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Data Science DevOps ML 
Analytics Vidhya
MAY 30, 2022
This article was published as a part of the Data Science Blogathon. Docker is a DevOps tool and is very popular in the DevOps and MLOPS world. The post A Complete Guide for Deploying ML Models in Docker appeared first on Analytics Vidhya.
374 
NOVEMBER 22, 2024
This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. The data mesh is a modern approach to data management that decentralizes data ownership and treats data as a product.
131
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How to Achieve High-Accuracy Results When Using LLMs
Maximizing Profit and Productivity: The New Era of AI-Powered Accounting
Relevance, Reach, Revenue: How to Turn Marketing Trends From Hype to High-Impact
Automation, Evolved: Your New Playbook For Smarter Knowledge Work
Analytics Vidhya
JULY 28, 2021
ArticleVideo Book This article was published as a part of the Data Science Blogathon ML + DevOps + Data Engineer = MLOPs Origins MLOps originated. The post DeepDive into the Emerging concpet of Machine Learning Operations or MLOPs appeared first on Analytics Vidhya.
337 
Towards AI
DECEMBER 26, 2024
4 Things to Keep in Mind Before Deploying Your ML Models This member-only story is on us. Source: Image By Author As a Cloud Engineer, Ive recently collaborated with a number of project teams, and my primary contribution to these teams has been to do the DevOps duties required on the GCP Cloud. Upgrade to access all of Medium.
116 
AWS Machine Learning Blog
FEBRUARY 21, 2025
Rockets legacy data science environment challenges Rockets previous data science solution was built around Apache Spark and combined the use of a legacy version of the Hadoop environment and vendor-provided Data Science Experience development tools.
67 
Analytics Vidhya
FEBRUARY 2, 2022
This article was published as a part of the Data Science Blogathon.
271 
IBM Journey to AI blog
AUGUST 11, 2023
Both computer scientists and business leaders have taken note of the potential of the data. Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. MLOps is the next evolution of data analysis and deep learning. What is MLOps? How the models are stored.
170 
IBM Journey to AI blog
AUGUST 12, 2024
Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.
266 
IBM Journey to AI blog
NOVEMBER 13, 2023
Overview of Kubernetes Containers —lightweight units of software that package code and all its dependencies to run in any environment—form the foundation of Kubernetes and are mission-critical for modern microservices, cloud-native software and DevOps workflows.
323 
O'Reilly Media
OCTOBER 19, 2021
As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. Can’t we just fold it into existing DevOps best practices? What does a modern technology stack for streamlined ML processes look like?
145 
Analytics Vidhya
DECEMBER 11, 2022
This article was published as a part of the Data Science Blogathon. Introduction Machine learning (ML) has become an increasingly important tool for organizations of all sizes, providing the ability to learn and improve from data automatically.
360 
AWS Machine Learning Blog
NOVEMBER 19, 2024
This year, generative AI and machine learning (ML) will again be in focus, with exciting keynote announcements and a variety of sessions showcasing insights from AWS experts, customer stories, and hands-on experiences with AWS services. Visit the session catalog to learn about all our generative AI and ML sessions.
85 
AWS Machine Learning Blog
JANUARY 5, 2024
This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.
127 
AWS Machine Learning Blog
JANUARY 23, 2025
The solution described in this post is geared towards machine learning (ML) engineers and platform teams who are often responsible for managing and standardizing custom environments at scale across an organization. You can use this solution to promote consistency of the analytical environments for data science teams across your enterprise.
100 
NOVEMBER 24, 2023
With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. Data scientists and ML engineers require capable tooling and sufficient compute for their work.
152 
databricks
SEPTEMBER 11, 2024
Personal Access Tokens (PATs) are a convenient way to access services like Azure Databricks or Azure DevOps without logging in with your password.
114 
Unite.AI
JUNE 19, 2024
In world of Artificial Intelligence (AI) and Machine Learning (ML), a new professionals has emerged, bridging the gap between cutting-edge algorithms and real-world deployment. Meet the MLOps Engineer: the orchestrating the seamless integration of ML models into production environments, ensuring scalability, reliability, and efficiency.
Towards AI
DECEMBER 26, 2024
4 Things to Keep in Mind Before Deploying Your ML Models This member-only story is on us. Source: Image By Author As a Cloud Engineer, Ive recently collaborated with a number of project teams, and my primary contribution to these teams has been to do the DevOps duties required on the GCP Cloud. Upgrade to access all of Medium.
111 
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. It currently offers services in a wide range of industries, from life sciences to wholesale distribution.
97 
ODSC - Open Data Science
APRIL 17, 2023
DevSecOps includes all the characteristics of DevOps, such as faster deployment, automated pipelines for build and deployment, extensive testing, etc., Source Purpose of Using DevSecOps in Traditional and ML Applications The DevSecOps practices are different in traditional and ML applications as each comes with different challenges.
