AWS Machine Learning Blog

JANUARY 5, 2024

Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems.

Data Science 132

Data Science Data Drift DevOps Auto-complete 132

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. Data Science Layers.

DevOps 144

DevOps Software Engineer Data Scientist ML 144

Flipboard

NOVEMBER 22, 2024

For example, in the bank marketing use case, the management account would be responsible for setting up the organizational structure for the bank’s data and analytics teams, provisioning separate accounts for data governance, data lakes, and data science teams, and maintaining compliance with relevant financial regulations.

ML 128

ML Data Science Metadata DevOps 128

Unite.AI

JUNE 19, 2024

MLOps, or Machine Learning Operations, is a multidisciplinary field that combines the principles of ML, software engineering, and DevOps practices to streamline the deployment, monitoring, and maintenance of ML models in production environments. ML Operations : Deploy and maintain ML models using established DevOps practices.

Software Engineer 182

Software Engineer DevOps Machine Learning ML 182

ODSC - Open Data Science

APRIL 12, 2023

In the weeks since we announced our first group of partners for ODSC East 2023 , we’ve added even more industry-leading organizations and startups helping to shape the future of AI and data science for enterprise. Through automating part of the process JetBrain frees developers to focus on growing, developing and creating.

Data Science 97

Data Science DevOps Deep Learning Software Development 97

IBM Journey to AI blog

JUNE 8, 2023

Krista Software helps Zimperium automate operations with IBM Watson Vamsi Kurukuri, VP of Site Reliability at Zimperium, developed a strategy to remove roadblocks and pain points in Zimperium’s deployment process. Once all parties approve the release, Krista then deploys it.

DevOps 214

DevOps Automation Machine Learning NLP 214

IBM Journey to AI blog

SEPTEMBER 20, 2023

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. IBM watsonx.governance is an end-to-end automated AI lifecycle governance toolkit that is built to enable responsible, transparent and explainable AI workflows.

Large Language Models 238

Large Language Models DevOps Data Science AI Modeling 238

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 129

Chatbots Automation Machine Learning DevOps 129

Pickl AI

APRIL 17, 2023

How can a DevOps team take advantage of Artificial Intelligence (AI)? DevOps is mainly the practice of combining different teams including development and operations teams to make improvements in the software delivery processes. So now, how can a DevOps team take advantage of Artificial Intelligence (AI)?

DevOps 52

DevOps Artificial Intelligence Artificial Intelligence Machine Learning 52

AWS Machine Learning Blog

FEBRUARY 27, 2024

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.

Machine Learning 137

Machine Learning DevOps Data Scientist Data Quality 137

Marktechpost

JULY 5, 2024

Programming for Data Science with Python This course series teaches essential programming skills for data analysis, including SQL fundamentals for querying databases and Unix shell basics. Students also learn Python programming, from fundamentals to data manipulation with NumPy and Pandas, along with version control using Git.

Data Science 60

Data Science Machine Learning Data Analysis Software Engineer 60

AWS Machine Learning Blog

NOVEMBER 26, 2024

With lifecycle configurations, system administrators can apply automated controls to their SageMaker Studio domains and their users. These automations can greatly decrease overhead related to ML project setup, facilitate technical consistency, and save costs related to running idle instances.

Data Scientist 97

Data Scientist Python Automation ML 97

ODSC - Open Data Science

JANUARY 31, 2023

Getting insight into your system allows you to detect and resolve issues quickly — and it’s an essential part of DevOps best practices. Proactively monitoring systems and gathering data allows AI to provide insights that enable organizations to predict risks and prevent problems from occurring.

DevOps 105

DevOps Machine Learning Data Science Automation 105

ODSC - Open Data Science

DECEMBER 1, 2023

Over the course of 3+ hours, you’ll learn How to take your machine learning model from experimentation to production How to automate your machine learning workflows by using GitHub Actions. You can also get data science training on-demand wherever you are with our Ai+ Training platform.

Data Science 52

Data Science DevOps Machine Learning Large Language Models 52

Pickl AI

JANUARY 30, 2023

Since the rise of Data Science, it has found several applications across different industrial domains. However, the programming languages that work at the core of Data Science play a significant role in it. Hence for an individual who wants to excel as a data scientist, learning Python is a must.

Python 97

Python DevOps Machine Learning Data Science 97

Flipboard

NOVEMBER 24, 2023

This offering enables BMW ML engineers to perform code-centric data analytics and ML, increases developer productivity by providing self-service capability and infrastructure automation, and tightly integrates with BMW’s centralized IT tooling landscape. A data scientist team orders a new JuMa workspace in BMW’s Catalog.

ML 146

ML DevOps Data Scientist ML Engineer 146

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.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

DataRobot Blog

SEPTEMBER 1, 2022

You need full visibility and automation to rapidly correct your business course and to reflect on daily changes. Imagine yourself as a pilot operating aircraft through a thunderstorm; you have all the dashboards and automated systems that inform you about any risks. How long will it take to replace the model? Request a Demo.

