AWS Machine Learning Blog

FEBRUARY 27, 2024

Automation of building new projects based on the template is streamlined through AWS Service Catalog , where a portfolio is created, serving as an abstraction for multiple products. Model explainability Model explainability is a pivotal part of ML deployments, because it ensures transparency in predictions.

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Machine Learning DevOps Data Quality Data Scientist 112

Heartbeat

FEBRUARY 1, 2023

MLOps is a highly collaborative effort that aims to manipulate, automate, and generate knowledge through machine learning. They may also be involved in the deployment process’s automation. It can assist you in simplifying and automating the creation and operation of machine-learning models.

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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. It’s much more than just automation. Only a small fraction of a real-world ML use case comprises the model itself.

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Machine Learning ML Engineer DevOps ML 103

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. It is important to resolve incidents immediately and prevent them from happening again.

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Automation Categorization DevOps Explainability 155

Heartbeat

OCTOBER 5, 2023

MLOps, often seen as a subset of DevOps (Development Operations), focuses on streamlining the development and deployment of machine learning models. Where is LLMOps in DevOps and MLOps In MLOps, engineers are dedicated to enhancing the efficiency and impact of ML model deployment. Exciting News!

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Large Language Models DevOps Prompt Engineer Prompt Engineering 52

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. We need both automated continuous monitoring AND periodic manual inspection.

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Marktechpost

JULY 4, 2023

” Even for seasoned programmers, the syntax of shell commands might need to be explained. Regex generation Regular expression generation is time-consuming for developers; however, Autoregex.xyz leverages GPT-3 to automate the process.

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Large Language Models BERT Natural Language Processing Categorization 104

Heartbeat

FEBRUARY 29, 2024

Furthermore, the software development process has evolved to embrace Agile methodologies, DevOps practices, and continuous integration/continuous delivery (CI/CD) pipelines. These tools have evolved to support the demands of modern software engineering, offering features like real-time collaboration, code analysis, and automated testing.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Automated retraining mechanism – The training pipeline built with SageMaker Pipelines is triggered whenever a data drift is detected in the inference pipeline. This will enable us to test the pattern to trigger automated retraining of the model. csv – Will be used to train the first version of model. data/ mammo-train-dataset-part2.csv

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Heartbeat

JULY 31, 2023

Comet provides a built-in hyperparameter optimization feature that automates the process of hyperparameter tuning. It automates the process of hyperparameter tuning, making it easy to find the best hyperparameters for a given machine learning problem. Using Comet saves time and reduces the risk of human error.

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LLM Machine Learning Deep Learning Data Scientist 52

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. This appliance can handle complex use cases out of the box, and it builds the hub-and-spoke framework for centralized management, automation and self-service.

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Large Language Models DevOps AI Modeling Automation 225

IBM Journey to AI blog

NOVEMBER 29, 2023

In this paper, we showcase how to easily deploy a banking application on both IBM Cloud for Financial Services and Satellite , using automated CI/CD/CC pipelines in a common and consistent manner. Details of each docker image is stored in an inventory repository, which is explained in detail in the Continuous Deployment section of this blog.

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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.

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AWS Machine Learning Blog

JUNE 27, 2023

This blog post is co-written with Marat Adayev and Dmitrii Evstiukhin from Provectus. That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in. All steps are run in an automated manner after the pipeline has been run.

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DataRobot Blog

FEBRUARY 11, 2022

In the first part of the “Ever-growing Importance of MLOps” blog, we covered influential trends in IT and infrastructure, and some key developments in ML Lifecycle Automation. These agents apply the concept familiar in the DevOps world—to run models in their preferred environments while monitoring all models centrally.

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Data Drift Machine Learning DevOps Data Scientist 52

The MLOps Blog

JUNE 27, 2023

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. Automated pipelining and workflow orchestration: Platforms should provide tools for automated pipelining and workflow orchestration, enabling you to define and manage complex ML pipelines.

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Machine Learning Metadata Data Quality Data Scientist 134

AWS Machine Learning Blog

OCTOBER 20, 2023

The functional architecture with different capabilities is implemented using a number of AWS services, including AWS Organizations , SageMaker, AWS DevOps services, and a data lake. A framework for vending new accounts is also covered, which uses automation for baselining new accounts when they are provisioned.

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AWS Machine Learning Blog

APRIL 26, 2023

This includes AWS Identity and Access Management (IAM) or single sign-on (SSO) access, security guardrails, Amazon SageMaker Studio provisioning, automated stop/start to save costs, and Amazon Simple Storage Service (Amazon S3) set up. MLOps engineering – Focuses on automating the DevOps pipelines for operationalizing the ML use case.

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The MLOps Blog

APRIL 17, 2023

Explainability : Making sure they can explain their experiment results. This, of course, is not true in all situations, but in circumstances where they need to understand how and why your model makes predictions, “explainability” becomes crucial. Legal compliance is another reason why explainability is essential.

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Metadata Data Scientist Explainability ML 52

AWS Machine Learning Blog

MAY 3, 2023

Moreover, if you have tens and hundreds of Studio users, consider how to automate the recovery process to avoid mistakes and save costs and time. This post explains the backup and recovery module and one approach to automate the process using an event-driven architecture. The rest of the steps are automated.

