Writing Robust Tests for Data & Machine Learning Pipelines
Eugene Yan
SEPTEMBER 3, 2022
Or why I should write fewer integration tests.
writing testing-pipelines 
Chuck Ros, SoftServe: Delivering transformative AI solutions responsibly
AI News
MAY 3, 2024
“Our AI engineers built a prompt evaluation pipeline that seamlessly considers cost, processing time, semantic similarity, and the likelihood of hallucinations,” Ros explained. ” Recognising the critical concern of ethical AI development, Ros stressed the significance of human oversight throughout the entire process.
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Sam King, CEO of Veracode – Interview Series
Unite.AI
JUNE 7, 2023
Founded in 2006, it provides SaaS application security that integrates application analysis into development pipelines. The ideal process includes testing in the IDE and the CI/CD pipeline. For years, software security has revolved around testing to find issues, but for every issue found, there is a manual task to fix.
A Recipe For a Robust Model Development Process
Towards AI
APRIL 7, 2024
It is the data we feed it with and a reliable pipeline. Overall, we need high confidence in our pipeline, model, and understanding of the problem and data. However, we cannot test many of the above points with unit tests as in traditional software development. A good trick is to write specific functions first.
How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps
AWS Machine Learning Blog
MAY 16, 2024
Training pipeline and model deployment The model training and deployment phase consists of the following steps: After the training data is uploaded to Amazon S3, CodeBuild runs based on the rules specified in EventBridge. For this reason, we built the MLOps architecture to manage the created models and provide real-time services.
GitLab’s new AI capabilities empower DevSecOps
AI News
NOVEMBER 13, 2023
Beyond code analysis, it supports planning, security issue comprehension and resolution, troubleshooting CI/CD pipeline failures, aiding in merge requests, and more. The State of AI in Software Development report by GitLab reveals that developers spend just 25 percent of their time writing code.
An introduction to Wazi as a Service
IBM Journey to AI blog
NOVEMBER 14, 2023
Moreover, 36% of developers struggle with the collaboration between development and IT Operations, leading to inefficiencies in the development pipeline. To compound these issues, repeated surveys highlight “testing” as the primary area causing delays in project timelines. How does Wazi as Service help drive modernization?
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Amazon AI Introduces DataLore: A Machine Learning Framework that Explains Data Changes between an Initial Dataset and Its Augmented Version to Improve Traceability
Marktechpost
MARCH 22, 2024
Data scientists and engineers frequently collaborate on machine learning ML tasks, making incremental improvements, iteratively refining ML pipelines, and checking the model’s generalizability and robustness. To build a well-documented ML pipeline, data traceability is crucial.
Machine Learning 135
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From Data Science to Production: Generating API Documentation with Swagger
Towards AI
MARCH 7, 2024
In the realm of IT application development, especially as a data scientist, it’s customary to encapsulate data processing and model inference pipelines into an API service. Integrate an AI model into an application. Source: by author. This API service essentially acts as a URL endpoint for invoking your AI model.
Data Science 82
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How to Build a Simple Generative AI Application with Gradio
Towards AI
FEBRUARY 2, 2024
Gradio is simply a great choice for creating a customizable user interface for machine learning models to test your proof of concept. And we’re also importing the pipeline function from the Hugging Face Transformers library, which is very good for working with pre-trained transformer models in NLP.
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Mainframe and the cloud? It’s easy with open source
IBM Journey to AI blog
SEPTEMBER 5, 2023
Empowering teams to use a standard pipeline based on Git to orchestrate the development and deployment of an application unleashes productivity. Wazi is a family of tools for delivering a cloud-native DX for z/OS and providing for cloud-native development and testing for z/OS in the IBM Cloud. No AI was used to write this article.
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A Guide to Mastering Large Language Models
Unite.AI
JANUARY 23, 2024
From chatbots to search engines to creative writing aids, LLMs are powering cutting-edge applications across industries. Orchestration Frameworks Streamline LLM application development using frameworks like LangChain, Cohere which make it easy to chain models into pipelines, integrate with data sources, and abstract away infrastructure.
