Christophe Blefari

DECEMBER 3, 2022

In a nutshell the 3 first articles are: MLOps isn’t DevOps for ML — Abi strongly answer to thenewstack.io Using Airflow the wrong way — An experimental article I wrote where I explore Airflow as a framework rather than a all-in-one scheduler/orchestrator tool.

Kafka 130

Kafka BI Finance Data 130

DareData

NOVEMBER 28, 2023

These practices and methodologies are commonly known as MLOps, short for Machine Learning Operations and they bridge the gap between data science and software engineering, ensuring the pillars of experimentation: reproducibility, performance, scalability and monitorization. But that is not all!

Data Science 52

Data Science Medical Machine Learning Data 52

Christophe Blefari

MARCH 17, 2023

Seldon is a MLOps platform that helps you deploying models in production. At core Seldon provides a framework that you can configure to serve you models on top of Kubenertes. Data Economy 💰 The Austrian data protection authority has decided that Meta tracking tools are in violation of the GDPR. It will create a precedent.

Data 130

Data SQL Deep Learning Kafka 130

Cloudera

FEBRUARY 16, 2022

In this article, we explore model governance, a function of ML Operations (MLOps). The AMPs framework also supports the promotion of models from the lab into production, a common MLOps task. The development of ML frameworks and toolkits simplifies these tasks for data scientists. Model Visibility. Model Explainability.

Machine Learning 81

Machine Learning Algorithm Government Metadata 81

ProjectPro

FEBRUARY 25, 2022

Machine Learning Pipeline Tools A machine learning pipeline uses hundreds of tools, libraries, and frameworks. It supports major deep learning frameworks like Torch, Tensorflow, and MXNet. Use Azure as your cloud platform, and Microsoft Azure's ecosystem includes several robust services for building an end-to-end MLOps pipeline.

Machine Learning 52

Machine Learning Building Amazon Web Services AWS 52

Data Engineering Podcast

JULY 10, 2022

Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Tired of deploying bad data?

Metadata 100

Metadata MongoDB MySQL Data Pipeline 100

AltexSoft

JUNE 29, 2021

In large, data-driven enterprises, MLEs are involved in MLOps, or automation of the entire model life cycle in production. Read more about it in our dedicated article MLOps: Methods and Tools of DevOps for Machine Learning. Key components of an MLOps cycle. Proficiency with ML frameworks and libraries.

Machine Learning 105

Machine Learning Engineering Algorithm Programming Language 105

DoorDash Engineering

NOVEMBER 28, 2023

We continued iterating on our initial prototype, which we created using a Python Flask and HTML framework. We organized major user tasks using a jobs-to-be-done framework and categorized users into three buckets: I. The post Transforming MLOps at DoorDash with Machine Learning Workbench appeared first on DoorDash Engineering Blog.

Machine Learning 90

Machine Learning Pipeline-centric Data Science Designing 90

Snowflake

JUNE 27, 2023

App developers can build and deploy front-end user interfaces using React and other popular web development frameworks. Common examples include container images used to kick-off ML training on GPUs or a step in a data pipeline which may be running any language, framework or library inside a container.

Scala 57

Scala Programming Language Data Science Python 57

Monte Carlo

AUGUST 21, 2023

For example, machine learning teams have invested in building or buying feature stores and MLops solutions. However, there will be some additional solutions that are specific to LLM engineering like model hosting, caching, AI agent frameworks, and many others that haven’t even been invented yet.

Data Science 98

Data Science Machine Learning Media Data 98

Knowledge Hut

NOVEMBER 16, 2023

Azure Machine Learning helps to build ML and AI services for end-to-end machine learning lifecycle that includes machine learning operations (MLOps). It provides Apache Spark clusters to work with and has support for almost all the required programming languages, data science frameworks and libraries.

Data Science 52

Data Science Technology Programming Language Cloud Computing 52

Databand.ai

DECEMBER 13, 2022

Follow Caleb on LinkedIn 6) Gowtham SB Data Engineer II at PayPal Gowtham is a big data enthusiast who works with various big data and AWS technologies to help companies build data frameworks and more. His most passionate topics include MLOps, machine learning, data quality and data governance.

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

Christophe Blefari

MARCH 2, 2024

Final reminder, next week there is La Conférence MLOps which will take place in Paris on March 7th. If you want to register I sill have a 40% promocode: mlops-blef-40. This is a 1h podcast, I hope you will enjoy listening to it. and the library does the heavy lifting accordingly.

