The MLOps Blog

JUNE 13, 2024

Data scientists often lack focus, time, or knowledge about software engineering principles. As a result, poor code quality and reliance on manual workflows are two of the main issues in ML development processes. You need to think about and improve the data, the model, and the code, which adds layers of complexity.

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ODSC - Open Data Science

JANUARY 25, 2023

The Azure ML team has long focused on bringing you a resilient product, and its latest features take one giant leap in that direction, as illustrated in the graph below (Figure 1). Continue reading to learn more about Azure ML’s latest announcements. This is the motivation behind several of Azure ML’s latest features.

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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.

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DagsHub

AUGUST 29, 2024

Source: Author Introduction Machine learning (ML) models, like other software, are constantly changing and evolving. Version control systems (VCS) play a key role in this area by offering a structured method to track changes made to models and handle versions of data and code used in these ML projects.

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The MLOps Blog

NOVEMBER 30, 2022

Data science teams currently struggle with managing multiple experiments and models and need an efficient way to store, retrieve, and utilize details like model versions, hyperparameters, and performance metrics. ML model versioning: where are we at? The short answer is we are in the middle of a data revolution.

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The MLOps Blog

OCTOBER 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. How do I develop my body of work?

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phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

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DagsHub

APRIL 21, 2024

Source: [link] Similarly, while building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. You need to make that model available to the end users, monitor it, and retrain it for better performance if needed.

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Pickl AI

NOVEMBER 28, 2024

Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. Introduction Machine Learning ( ML ) is revolutionising industries, from healthcare and finance to retail and manufacturing. Fundamental Programming Skills Strong programming skills are essential for success in ML.

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Snorkel AI

OCTOBER 18, 2023

From onboarding new customers to analyzing pictures or videos of damages for evaluation, machine learning (ML) and AI offer exciting possibilities for optimization and cost-saving across the insurance industry. Claims data is often noisy, unstructured, and multi-modal.

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Snorkel AI

OCTOBER 18, 2023

From onboarding new customers to analyzing pictures or videos of damages for evaluation, machine learning (ML) and AI offer exciting possibilities for optimization and cost-saving across the insurance industry. Claims data is often noisy, unstructured, and multi-modal.

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Snorkel AI

OCTOBER 18, 2023

From onboarding new customers to analyzing pictures or videos of damages for evaluation, machine learning (ML) and AI offer exciting possibilities for optimization and cost-saving across the insurance industry. Claims data is often noisy, unstructured, and multi-modal.

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DagsHub

OCTOBER 14, 2024

With these several trials, it is quite common to lose track of all the combinations that you might have tried out in pursuit of a better ML model. Why is ML Experiment Tracking Important? This is one of the most important reasons why you need to keep track of all the experiments (including minor ones) in the modeling phase.

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Mlearning.ai

FEBRUARY 16, 2023

The platform enables quick, flexible, and convenient options for storing, processing, and analyzing data. The solution was built on top of Amazon Web Services and is now available on Google Cloud and Microsoft Azure. ML models, in turn, require significant volumes of adequate data to ensure accuracy.

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DagsHub

DECEMBER 11, 2023

DagsHub DagsHub is a centralized Github-based platform that allows Machine Learning and Data Science teams to build, manage and collaborate on their projects. In addition to versioning code, teams can also version data, models, experiments and more. DVC can efficiently handle large files and machine learning models.

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ODSC - Open Data Science

APRIL 6, 2023

But its status as the go-between for programming and data professionals isn’t its only power. Within SQL you can also filter data, aggregate it and create valuations, manipulate data, update it, and even do data modeling. Finally, cloud services.

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DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging. This article will discuss managing unstructured data for AI and ML projects. What is Unstructured Data?

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Snorkel AI

JUNE 7, 2023

s Daniel Wu , and Snorkel AI’s Aarti Bagul explored the ethical challenges of leveraging generative AI in the midst of an ML arms race. They touched on AI regulation, risk mitigation strategies, and the importance of data-centric ML systems in ensuring responsible AI innovation. Catch the sessions you missed!

Data Scientist 52

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Snorkel AI

JUNE 7, 2023

s Daniel Wu , and Snorkel AI’s Aarti Bagul explored the ethical challenges of leveraging generative AI in the midst of an ML arms race. They touched on AI regulation, risk mitigation strategies, and the importance of data-centric ML systems in ensuring responsible AI innovation. Learn more, live!

Data Scientist 52

Data Scientist AI AI Machine Learning 52

phData

SEPTEMBER 28, 2023

Attach a Common Data Model Folder (preview) When you create a Dataflow from a CDM folder, you can establish a connection to a table authored in the Common Data Model (CDM) format by another application. With the import option, users can create a new version of the Dataflow while the original Dataflow remains unchanged.

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The MLOps Blog

JANUARY 25, 2023

ML-Based Approach: Rule-based approach fails to identify things like Irony and sarcasm, multiple types of negations, word ambiguity, and multipolarity in text. Due to this, businesses are now focusing on an ML-based approach, where different ML algorithms are trained on a large dataset of prelabeled text.

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DagsHub

NOVEMBER 11, 2024

In this article, we’ll explore how AI can transform unstructured data into actionable intelligence, empowering you to make informed decisions, enhance customer experiences, and stay ahead of the competition. What is Unstructured Data? Word2Vec , GloVe , and BERT are good sources of embedding generation for textual data.

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Pickl AI

OCTOBER 3, 2024

Predictive Analytics : Models that forecast future events based on historical data. Model Repository and Access Users can browse a comprehensive library of pre-trained models tailored to specific business needs, making it easy to find the right solution for various applications.

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Pickl AI

OCTOBER 16, 2024

Data Model : RDBMS relies on a structured schema with predefined relationships among tables, whereas NoSQL databases use flexible data models (e.g., key-value pairs, document-based) that accommodate unstructured data. Scalability : RDBMS typically scales vertically by adding more resources to a single server.

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DagsHub

OCTOBER 28, 2024

For instance, in a computation neuroscience startup, there are various teams – mathematicians, neuroscientists, AI/ML engineers, HR, etc. AI/ML engineers can use LLM as a code assistant to develop new models. At the same time, the AI/ML team needs an LLM to help design a novel neural network model.

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Mlearning.ai

SEPTEMBER 4, 2023

Turbo model, this may be a better fit for some businesses.[2],[3] 2],[3] It’s important to note that even with a million-dollar investment, it may be challenging to match the general performance and latency of these commercial models.[4] 4] Private instances: Microsoft Azure provides a private instance of ChatGPT.

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Mlearning.ai

JUNE 16, 2023

Generative AI can be used to automate the data modeling process by generating entity-relationship diagrams or other types of data models and assist in UI design process by generating wireframes or high-fidelity mockups. diagram Using ChatGPT to build system diagrams — Part II Generate C4 diagrams using mermaid.js

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phData

JULY 18, 2023

Why Migrate to a Modern Data Stack? Data teams can focus on delivering higher-value data tasks with better organizational visibility. Move Beyond One-off Analytics: The Modern Data Stack empowers you to elevate your data for advanced analytics and integration of AI/ML, enabling faster generation of actionable business insights.

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Towards AI

JANUARY 22, 2025

These are instead some of the skills that I would strongly master: Theoretical foundation: A strong grasp of concepts like exploratory data analysis (EDA), data preprocessing, and training/finetuning/testing practices, ML models remains essential. These two languages cover most data science workflows.

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