15 Short Artificial Intelligence (AI) Courses on DeepLearning.AI
Marktechpost
APRIL 11, 2024
From filtering models based on specific criteria to writing minimal lines of code for various tasks, students will learn how to leverage the transformers library effectively. Participants will learn to adapt open-source pipelines for supervised fine-tuning, manage model versions, and preprocess datasets. Build LLM Apps with LangChain.js
Building an End-to-End Machine Learning Project to Reduce Delays in Aggressive Cancer Care.
Towards AI
APRIL 7, 2024
This article seeks to also explain fundamental topics in data science such as EDA automation, pipelines, ROC-AUC curve (how results will be evaluated), and Principal Component Analysis in a simple way. SweetViz is an open-source Python library that generates visualizations that let you begin your EDA by writing two lines of code!
Machine Learning 114
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Application modernization overview
IBM Journey to AI blog
NOVEMBER 24, 2023
Subsequent phases are build and test and deploy to production. Further, for re-write initiatives, one needs to map functional capabilities to legacy application context so as to perform effective domain-driven design/decomposition exercises. Let us explore the Generative AI possibilities across these lifecycle areas.
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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD
AWS Machine Learning Blog
NOVEMBER 9, 2023
Prod environment – Where the ML pipelines from dev are promoted to as a first step, and scheduled and monitored over time. CI/CD and source control – The deployment of ML pipelines across environments is handled through CI/CD set up with Jenkins, along with version control handled through GitHub.
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Modernizing data science lifecycle management with AWS and Wipro
AWS Machine Learning Blog
JANUARY 5, 2024
There are dependencies and complexities with integrating third-party tools into the MLOps pipeline. Wipro further accelerated their ML model journey by implementing Wipro’s code accelerators and snippets to expedite feature engineering, model training, model deployment, and pipeline creation.
Data Science 110
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The Top 13 AI-Powered CRM Platforms
Towards AI
FEBRUARY 22, 2024
Predictive Sales Forecasting: To gain insights into future sales trends and pipeline health for making informed decisions. Test Before You Invest: Test the software using free trials or demos to ensure the software fits your needs perfectly. Minimal AI Features: No true AI features except basic suggestions and auto-fill.
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Code Evolution: Transforming Software Development with Generative AI Adoption
Becoming Human
APRIL 19, 2024
This radical method has the power to completely change how software is developed, tested, and implemented. Automated Testing: By automating the creation of test cases, generative AI can expedite the software development process’ testing phase.
Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 2
AWS Machine Learning Blog
OCTOBER 2, 2023
In Part 1 of this series, we drafted an architecture for an end-to-end MLOps pipeline for a visual quality inspection use case at the edge. The focus on managed and serverless services reduces the need to operate infrastructure for your pipeline and allows you to get started quickly. Labeling jobs are used to manage labeling workflows.
Automation 92
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Bridging Large Language Models and Business: LLMops
Unite.AI
OCTOBER 16, 2023
The potential applications are boundless—from drafting emails, creating code, answering queries, to even writing creatively. Integrating a feedback loop within LLMOps pipelines not only simplifies evaluation but also fuels the fine-tuning process. With a recent seed funding of $3 million led by Lightspeed Venture Partners, Portkey.ai
Data science vs data analytics: Unpacking the differences
IBM Journey to AI blog
SEPTEMBER 19, 2023
Your skill set should include the ability to write in the programming languages Python, SAS, R and Scala. An electrical engineer can use prescriptive analytics to digitally design and test out various electrical systems to see expected energy output and predict the eventual lifespan of the system’s components.
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Unlock personalized experiences powered by AI using Amazon Personalize and Amazon OpenSearch Service
AWS Machine Learning Blog
FEBRUARY 29, 2024
Populating the index with representative data facilitates thorough testing and validation of the plugin. Set up search pipelines to activate the plugin’s functionality. Search pipelines contain request preprocessors and response postprocessors that transform queries and results. For values, specify true or false.
