Data Science Dojo

AUGUST 24, 2023

This has led to the emergence of MLOps as a way to standardize and streamline the ML workflow. MLOps emphasizes the need for continuous integration and continuous deployment (CI/CD) in the ML workflow, ensuring that models are updated in real-time to reflect changes in data or ML algorithms. What is MLOps?

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AWS Machine Learning Blog

FEBRUARY 27, 2024

However, even though the pace of innovation is high, the different teams had developed their own ways of working and were in search of a new MLOps best practice. We decided to put in a joint effort to build a prototype on a best practice for MLOps.

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Heartbeat

FEBRUARY 1, 2023

Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. What is MLOps? MLOps is a broad field that encompasses all aspects of developing a machine learning model. This means that even in the business world, MLOps should be addressed.

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Becoming Human

MAY 11, 2023

Machine learning operations, or MLOps, are the set of practices and tools that aim to streamline and automate the machine learning lifecycle. MLOps projects are becoming increasingly popular as companies seek to leverage the power of AI to gain a competitive edge. What are MLOps Projects?

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Iguazio

DECEMBER 14, 2023

As MLOps veterans, Yaron and Noah were all too familiar with the following scenario: data science development in enterprises started small, in the lab, where teams worked in isolation with limited datasets. With practical code examples and specific tool recommendations, the book empowers readers to implement the concepts effectively.

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DataRobot Blog

SEPTEMBER 1, 2022

To prevent delays in productionalizing AI , many organizations invest in MLOps. IDC 2 predicts that by 2024, 60% of enterprises would have operationalized their ML workflows by using MLOps. One of the MLOps features that consistently impresses customers is Continuous AI and the Challenger/Champion framework.

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ODSC - Open Data Science

OCTOBER 26, 2023

10 Essential LLMs Topics to Know, LLMOps and MLOps, and Trending Open-Source Data Visualization Tools 10 Essential Topics to Master LLMs and Generative AI In this blog, we’ll explore ten key aspects of building generative AI applications, including large language model basics, fine-tuning, and core NLP competencies.

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AWS Machine Learning Blog

MAY 23, 2023

Organizations with greater maturity in the ML domain adopt an ML operations (MLOps) paradigm that incorporates continuous integration, continuous delivery, continuous deployment, and continuous training. As such, an ML model is the product of an MLOps pipeline, and a pipeline is a workflow for creating one or more ML models.

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The MLOps Blog

APRIL 16, 2024

integrates with any MLOps stack, and it just works. It’s a technology-agnostic, open-source pipelines framework that’s easy to plugin and just works. If you’ve been into MLOps even for 5 minutes, you probably already know that there’s no one correct way to go about it. is an MLOps stack component for experiment tracking.

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Iguazio

NOVEMBER 16, 2023

An MLOps platform enables streamlining and automating the entire ML lifecycle, from model development and training to deployment and monitoring. When it comes to scaling your MLOps operations, a high-quality, reliable and effective MLOps platform is essential for growth.

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DataRobot

JUNE 16, 2021

Our DataRobot MLOps product has focused on resolving many of the issues with bringing models into production. The MLOps Monitoring Agent is an MLOps feature for standardized model monitoring regardless of where the model is running or what language the model is written in. New in DataRobot MLOps Release 7.1:

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AWS Machine Learning Blog

JANUARY 10, 2024

Model developers often work together in developing ML models and require a robust MLOps platform to work in. A scalable MLOps platform needs to include a process for handling the workflow of ML model registry, approval, and promotion to the next environment level (development, test, UAT, or production).

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Heartbeat

JANUARY 9, 2024

LLMOps vs. MLOps: A Comparative Analysis In this comparative analysis, we explore the differences between LLMOps and MLOps, two independent engineering professions focusing on deploying and managing large language models (LLMs) and standard machine learning models.

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DagsHub

MARCH 21, 2024

To do so, teams implement a Machine Learning Operations (MLOps) pipeline to automate their model management. But, constructing a comprehensive solution tailored for computer vision using Machine Learning Operations (MLOps) is often demanding, and requires a strategic integration of open-source tools.

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DataRobot Blog

DECEMBER 6, 2022

MLOps Helps Mitigate the Unforeseen in AI Projects. Data scientists are continually impressed with DataRobot MLOps features for Continuous AI and the Challenger – Champion framework. How to Integrate DataRobot and Apache Airflow for Orchestration and MLOps Workflows. Read the blog. .

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Flipboard

FEBRUARY 15, 2023

JumpStart APIs unlock the usage of JumpStart capabilities in your workflows, and integrate with tools such as the model registry that are part of MLOps pipelines and anywhere else you’re interacting with SageMaker via SDK. In the next section, we demonstrate how you can use SageMaker Pipelines and SageMaker Projects to set up MLOps.

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AWS Machine Learning Blog

MAY 9, 2024

This post, along with the Cloud Adoption Framework for AI/ML and Well-Architected Machine Learning Lens , serves as a guide for implementing an effective AI/ML CoE with the objective to capture generative AI’s possibilities. The CoE helps make sure that models are developed and deployed in a safe, trustworthy, and ethical fashion.

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DataRobot Blog

AUGUST 16, 2022

Machine learning operations ( MLOps ) tools, which handle model retraining, testing, metrics tracking, versioning, and management. For 44% of DataOps and MLOps practitioners and 38% of beginners, the biggest issue was restricted access to data silos, a problem which is best addressed by an overarching data management strategy.