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Data Drift Data Science AI AI 145

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.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

IBM Journey to AI blog

JULY 11, 2024

AI can also provide actionable recommendations to address issues and augment incomplete or inconsistent data, leading to more accurate insights and informed decision-making. Developments in machine learning , automation and predictive analytics are helping operations managers improve planning and streamline workflows.

Artificial Intelligence 122

Artificial Intelligence Artificial Intelligence Automation DevOps 122

AWS Machine Learning Blog

JULY 8, 2024

MLOps practitioners have many options to establish an MLOps platform; one among them is cloud-based integrated platforms that scale with data science teams. TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively.

Data Scientist 120

Data Scientist ML Engineer Machine Learning Data Science 120

AWS Machine Learning Blog

OCTOBER 28, 2024

For automated alerts for model monitoring, creating an Amazon Simple Notification Service (Amazon SNS) topic is recommended, which email user groups will subscribe to for alerts on a given CloudWatch metric alarm. He is a technology enthusiast and a builder with a core area of interest in AI/ML, data analytics, serverless, and DevOps.

ML 113

ML Metadata Data Scientist DevOps 113

AWS Machine Learning Blog

OCTOBER 2, 2023

An MLOps pipeline allows to automate the full ML lifecycle from data labeling to model training and deployment. Implementing an MLOps pipeline at the edge introduces additional complexities that make the automation, integration, and maintenance processes more challenging due to the increased operational overhead involved.

DevOps 121

DevOps Automation ML Computer Vision 121

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. Model Training Frameworks This stage involves the process of creating and optimizing predictive models with labeled and unlabeled data.

Data Drift 124

Data Drift Machine Learning Data Scientist ML 124

AWS Machine Learning Blog

OCTOBER 3, 2024

This approach led to data scientists spending more than 50% of their time on operational tasks, leaving little room for innovation, and posed challenges in monitoring model performance in production. To meet this demand amidst rising claim volumes, Aviva recognizes the need for increased automation through AI technology.

DevOps 93

DevOps Data Science Data Scientist ML 93

IBM Journey to AI blog

JULY 11, 2024

AI can also provide actionable recommendations to address issues and augment incomplete or inconsistent data, leading to more accurate insights and informed decision-making. Developments in machine learning , automation and predictive analytics are helping operations managers improve planning and streamline workflows.

Artificial Intelligence 100

Artificial Intelligence Artificial Intelligence Automation DevOps 100

AWS Machine Learning Blog

OCTOBER 23, 2024

Roy Gunter , DevOps Engineer at Curriculum Advantage, manages cloud infrastructure and automation for Classworks. She is a technologist with a PhD in Computer Science, a master’s degree in Education Psychology, and years of experience in data science and independent consulting in AI/ML.

DevOps 89

DevOps Generative AI LLM Large Language Models 89

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 Scientist Data Quality 134

Flipboard

MARCH 7, 2023

You can implement comprehensive tests, governance, security guardrails, and CI/CD automation to produce custom app images. Implement an automated image authoring process As already mentioned, you can use the Studio Image Build CLI to implement an automated CI/CD process of app image creation and deployment with CodeBuild and sm-docker CLI.

Python 123

Python Data Science Data Scientist Automation 123

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.

ML 132

ML Data Scientist ML Engineer Data Science 132

AWS Machine Learning Blog

OCTOBER 16, 2023

They provide advanced technology that combines AI-powered automation with human feedback, deep insights, and expertise. Although the solution did alleviate GPU costs, it also came with the constraint that data scientists needed to indicate beforehand how much GPU memory their model would require.

Data Scientist 114

Data Scientist Computer Vision DevOps ML 114

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.

ML 114

ML Machine Learning Data Science Data Drift 114

AWS Machine Learning Blog

AUGUST 31, 2023

In addition to data engineers and data scientists, there have been inclusions of operational processes to automate & streamline the ML lifecycle. Depending on your governance requirements, Data Science & Dev accounts can be merged into a single AWS account.

ML 125

ML Data Scientist Machine Learning Data Science 125

AWS Machine Learning Blog

AUGUST 22, 2024

As you move from pilot and test phases to deploying generative AI models at scale, you will need to apply DevOps practices to ML workloads. Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance.

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Generative AI Metadata Python ML 127

AWS Machine Learning Blog

MARCH 1, 2023

MLOps focuses on the intersection of data science and data engineering in combination with existing DevOps practices to streamline model delivery across the ML development lifecycle. MLOps requires the integration of software development, operations, data engineering, and data science.

Data Ingestion 98

Data Ingestion DevOps ML Automation 98

AWS Machine Learning Blog

FEBRUARY 13, 2023

The creation of a scalable and hassle-free data science environment is key. Toolkits to automate the infrastructure become essential for horizontal scaling of AI/ML efforts within a corporation. In the past, AWS CloudFormation templates have been created to automate the creation of these services.

ML 97

ML Machine Learning Data Science Automation 97

Marktechpost

JULY 17, 2023

Data gathering, pre-processing, modeling, and deployment are all steps in the iterative process of predictive analytics that results in output. We can automate the procedure to deliver forecasts based on new data continuously fed throughout time. The business offers hundreds of tools for different industries.

Machine Learning 98

Machine Learning Data Mining Data Scientist Data Science 98