The six strategic uses cases for AIOps
IBM Journey to AI blog
JUNE 26, 2023
Improve CI/CD pipelines The continuous integration/continuous delivery pipeline—commonly referred to as the CI/CD pipeline —is an agile DevOps workflow focused on a frequent and reliable software delivery process. A key characteristic of the CI/CD pipeline is the use of automation to ensure code quality.
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Google Launches Bard, a Challenge to Rival ChatGPT
ODSC - Open Data Science
FEBRUARY 8, 2023
Providing an example of the company’s goal with Bard, Pichai went on to write, “ Bard can be an outlet for creativity, and a launchpad for curiosity, helping you to explain new discoveries from NASA’s James Webb Space Telescope to a 9-year-old, or learn more about the best strikers in football right now, and then get drills to build your skills. ”
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MLOps and the evolution of data science
IBM Journey to AI blog
AUGUST 11, 2023
These insights can help drive decisions in business, and advance the design and testing of applications. Repeat—Teams will go through each step of the ML pipeline again until they’ve achieved the desired outcome. How to use ML to automate the refining process into a cyclical ML process.
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Leveraging generative AI on AWS to transform life sciences
IBM Journey to AI blog
JULY 19, 2023
Code creation: Code co-pilot, code conversion, create technical documentation, test cases and more. How to build a generative AI pipeline in AWS for narrative generation? The high-level pipeline for this process is shown in Figure 1. Pipeline for generating adverse event narratives Figure 2.
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Implement a custom AutoML job using pre-selected algorithms in Amazon SageMaker Automatic Model Tuning
AWS Machine Learning Blog
NOVEMBER 15, 2023
Such preprocessing techniques could be applied individually or be combined in a pipeline. The dataset is split into training and testing data frames and uploaded to the SageMaker session default S3 bucket. Training script template The AutoML workflow in this post is based on scikit-learn preprocessing pipelines and algorithms.
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Deploying a Custom Image Classifier on an OAK-D
PyImageSearch
APRIL 3, 2023
As an engineer, your work might include more than just running the deep learning models on a cluster equipped with high-end GPUs and achieving state-of-the-art results on the test data. blob ) as required by OAK hardware test_data : It contains a few vegetable images from the test set, which the classify_image.py
Meet the Seattle-area startups that just graduated from Y Combinator
SEPTEMBER 25, 2023
Devs shouldn’t be neck-deep in evaluation pipelines just to test their software, so we solve that complexity for them. Watto securely uses this contextual data to build high quality documents/reports that employees spend quarters in writing and getting reviewed. Gleam Gleam founders Emeka Itegbe (left) Oliver Keh.
The Sequence Chat: Emmanuel Turlay – CEO, Sematic
TheSequence
JULY 12, 2023
. 🛠 ML Work Your most recent project is Sematic, which focuses on enabling Python-based orchestration of ML pipelines. ML Engineers want to focus on writing Python logic, and visualizing the impact of their changes quickly. This required large end-to-end pipelines. should be tracked in a knowledge graph. Observability.
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Kubeflow Pipelines: Orchestrating Machine Learning Workflows With Ease
Mlearning.ai
JULY 10, 2023
Everything you need to know about Kubeflow Pipelines for Machine Learning Pipelines Image by Lukas from Pixabay Kubeflow Pipelines (KFP) is a powerful tool that enables you to build, deploy, and run machine learning pipelines in a scalable and reproducible manner using Docker containers.
Applying Responsible NLP in Real-World Projects
John Snow Labs
MAY 15, 2023
The underlying principles behind the NLP Test library: Enabling data scientists to deliver reliable, safe and effective language models. Privacy: Data privacy and security should be prioritized in all stages of the AI pipeline. Software Engineering Fundamentals Testing software is crucial to ensure it works as intended.
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sktime?—?Python Toolbox for Machine Learning with Time Series
ODSC - Open Data Science
MAY 25, 2023
Build tuned auto-ML pipelines, with common interface to well-known libraries (scikit-learn, statsmodels, tsfresh, PyOD, fbprophet, and more!) We provide extension templates for all supported learning tasks to enable you to write your own components Option 1: you want an estimator in sktime? Annotation? Something else?