Data 162

Data Python Database BI 162

RandomTrees

APRIL 24, 2024

MLOps vs LLMOps Large Language Model Operations (LLMOps) and Machine Learning Operations (MLOps) are both important aspects of AI development, but they focus on different areas. LLMOps is a more specific subset of MLOps. It is MLOps but for large language models (like GPT-3). This is where LLMOps comes into play.

Machine Learning 52

Machine Learning Datasets Data Collection Engineering 52

Knowledge Hut

OCTOBER 29, 2023

Deploy A Classification Model Using Azure DevOps This is a very good Azure project that can give you an idea of the DevOps process using Azure DevOps as well as a glimpse of MLOps. The idea is as simple as it sounds. Finally, get ready with a flow diagram to implement the chosen projects.

Project 52

Project Hospitality Food Datasets 52

ProjectPro

DECEMBER 16, 2021

To give it a better technical context, ML engineers mainly work towards serving and packaging the machine learning model in an appropriate framework and scalable manner to make them available to end-users. They should also have experience with ML model deployment using popular python-based frameworks like Flask, Fast API, etc.

Machine Learning 52

Machine Learning Engineering Pipeline-centric Database-centric 52

Data Engineering Podcast

SEPTEMBER 10, 2023

Built on Timely Dataflow and Differential Dataflow, open source frameworks created by cofounder Frank McSherry at Microsoft Research, Materialize is trusted by data and engineering teams at Ramp, Pluralsight, Onward and more to build real-time data products without the cost, complexity, and development time of stream processing.

BI 208

BI SQL Data Machine Learning 208

ProjectPro

SEPTEMBER 16, 2021

87% of Data Science Projects never make it to production - VentureBeat According to an analytics firm, Cognilytica, the MLOps market is anticipated to be worth $4 billion by end of 2025. Therefore, MLOps projects will require data scientists and MLOps engineers to be working together in the next decade.

Project 52

Project Amazon Web Services Machine Learning Data Science 52

ProjectPro

FEBRUARY 25, 2022

Read this article to find the right resources for learning MLOps. The blog starts with an introduction to MLOps, skills required to become an MLOps engineer, and then lays out an MLOps learning path for beginners. MLOps is an acronym that represents the combination of Machine-Learning(ML) and Operations.

Deep Learning 52

Deep Learning Algorithm Machine Learning Data Science 52

Knowledge Hut

DECEMBER 22, 2023

Hadoop Gigabytes to petabytes of data may be stored and processed effectively using the open-source framework known as Apache Hadoop. " Spark, Hadoop, and other frameworks are used to manage large datasets. These large data sets are referred to as "Big Data." Non-Technical Data Science Skills 1.

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

Christophe Blefari

NOVEMBER 11, 2022

This is a morning coffee from the MLOps Community with Ian Schweer who works at Riot Games. The community created dbt as a framework. A framework to organise your data assets and your knowledge. As of today, dbt is the most advanced framework to do this. If not, you'll still like it I think.

BI 130

BI Data Warehouse Data Database 130

LinkedIn Engineering

DECEMBER 20, 2023

Decision making Leveraging the collected signals, we need to build a framework that bridges this multitude of signals, machine learning modules, business rules, and policy enforcement into a dynamic, responsive system. At the core of inference at scale lies the fusion of ML with a wealth of data.

Building 91

Building Algorithm Kafka Machine Learning 91

ProjectPro

OCTOBER 20, 2021

This AutoML application also includes MLOps tools to manage data and models at Scale. Vertex AI is a one-stop shop for end-to-end integration of AI that supports all open source frameworks for the entire ML Workflow. API-first approach Okay, so how do applications that use different frameworks or languages cross-communicate?

Machine Learning 52

Machine Learning Algorithm Government Data Science 52

Pinterest Engineering

SEPTEMBER 12, 2023

In this blogpost, we will share our assessment of the ML developer velocity bottlenecks and delve deeper into how we adopted Ray , the open source framework to scale AI and machine learning workloads, into our ML Platform to improve dataset iteration speed from days to hours, while improving our GPU utilization to over 90%.

Data Process 87

Data Process Process Datasets Scala 87

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . CompactBI — TestDrive is a testing framework for your data and the processes behind them. ModelOps/MLOps.

