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. The team sets up an automated pipeline for continuous integration and deployment.

Software Engineer 52

Software Engineer Software Development DevOps Algorithm 52

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

AWS Machine Learning Blog

OCTOBER 22, 2024

It accelerates your generative AI journey from prototype to production because you don’t need to learn about specialized workflow frameworks to automate model development or notebook execution at scale. She has a decade of experience in DevOps, infrastructure, and ML. Brock Wade is a Software Engineer for Amazon SageMaker.

Automation 82

Automation LLM DevOps Python 82

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 127

Chatbots Automation Machine Learning DevOps 127

IBM Journey to AI blog

AUGUST 11, 2023

MLOps, which stands for machine learning operations, uses automation, continuous integration and continuous delivery/deployment (CI/CD) , and machine learning models to streamline the deployment, monitoring and maintenance of the overall machine learning system. How to use ML to automate the refining process into a cyclical ML process.

Data Science 188

Data Science DevOps Machine Learning Data Scientist 188

Heartbeat

FEBRUARY 1, 2023

MLOps is a highly collaborative effort that aims to manipulate, automate, and generate knowledge through machine learning. The machine learning engineers are in charge of taking the models developed by data scientists and deploying them into production. They may also be involved in the deployment process’s automation.

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. It’s much more than just automation.

Machine Learning 129

Machine Learning ML Engineer DevOps ML 129

Marktechpost

JULY 5, 2024

The curriculum also includes classical search, automated planning, and probabilistic graphical models for comprehensive AI training. It covers advanced topics, including scikit-learn for machine learning, statistical modeling, software engineering practices, and data engineering with ETL and NLP pipelines.

Data Science 64

Data Science Machine Learning Software Engineer Data Analysis 64

Marktechpost

OCTOBER 27, 2024

Best Use Cases: Data science, machine learning, artificial intelligence, web development, automation, and scientific computing. Best Use Cases: Game development, Windows applications, web development (ASP.NET), and enterprise software. It’s used in system programming, network programming, cloud computing, and DevOps.

Data Analysis 135

Data Analysis Big Data Natural Language Processing Machine Learning 135

Unite.AI

JUNE 24, 2024

It combines principles from DevOps, such as continuous integration, continuous delivery, and continuous monitoring, with the unique challenges of managing machine learning models and datasets. As the adoption of machine learning in various industries continues to grow, the demand for robust MLOps tools has also increased.

Data Drift 124

Data Drift Machine Learning Data Scientist DevOps 124

AWS Machine Learning Blog

NOVEMBER 7, 2024

Operating such large-scale forecasting requires resilient, reusable, reproducible, and automated machine learning (ML) workflows with fast experimentation and continuous improvements. At the same time, we incorporated continuous integration and delivery (CI/CD) processes, automated testing, and model versioning into the solution.

ML 111

ML Machine Learning Automation Data Scientist 111

AWS Machine Learning Blog

JUNE 11, 2024

This feature streamlines the process of launching new instances with the most up-to-date Neuron SDK, enabling you to automate your deployment workflows and make sure you’re always using the latest optimizations. Anant Sharma is a software engineer at AWS Annapurna Labs specializing in DevOps.

Deep Learning 123

Deep Learning ML Automation Auto-complete 123

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 100

Python Data Scientist DevOps ML 100

AWS Machine Learning Blog

JULY 25, 2024

Setting up proper test cases and automating the evaluation process can be difficult due to the conversational and dynamic nature of agent interactions. This rigorous testing approach is vital for maintaining the reliability and consistency of conversational AI agents as they progress through the software delivery lifecycle.

Conversational AI 130

Conversational AI Machine Learning AI AI 130

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

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 116

ML Machine Learning Data Science Data Drift 116

AWS Machine Learning Blog

JULY 8, 2024

This automated solution helps you get started quickly by providing all the code and configuration necessary to generate your unique images—all you need is images of your subject. However, this solution uses the equivalent GUI parameters as a pre-configured TOML file to automate the entire Stable Diffusion XL fine-tuning process.

Automation 124

Automation ML DevOps Software Engineer 124

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

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

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 121

Large Language Models Generative AI LLM AI 121

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 85

Data Scientist Data Quality Python ML 85

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.

Metadata 134

Metadata Machine Learning Data Scientist Data Quality 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 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 89

DevOps Computer Vision Automation ML 89

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

Unite.AI

OCTOBER 18, 2024

As companies grapple with the complexities of cloud-native architectures, microservices, and the need for rapid deployment, IDPs offer a solution that streamlines workflows, automates repetitive tasks, and empowers developers to focus on what they do best – writing code.

IDP 162

IDP DevOps Automation Software Development 162

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

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.

Generative AI 335

Generative AI DevOps Software Development Auto-complete 335

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

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

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 Natural Language Processing 52