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. Not a fork.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

O'Reilly Media

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. Can’t we just fold it into existing DevOps best practices?

DevOps 138

DevOps Software Engineer Data Scientist ML 138

ODSC - Open Data Science

DECEMBER 11, 2023

This tool is designed to assist development and DevOps teams in the detection and mitigation of security vulnerabilities, with a focus on detecting sensitive secrets and vital credentials that may be exposed inadvertently. Users can provide their own patterns and rules for secret detection.

Software Development 52

Software Development Data Science DevOps Software Engineer 52

AWS Machine Learning Blog

MAY 1, 2024

This post demonstrates how to build a chatbot using Amazon Bedrock including Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock , within an automated solution. Solution overview In this post, we use publicly available data, encompassing both unstructured and structured formats, to showcase our entirely automated chatbot system.

Chatbots 98

Chatbots Automation Machine Learning DevOps 98

AWS Machine Learning Blog

NOVEMBER 7, 2023

However, with the advent of generative AI, we can now automate the crafting of ads that truly resonate with local audiences, all while upholding stringent standards and guidelines. Cost-effective – By automating the creation of tailored ads, companies can significantly reduce costs associated with ad production and revisions.

Large Language Models 93

Large Language Models Generative AI LLM AI 93

Viso.ai

MARCH 29, 2024

MLflow can be seen as a tool that fits within the MLOps (synonymous with DevOps) framework. Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments. ML Tasks – source Automating these ML lifecycle steps is extremely difficult.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

AWS Machine Learning Blog

AUGUST 15, 2023

Content moderation automation allows Amazon to scale the process while keeping high accuracy. Rekognition Content Moderation automates and streamlines image and video moderation workflows without requiring ML experience. Combined with the DevOps efficiency gains, the Amazon Shopping team achieved significant cost savings.

DevOps 76

DevOps Computer Vision Automation ML 76

AWS Machine Learning Blog

JUNE 21, 2023

You can use lifecycle configurations to automate customization for your Studio environment. This enables you to apply DevOps best practices and meet safety, compliance, and configuration standards across all AWS accounts and Regions. About the Authors Cory Hairston is a Software Engineer with the Amazon ML Solutions Lab.

Python 80

Python Data Scientist ML DevOps 80

AWS Machine Learning Blog

APRIL 26, 2023

Commonly, the work is split up between the following workstreams: Cloud engineering (infrastructure and security) – Focuses on verifying that the AWS accounts and infrastructure are set up and secure ahead of EBA. Data engineering – Identifies the data sources, sets up data ingestion and pipelines, and prepares data using Data Wrangler.

ML 88

ML Machine Learning Data Science Data Drift 88

ODSC - Open Data Science

JULY 19, 2023

The data science team is now expected to be equipped with CI/CD skills to sustain ongoing inference with retraining cycles and automated redeployments of models. The people associated with this phase are primarily ML Engineers. This manual step can ensure that the developed model adheres to the responsible AI principles.

Machine Learning 52

Machine Learning Data Drift Data Science ML Engineer 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.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

Viso.ai

MARCH 1, 2023

This trend represents a step towards the decades-long goal in computer science to automate manual coding. Hence, d evelopment platforms that reduce code and automate development tasks for developers have recently started to become w idely adopted. This article will cover the following topics: What are no-code and low-code platforms?

Computer Vision 52

Computer Vision Software Development AI AI 52

The MLOps Blog

OCTOBER 3, 2023

I started from tech, my first job was an internship at Google as a software engineer. I’m from Poland, and I remember when I got an offer from Google to join as a regular software engineer. I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps.

ML 52

ML Data Scientist Machine Learning Data Science 52

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.

Data Scientist 72

Data Scientist Data Quality Python ML 72

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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Generative AI DevOps Software Development Auto-complete 293

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

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. However, writing test is time-consuming, often ignored which causes bad software quality.

Software Development 52

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. It is most common to use containers for machine learning pipelines.

ML 98

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.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

The MLOps Blog

JUNE 5, 2023

One of the hardest things about MLOps today is that a lot of data scientists aren’t native software engineers, but it may be possible to lower the bar to software engineering. I’ve seen tools that help you write and author pull requests more efficiently, and that help automate building documentation.

Large Language Models 52

Large Language Models Machine Learning Software Engineer ML 52