6 Remote AI Jobs to Look for in 2024
ODSC - Open Data Science
DECEMBER 19, 2023
These professionals are responsible for creating and maintaining prompts for AI models, redlining, and finetuning models through tests and prompt work. They use their knowledge of data warehousing, data lakes, and big data technologies to build and maintain data pipelines. Prompt Engineer Prompt engineers are in the wild west of AI.
SambaSafety automates custom R workload, improving driver safety with Amazon SageMaker and AWS Step Functions
AWS Machine Learning Blog
JUNE 16, 2023
The SambaSafety data science team used a code repository solution external to AWS; the final pipeline had to be intelligent enough to trigger based on updates to their code base, which was written primarily in R. The solution delivered by Firemind for SambaSafety’s data science team was built around two ML pipelines.
Automation 76
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ChatGPT, Author of The Quixote
O'Reilly Media
MARCH 26, 2024
In Borges’ fable Pierre Menard, Author of The Quixote , the eponymous Monsieur Menard plans to sit down and write a portion of Cervantes’ Don Quixote. Not to transcribe, but re-write the epic novel word for word: His goal was never the mechanical transcription of the original; he had no intention of copying it. joined Flickr.
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Evaluation of RAG Pipelines for more reliable LLM applications
Mlearning.ai
JANUARY 3, 2024
Building a PoC RAG pipeline is not overtly complex. However, to enhance its robustness, thorough testing on a dataset that accurately mirrors the production distribution is imperative. Ground Truth or known correct response Datapoints required for evaluating RAG pipelines Evaluation Metrics Ragas Metrics A.
LLM 52
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How to Build a CI/CD MLOps Pipeline [Case Study]
The MLOps Blog
MARCH 15, 2023
This article is a real-life study of building a CI/CD MLOps pipeline. CI/CD pipeline: key thoughts and considerations Continuous integration and continuous deployment (CI/CD) are crucial in ML model deployments because it allows faster and more efficient model updates and enhancements. S3 buckets.
ETL 52
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? Guest Post: LLMs & humans: The perfect duo for data labeling
TheSequence
OCTOBER 23, 2023
We’ve been testing multiple LLMs on our own data labeling projects and comparing them to human labeling with a crowd of trained annotators. You just need to write a detailed prompt with task instructions and examples in text format. So how do we structure a hybrid pipeline? Absolutely.
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Build custom code libraries for your Amazon SageMaker Data Wrangler Flows using AWS Code Commit
AWS Machine Learning Blog
MARCH 21, 2023
It contains over 300 built-in data transformation steps to aid with feature engineering, normalization, and cleansing to transform your data without having to write any code. We do this in the custom transform step because Data Wrangler doesn’t have a built-in transform for this task as of this writing. Choose Export to.
How BigBasket improved AI-enabled checkout at their physical stores using Amazon SageMaker
AWS Machine Learning Blog
FEBRUARY 13, 2024
Split data into train, validation, and test sets. BigBasket used SageMaker notebooks to train their ML models and were able to easily port their existing open source PyTorch and other open source dependencies to a SageMaker PyTorch container and run the pipeline seamlessly. Their starting training data size was over 1.5
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Real-World MLOps Examples: End-To-End MLOps Pipeline for Visual Search at Brainly
The MLOps Blog
MARCH 28, 2023
At the time of this writing, Brainly has over 300 million monthly users across the globe. The ML infrastructure team makes it easy for the AI teams to create training pipelines with internal tools that make their workflow easier. These datasets would go into the training pipelines they have already set up.
Build production-ready generative AI applications for enterprise search using Haystack pipelines and Amazon SageMaker JumpStart with LLMs
AWS Machine Learning Blog
AUGUST 14, 2023
In this post, we showcase how to build an end-to-end generative AI application for enterprise search with Retrieval Augmented Generation (RAG) by using Haystack pipelines and the Falcon-40b-instruct model from Amazon SageMaker JumpStart and Amazon OpenSearch Service. Initialize DocumentStore and index documents.
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