The MLOps Blog

JULY 18, 2024

As machine learning (ML) drives innovation across industries, organizations seek ways to improve and optimize their ML workflows. End-to-end (E2E) MLOps platforms promise to simplify the complicated process of building, deploying, and maintaining ML models in production.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. They’re looking for people who know all related skills, and have studied computer science and software engineering.

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The MLOps Blog

JANUARY 23, 2023

However, there are some key differences that we need to consider: Size and complexity of the data In machine learning, we are often working with much larger data. Basically, every machine learning project needs data. Given the range of tools and data types, a separate data versioning logic will be necessary.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Cloud Computing, APIs, and Data Engineering NLP experts don’t go straight into conducting sentiment analysis on their personal laptops. TensorFlow is desired for its flexibility for ML and neural networks, PyTorch for its ease of use and innate design for NLP, and scikit-learn for classification and clustering.

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O'Reilly Media

SEPTEMBER 15, 2021

Cloud certifications, specifically in AWS and Microsoft Azure, were most strongly associated with salary increases. As we’ll see later, cloud certifications (specifically in AWS and Microsoft Azure) were the most popular and appeared to have the largest effect on salaries. The top certification was for AWS (3.9%

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ODSC - Open Data Science

APRIL 3, 2023

While a data analyst isn’t expected to know more nuanced skills like deep learning or NLP, a data analyst should know basic data science, machine learning algorithms, automation, and data mining as additional techniques to help further analytics. Cloud Services: Google Cloud Platform, AWS, Azure.

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Data Science Dojo

FEBRUARY 20, 2023

Machine learning (ML) is the technology that automates tasks and provides insights. It allows data scientists to build models that can automate specific tasks. It comes in many forms, with a range of tools and platforms designed to make working with ML more efficient. It is also useful for training models on smaller datasets.

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phData

JULY 2, 2024

Familiar Client Side Libraries – Snowpark brings in-house and deeply integrated, DataFrame-style programming abstract and OSS-compatible APIs to the languages data practitioners like to use (Python, Scala, etc). It also includes the Snowpark ML API for more efficient machine language (ML) modeling and ML operations.

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Mlearning.ai

JULY 11, 2023

For example, when it comes to deploying projects on cloud platforms, different companies may utilize different providers like AWS, GCP, or Azure. Therefore, having proficiency in a specific cloud platform, such as Azure, does not mean you will exclusively work with that platform in the industry.

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phData

JULY 5, 2024

To provide an example, traditional structured data such as a user’s demographic information can be provided to an AI application to create a more personable experience. Our data engineering blog in this series explores the concept of data engineering and data stores for Gen AI applications in more detail.

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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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ODSC - Open Data Science

JANUARY 29, 2024

Data, Engineering, and Programming Skills Programming Despite the rise of no-code platforms and AI code assistance, programming skills are still essential for training and fine-tuning LLM models, scripting for data processing, and integrating models into applications.

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The MLOps Blog

MAY 17, 2023

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

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phData

AUGUST 6, 2024

What is Snowpark and Why Use It for Building Data Pipelines? Snowpark offers two client-side libraries: Snowpark API and Snowpark ML API. The Snowpark API includes the DataFrame API and integrates with other popular open-source APIs developers can use to complete data engineering tasks.

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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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ODSC - Open Data Science

JUNE 7, 2023

Since data is left in its raw form within the data lake, it’s easier for data teams to experiment with models and analysis techniques with greater flexibility. So let’s take a look at a few of the leading industry examples of data lakes. Snowflake Snowflake is a cross-cloud platform that looks to break down data silos.

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Pickl AI

OCTOBER 17, 2024

Strategies may involve optimising SQL queries, parallelising tasks, and ensuring data pipelines are designed for efficient processing. Regularly reviewing the entire ETL workflow and seeking feedback from data engineers can uncover areas for improvement. It also makes predictions for the future of ETL processes.

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phData

JANUARY 31, 2024

In this blog, we’ll show you how to build a robust energy price forecasting solution within the Snowflake Data Cloud ecosystem. We’ll cover how to get the data via the Snowflake Marketplace, how to apply machine learning with Snowpark , and then bring it all together to create an automated ML model to forecast energy prices.

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The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve.

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IBM Journey to AI blog

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.

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Pickl AI

MAY 15, 2024

You can adopt these strategies as well as focus on continuous learning to upscale your knowledge and skill set. Leverage Cloud Platforms Cloud platforms like AWS, Azure, and GCP offer a suite of scalable and flexible services for data storage, processing, and model deployment.

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Mlearning.ai

JUNE 16, 2023

How to use the Codex models to work with code - Azure OpenAI Service Codex is the model powering Github Copilot. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API. The data would be interesting to analyze. The article has good points with any LLM Use prompt to guide.

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phData

JUNE 25, 2024

It’s clear that the Snowflake AI Data Cloud is coming of age as a powerful AI/ML platform, and it’s a good time for data scientists to look at adopting Snowflake more heavily to better align with their organization’s data platform and strategy. You might use one every single day.

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The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

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DataRobot Blog

FEBRUARY 2, 2022

DataRobot enables entire teams — from data scientists to data engineers and from IT to business users — to collaborate on a unified platform. By partnering with major cloud companies like AWS, Google Cloud, Azure, and VMware, DataRobot helps customers harness the power of their data wherever it lives.

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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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