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Data Scientist Automation DevOps Machine Learning 60

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. He works with enterprise customers to build strategic, well-architected solutions and is passionate about automation. Outside work, he enjoys family time, tennis, cooking and traveling.

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Machine Learning Categorization ML DevOps 79

TensorFlow

JANUARY 18, 2023

In this blog post, we discuss the crucial details of building an end-to-end ML pipeline for Semantic Segmentation tasks with TFX and various Google Cloud services such as Dataflow, Vertex Pipelines, Vertex Training, and Vertex Endpoint. The last one is about automation and implementing CI/CD using GitHub Actions. Hub service.

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AWS Machine Learning Blog

JANUARY 11, 2023

If you’re interested in learning more on this, refer to MLOps foundation roadmap for enterprises with Amazon SageMaker , which explains in detail a framework for model building, training, and deployment following best practices. In this prototype, we follow a fully automated provisioning methodology in accordance with IaC best practices.

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ML Python Automation Machine Learning 66

AWS Machine Learning Blog

AUGUST 29, 2023

In this post, we describe how to create an MLOps workflow for batch inference that automates job scheduling, model monitoring, retraining, and registration, as well as error handling and notification by using Amazon SageMaker , Amazon EventBridge , AWS Lambda , Amazon Simple Notification Service (Amazon SNS), HashiCorp Terraform, and GitLab CI/CD.

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Data Scientist Data Quality Python ML 72

The MLOps Blog

OCTOBER 3, 2023

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. For example, Shopify published a blog post about Merlin. You can explain, “Hey, this is the logic of the function, this is what the terms mean, etc. They’re terrible people.

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ML Data Scientist Machine Learning Data Science 52

IBM Journey to AI blog

MARCH 6, 2024

DevOps Research and Assessment metrics (DORA), encompassing metrics like deployment frequency, lead time and mean time to recover , serve as yardsticks for evaluating the efficiency of software delivery. Developers and IT Operators can speed up application modernization efforts and generate automation to rapidly scale IT environments.

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The MLOps Blog

APRIL 5, 2023

These teams may include but are not limited to data scientists, software developers, machine learning engineers, and DevOps engineers. Save and Load Machine Learning Models in Python with scikit-learn ML Explained – Aggregate Intellect – AI.SCIENCE Model Packaging Overview (NLP + MLOps workshop sneak peak) Brownlee, J.

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ML Machine Learning Deep Learning Metadata 69

Explosion

JUNE 18, 2021

In this blog post, I’m going to discuss some of the biggest challenges for applied NLP and translating business problems into machine learning solutions. This blog post is based on talks I gave at the “Teaching NLP” workshop at NAACL 2021 and the L3-AI online conference.

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NLP Machine Learning Natural Language Processing DevOps 52

Pickl AI

MAY 15, 2024

So if you are looking forward to a Data Science career , this blog will work as a guiding light. Model Interpretability and Explainability While complex models might achieve high accuracy, it’s often challenging to interpret their decision-making processes. Dreaming of a Data Science career but started as an Analyst?

Data Scientist 52

Data Scientist Data Science Machine Learning Data Quality 52

Mlearning.ai

JUNE 16, 2023

Advise on getting started on topics Recommend get started materials Explain an implementation Explain general concepts in specific industry domain (e.g. Advise on getting started on topics Recommend get started materials Explain an implementation Explain general concepts in specific industry domain (e.g.

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Software Development Generative AI ChatGPT Prompt Engineer 52

The MLOps Blog

MAY 9, 2023

The pipelines let you orchestrate the steps of your ML workflow that can be automated. Simplify the end-to-end orchestration of the multiple steps in the machine learning workflow for projects with little to no intervention (automation) from the ML team. Is it a black-box model, or can the decisions be explained?

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ML Machine Learning Metadata Automation 98

The MLOps Blog

MARCH 21, 2023

Automation You want the ML models to keep running in a healthy state without the data scientists incurring much overhead in moving them across the different lifecycle phases. Automation is a good MLOps practice for speeding up all parts of that lifecycle. This would let you roll back changes and inspect potentially buggy code.

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AWS Machine Learning Blog

JANUARY 13, 2023

Standardize building and reuse of AI solutions across business functions and AI practitioners’ personas, while ensuring adherence to enterprise best practices: Automate and standardize the repetitive undifferentiated engineering effort. Ensure the required isolation and control of sensitive data according to common governance standards.

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AWS Machine Learning Blog

OCTOBER 10, 2023

You can use natural language with a conversational chat interface to perform tasks such as creating narratives, reports, and blog posts; answering questions; summarizing notes and articles; and explaining concepts, without writing a single line of code. Gavin Satur is a Principal Solutions Architect at Amazon Web Services.

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The MLOps Blog

JUNE 5, 2023

And you wrote a few blog posts on the next wave of ML infrastructure. To explain that a little further, when you think about what those models are, the way that GPT-3 or the other similar language models are trained is on this corpus of data called the Common Crawl, which is essentially the whole internet, right?

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Large Language Models Machine Learning Software Engineer ML 52