Consulting 123

Consulting Machine Learning Data Science Data Pipeline 123

Cloudera

APRIL 27, 2023

Ray has emerged as a popular framework because of its superior erformance over Apache Spark for distributed AI compute workloads. Cloudera customers are well prepared to leverage next generation distributed compute frameworks like Ray right on top of their data. This is the power of being open by design.

Machine Learning 75

Machine Learning Python Deep Learning Architecture 75

Cloudera

AUGUST 25, 2020

As you may remember, we recently announced a full set of MLOps capabilities in Cloudera Machine Learning , our cloud native machine learning tool for the cloud. They can take many forms from Python-based rest APIs to R scripts to distributed frameworks like SparkML. Building The Future of Enterprise Production ML.

Machine Learning 74

Machine Learning Data Science Python Cloud 74

ProjectPro

JUNE 13, 2018

Project Idea 5: MLOps AWS Project on Topic Modeling using Gunicorn Flask This project helps you learn the end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable machine learning models by using AWS. Knowledge of AWS networking and security services and AWS Well-Architected Framework.

AWS 52

AWS Certification Amazon Web Services Cloud Computing 52

Knowledge Hut

NOVEMBER 17, 2023

Amazon EMR It is an AWS data science platform for easy execution and processing of big data frameworks, such as Apache, Hadoop and Spark. Data processing frameworks These are the engines to process and analyze the data running on YARN. Apache Spark - a cluster framework for processing big data.

AWS 52

AWS Data Science Certification Amazon Web Services 52

DareData

FEBRUARY 1, 2021

If you are a business person wondering why you should invest in DevOps / MLOps, this is your guide in terms of real live money. Be sure to read to the end for instructions on how to execute an analysis of expected gains from infrastructure changes required for MLOps in your organisation. Caveat: this is a work in progress.

Programming Language 52

Programming Language Project Engineering Consulting 52

Cloudera

OCTOBER 26, 2022

Accessible with a single click from Cloudera machine learning or via public GitHub repositories, AMPs provide an end-to-end framework for building, deploying, and monitoring business-ready ML applications. Much like Dask, Ray is a unified framework for scaling AI and Python applications. We are still hard at work on some new AMPs, too.

Machine Learning 74

Machine Learning Project Deep Learning Data Science 74

Mutt Data

APRIL 11, 2023

Many tutorials for Machine Learning practitioners teach you how to serve your models through a RESTful interface using FastAPI, but most lack a complete outlook into the MLOps aspect of it. Python frameworks do not rank well. Observability: FastAPI is just a web framework. So, already, the language of choice is not optimal.

Utilities 40

Utilities Python Deep Learning AWS 40

Snowflake

MARCH 30, 2023

For this reason, a new data management for ML framework has emerged to help manage this complexity: the “feature store.” Yes, feature stores are part of the MLOps discipline. In addition, by using our “set and forget” framework described in this article , the entire orchestration can be managed by Snowflake as well.

Engineering 77

Engineering Raw Data Data Science Scala 77

Cloudera

JULY 26, 2021

hybrid or public, multi-cloud) and advanced analytical frameworks (e.g., MLOps, DataOps and Streaming DevOps) will come together to build data products and a unified control plane / observability capabilities for unified management and controls. Deep Java Learning, Apache Spark 3.x,

Generalist 114

Generalist Telecommunication Healthcare Data 114

Snowflake

AUGUST 22, 2023

Snowflake Native App Framework, currently in public preview on AWS, is now available for developers to build and test Snowflake Native Apps.

Metadata 70

Metadata Data AWS Business Analyst 70

Knowledge Hut

SEPTEMBER 6, 2023

It provides a thorough understanding of the most in-demand tools, techniques, frameworks, and algorithms in the data science industry, including Excel, SQL, Tableau, Python, Business Analytics, Machine Learning, Deep Learning, NLP, and Data Analytics & Visualization, as well as Big Data Frameworks (Hadoop Ecosystem, Spark, Cloud Computing).

Data Science 95

Data Science Certification Business Analyst Machine Learning 95

U-Next

MARCH 13, 2023

An automated Machine Learning system may learn how to interpret visual input with the use of pre-programmed computational frameworks. The two main methods used to accomplish this are Deep Learning, a particular type of Machine Learning, and convolutional neural networks, a significant variety of neural networks.

Deep Learning 73

Deep Learning Machine Learning Datasets Healthcare 73