97 
AWS Machine Learning Blog
OCTOBER 20, 2023
Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.
119 
AWS Machine Learning Blog
APRIL 26, 2023
Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.
105 
AWS Machine Learning Blog
OCTOBER 28, 2024
Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. SageMaker Model Monitor monitors the quality of SageMaker ML models in production. Your client applications invoke this endpoint to get inferences from the model.
97 
IBM Journey to AI blog
SEPTEMBER 20, 2023
Foundational models (FMs) are marking the beginning of a new era in machine learning (ML) and artificial intelligence (AI) , which is leading to faster development of AI that can be adapted to a wide range of downstream tasks and fine-tuned for an array of applications. IBM watsonx consists of the following: IBM watsonx.ai
AWS Machine Learning Blog
FEBRUARY 27, 2024
In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker.
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)?
52 
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.
60 
The MLOps Blog
JANUARY 23, 2023
Lived through the DevOps revolution. Came to ML from software. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies. Probably sooner than you think.
59 
Heartbeat
FEBRUARY 1, 2023
Machine learning has become an essential part of our lives because we interact with various applications of ML models, whether consciously or unconsciously. Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. What is MLOps?
96 
DataRobot Blog
SEPTEMBER 1, 2022
IDC 2 predicts that by 2024, 60% of enterprises would have operationalized their ML workflows by using MLOps. The same is true for your ML workflows – you need the ability to navigate change and make strong business decisions. These and many other questions are now on top of the agenda of every data science team.
145 
Towards AI
AUGUST 16, 2023
Pietro Jeng on Unsplash MLOps is a set of methods and techniques to deploy and maintain machine learning (ML) models in production reliably and efficiently. Thus, MLOps is the intersection of Machine Learning, DevOps, and Data Engineering (Figure 1). Projects: a standard format for packaging reusable ML code.
AWS Machine Learning Blog
SEPTEMBER 23, 2024
Machine learning (ML) projects are inherently complex, involving multiple intricate steps—from data collection and preprocessing to model building, deployment, and maintenance. To start our ML project predicting the probability of readmission for diabetes patients, you need to download the Diabetes 130-US hospitals dataset.
78 
AWS Machine Learning Blog
JULY 8, 2024
As industries begin adopting processes dependent on machine learning (ML) technologies, it is critical to establish machine learning operations (MLOps) that scale to support growth and utilization of this technology. There were noticeable challenges when running ML workflows in the cloud.
107 
AWS Machine Learning Blog
NOVEMBER 26, 2024
Amazon SageMaker Studio is the first integrated development environment (IDE) purposefully designed to accelerate end-to-end machine learning (ML) development. You can create multiple Amazon SageMaker domains , which define environments with dedicated data storage, security policies, and networking configurations.
AWS Machine Learning Blog
DECEMBER 1, 2023
Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. About the Authors Mair Hasco is an AI/ML Specialist for Amazon SageMaker Studio. Get started on SageMaker Studio here.
113 
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.
97 
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. What is MLOps?
120 
AWS Machine Learning Blog
FEBRUARY 7, 2025
Data science teams often face challenges when transitioning models from the development environment to production. This post, part of the Governing the ML lifecycle at scale series ( Part 1 , Part 2 , Part 3 ), explains how to set up and govern a multi-account ML platform that addresses these challenges.
62 
AWS Machine Learning Blog
OCTOBER 3, 2024
To achieve this effectively, Aviva harnesses the power of machine learning (ML) across more than 70 use cases. Previously, ML models at Aviva were developed using a graphical UI-driven tool and deployed manually. Therefore, developing and deploying more ML models is crucial to support their growing workload.
83 
The MLOps Blog
JUNE 27, 2023
Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.
134 
AWS Machine Learning Blog
OCTOBER 23, 2024
Roy Gunter , DevOps Engineer at Curriculum Advantage, manages cloud infrastructure and automation for Classworks. Dr. Changsha Ma is an AI/ML Specialist at AWS. 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.
84 
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. Join thousands of data leaders on the AI newsletter.
AWS Machine Learning Blog
AUGUST 22, 2024
You can use Amazon SageMaker Model Building Pipelines to collaborate between multiple AI/ML teams. SageMaker Pipelines You can use SageMaker Pipelines to define and orchestrate the various steps involved in the ML lifecycle, such as data preprocessing, model training, evaluation, and deployment.
114 
AWS Machine Learning Blog
OCTOBER 2, 2023
A successful deployment of a machine learning (ML) model in a production environment heavily relies on an end-to-end ML pipeline. Although developing such a pipeline can be challenging, it becomes even more complex when dealing with an edge ML use case. This helps avoid costly defects at later stages of the production process.
108
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