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AWS Machine Learning Blog

JANUARY 5, 2024

In this post, we discuss model development and MLOps framework implementation for one of Wipro’s customers that uses Amazon SageMaker and other AWS services. MLOps – Model monitoring and ongoing governance wasn’t tightly integrated and automated with the ML models.

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DataRobot Blog

APRIL 29, 2022

The need for an effective Model Risk Management (MRM) framework can be demonstrated with countless case studies of recent MRM failures. A purposeful MLOps strategy can provide exactly this. MLOps provides the necessary guard rails, documentation, monitoring, and approval processes that are needed for security and audit.

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Heartbeat

NOVEMBER 29, 2023

These strong frameworks have changed the field, allowing researchers and practitioners to create and deploy cutting-edge machine learning models. This article sheds light on the fundamental distinctions between these two prominent frameworks, helping you make an informed choice.

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The MLOps Blog

JANUARY 11, 2023

Overall, implementing MLOps in an IoT edge company is important because IoT systems often involve relatively large volumes of data, high levels of complexity, and real-time decision-making. If the machine learning tasks required by your use cases can be implemented using AI services, then you don’t need an MLOps solution.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Certain skills become more or less popular, new platforms and frameworks are cycled in, and responsibilities change. Data Science Skills and Competencies Aside from knowing particular frameworks and languages, there are various topics and competencies that any data scientist should know. Joking aside, this does infer particular skills.

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ODSC - Open Data Science

JANUARY 31, 2024

This LLM framework lets you curate and organize your own data sources, like documents, databases, and APIs, making them readily accessible to your LLM. In short, think of this LLM framework as a normal chatbot but on steroids. This open-source LLM framework integrates LLMs with H2O, a popular data science platform.

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Towards AI

MARCH 3, 2024

But in this jungle of ones and zeros, there lies a crucial tool — MLOps, the superhero of streamlined development and deployment. Here’s a preview of how the app will appear U+26A1 Image By Author: Streamlit App Now that we have the basic framework for our project, it’s time to initialize Git and set up a GitHub repository.

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The MLOps Blog

MARCH 28, 2023

In this second installment of the series “Real-world MLOps Examples,” Paweł Pęczek , Machine Learning Engineer at Brainly , will walk you through the end-to-end Machine Learning Operations (MLOps) process in the Visual Search team at Brainly. Watch this video to learn how the Content AI team does MLOps. “If

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AWS Machine Learning Blog

FEBRUARY 22, 2023

This post discusses how to bring the JAX framework into your SageMaker domain. JAX is an up-and-coming ML framework for which SageMaker has no built-in support. To that end, the exact training and inference pipelines used by Boomi’s MLOps team necessitated additional steps for automated deployment, rollback, and monitoring.

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ODSC - Open Data Science

JULY 13, 2023

MLOPs with Azure Machine Learning The MLOps v2 accelerator is the de-facto MLOps solution from Microsoft going forward. Join this webinar to learn how to easily generate insights from unstructured data by customizing an LLM with open-source declarative ML framework, Ludwig, and Predibase.

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phData

MARCH 4, 2024

Snowpark ML Snowpark ML serves as the Python library and foundational framework for complete ML workflows within Snowflake, encompassing functionalities for both model development and operations. Leveraging Snowpark ML, users can employ well-known Python frameworks for preprocessing, feature engineering, and training tasks.

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ODSC - Open Data Science

OCTOBER 13, 2023

As the Co-Founder and CTO of Iguazio, Yaron drives the strategy for the company’s MLOps platform and led the shift towards the production-first approach to data science and catering to real-time AI use cases.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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ODSC - Open Data Science

JUNE 7, 2023

Containers + GPUs In Depth Emily Curtin | Staff MLOps Engineer | Intuit Mailchimp This talk is a highly technical walkthrough of the lower-level system libraries involved in GPU computing. You’ll leave this session with an understanding of the available resources for building AI systems that work safely and correctly.

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ODSC - Open Data Science

MAY 15, 2023

It is tempting to start with technology improvements and lean on existing frameworks, like Google’s MLOps framework , to show progress and demonstrate value. To manage this, most leaders have created a plan of attack in the form of a capability roadmap that addresses those challenges in order of priority and dependency.

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ODSC - Open Data Science

FEBRUARY 27, 2024

Containers + GPUs In Depth Emily Curtin | Staff MLOps Engineer | Intuit Mailchimp This talk is a highly technical walkthrough of the lower-level system libraries involved in GPU computing. You’ll leave this session with an understanding of the available resources for building AI systems that work safely and correctly.

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ODSC - Open Data Science

OCTOBER 20, 2023

You’ll also learn about the latest tools in NLP, machine learning, MLOps, and more during the Solution Talks. Building and Deploying a Gen AI App in 20 Minutes Nick Schenone | Pre-Sales MLOps Engineer | Iguazio Building your own Generative AI application can be quite difficult. Check them out below.

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DataRobot Blog

OCTOBER 18, 2022

Knowing this, we walked through a demo of DataRobot AI Cloud MLOps solution , which can manage the open-source models developed by the retailer and regularly provide metrics such as service health, data drift and changes in accuracy. Today, his team is using open-source packages without a standardized AI platform.

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DagsHub

APRIL 21, 2024

This collaboration of ML and operations teams is what you call MLOps and focuses on streamlining the process of deploying the ML models to production, along with maintaining and monitoring them. What is MLOps? To back it up let me introduce you to some of the real-world use cases where MLOps is playing a key role.

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