models ultimately helped TWCo create predictive, privacy-friendly experiences that improved user experience and explains how weather conditions impact consumers’ daily planning or business operations. We also reviewed the architecture design that helps maintain responsibilities between different users modularized.

Data Scientist 121

Data Scientist ML Engineer Machine Learning Data Science 121

AWS Machine Learning Blog

FEBRUARY 2, 2024

We calculate the following information based on the clustering output shown in the following figure: The number of dimensions in PCA that explain 95% of the variance The location of each cluster center, or centroid Additionally, we look at the proportion (higher or lower) of samples in each cluster, as shown in the following figure.

ETL 125

ETL DevOps LLM Generative AI 125

AWS Machine Learning Blog

MAY 31, 2024

Explain first before answering.", "parameters": { "max_new_tokens": 200, "do_sample": True, "top_p": 0.9, "temperature": 0.6, "return_full_text": False }, } response = predictor.predict(payload)[0]["generated_text"].strip() strip() print(response) The following is the output: Sure, I'll explain the process first before giving the answer.

Python 119

Python Machine Learning Auto-classification ML 119

AWS Machine Learning Blog

DECEMBER 5, 2023

Enterprises need a responsible and safer way to send sensitive information to the models without needing to take on the often prohibitively high overheads of on-premises DevOps. The process for creating the transformation for fine-tuning datasets is the same as that explained in the solution architecture section earlier in this post.

LLM 127

LLM AI AI Generative AI 127

Mlearning.ai

AUGUST 18, 2023

TL;DR This series explain how to implement intermediate MLOps with simple python code, without introducing MLOps frameworks (MLflow, DVC …). My interpretation to MLOps is similar to my interpretation of DevOps. If you rather jump straight to the code, here’s the repository [link]. Replace MLOps with program .Source

DevOps 52

DevOps Python ML ML Engineer 52

AWS Machine Learning Blog

APRIL 26, 2023

MLOps engineering – Focuses on automating the DevOps pipelines for operationalizing the ML use case. Data science – The heart of ML EBA and focuses on feature engineering, model training, hyperparameter tuning, and model validation. This may often be the same team as cloud engineering.

ML 115

ML Machine Learning Data Science Data Drift 115

DataRobot Blog

FEBRUARY 11, 2022

These agents apply the concept familiar in the DevOps world—to run models in their preferred environments while monitoring all models centrally. All models built within DataRobot MLOps support ethical AI through configurable bias monitoring and are fully explainable and transparent. Governance and Trust.

Data Drift 52

Data Drift Machine Learning DevOps Data Scientist 52

The MLOps Blog

JUNE 27, 2023

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. Truera offers capabilities such as model debugging, explainability, and fairness assessment to gain insights into model behavior and identify potential issues or biases. Learn more from the documentation.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134