Object-oriented programming for data scientists: Build your ML estimator
KDnuggets
AUGUST 30, 2019
Implement some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better.
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KDnuggets
AUGUST 30, 2019
Implement some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better.
KDnuggets
SEPTEMBER 11, 2019
How one person overcame rejections applying to Data Scientist positions by getting actual data on who is getting hired; Advice from Andrew Ng on building ML career and reading research papers; 10 Great Python resources for Data Scientists; Python Libraries for Interpretable ML,
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KDnuggets
SEPTEMBER 9, 2019
Andrew Ng; Object-oriented programming for data scientists: Build your ML estimator. Also: Python Libraries for Interpretable Machine Learning; TensorFlow vs PyTorch vs Keras for NLP; Advice on building a machine learning career and reading research papers by Prof.
Towards AI
AUGUST 7, 2024
From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams Photo by Parabol | The Agile Meeting Toolbox on Unsplash In this article, we will explore the essential VS Code extensions that enhance productivity and collaboration for data scientists and machine learning (ML) engineers.
KDnuggets
SEPTEMBER 25, 2019
Learn about unexpected risk of AI applied to Big Data; Study 5 Sampling Algorithms every Data Scientist needs to know; Read how one data scientist copes with his boring days of deploying machine learning; 5 beginner-friendly steps to learn ML with Python; and more.
KDnuggets
NOVEMBER 20, 2019
Read tips and tricks that helped one Data Scientist to get better at Machine Learning; Learn how to make ML project cost-effective; Consider submitting a blog to KDnuggets - you can be profiled here; and study how to manipulate Python lists.
Data Science 101
APRIL 29, 2019
Here is the latest data science news for the week of April 29, 2019. From Data Science 101. The Go Programming Language for Data Science Quick Video Tutorial for Find Updates in Azure Two-Minute Papers, One Pixel attack on NN. General Data Science.
KDnuggets
OCTOBER 2, 2019
Also: Top KDnuggets tweets, Sep 18-24: Python Libraries for Interpretable Machine Learning; Scikit-Learn: A silver bullet for basic ML; Automatic Version Control for Data Scientists; My journey path from a Software Engineer to BI Specialist to a Data Scientist.
AWS Machine Learning Blog
MAY 10, 2023
Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. Given the importance of Jupyter to data scientists and ML developers, AWS is an active sponsor and contributor to Project Jupyter.
AWS Machine Learning Blog
SEPTEMBER 19, 2023
Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.
KDnuggets
SEPTEMBER 25, 2019
Python Libraries for Interpretable Machine Learning; Scikit-Learn: A silver bullet for basic machine learning; I wasn't getting hired as a Data Scientist. So I sought data on who is; Which Data Science Skills are core and which are hot/emerging ones?
KDnuggets
OCTOBER 9, 2019
Also: 12 things I wish I'd known before starting as a Data Scientist; 10 Free Top Notch Natural Language Processing Courses; The Last SQL Guide for Data Analysis; The 4 Quadrants of #DataScience Skills and 7 Principles for Creating a Viral DataViz.
DataRobot Blog
MARCH 30, 2022
For those who haven’t read the prior blogs ( 2019 , 2020 , 2021 ), the idea behind this task is to leverage various sources of data for each track (betting odds, audio analysis , lyric sentiment) to rank which ones are most likely to win the aforementioned awards. Composable ML. the one that appears on top of the leaderboard ).
AWS Machine Learning Blog
DECEMBER 1, 2023
Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. About the Authors Mair Hasco is an AI/ML Specialist for Amazon SageMaker Studio. Get started on SageMaker Studio here.
AWS Machine Learning Blog
APRIL 9, 2024
The correct response for this query is “Amazon’s annual revenue increased from $245B in 2019 to $434B in 2022,” based on the documents in the knowledge base. The generated response is “Amazon’s annual revenue increase from $245B in 2019 to $434B in 2022.” We ask “What was the Amazon’s revenue in 2019 and 2021?”
The MLOps Blog
APRIL 5, 2023
In this comprehensive guide, we’ll explore the key concepts, challenges, and best practices for ML model packaging, including the different types of packaging formats, techniques, and frameworks. These teams may include but are not limited to data scientists, software developers, machine learning engineers, and DevOps engineers.
AWS Machine Learning Blog
DECEMBER 18, 2024
Fastweb , one of Italys leading telecommunications operators, recognized the immense potential of AI technologies early on and began investing in this area in 2019. With a vision to build a large language model (LLM) trained on Italian data, Fastweb embarked on a journey to make this powerful AI capability available to third parties.
AWS Machine Learning Blog
APRIL 19, 2023
Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. Our data scientists train the model in Python using tools like PyTorch and save the model as PyTorch scripts. The DJL was created at Amazon and open-sourced in 2019.
AWS Machine Learning Blog
APRIL 7, 2025
This approach allows for greater flexibility and integration with existing AI and machine learning (AI/ML) workflows and pipelines. By providing multiple access points, SageMaker JumpStart helps you seamlessly incorporate pre-trained models into your AI/ML development efforts, regardless of your preferred interface or workflow.
AWS Machine Learning Blog
DECEMBER 4, 2023
We capitalized on the powerful tools provided by AWS to tackle this challenge and effectively navigate the complex field of machine learning (ML) and predictive analytics. SageMaker is a fully managed ML service. This was a crucial aspect in achieving agility in our operations and a seamless integration of our ML efforts.
AWS Machine Learning Blog
APRIL 29, 2024
This is a guest post co-authored with Ville Tuulos (Co-founder and CEO) and Eddie Mattia (Data Scientist) of Outerbounds. For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time.
AWS Machine Learning Blog
APRIL 6, 2023
& AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. We evaluated the WAPE for all BLs in the auto end market for 2019, 2020, and 2021.
AWS Machine Learning Blog
DECEMBER 7, 2023
Amazon Forecast is a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts. Initially, daily forecasts for each country are formulated through ML models. He joined Getir in 2019 and currently works as a Senior Data Science & Analytics Manager.
AWS Machine Learning Blog
NOVEMBER 1, 2024
For our evaluation, we used the F1 score , which is an evaluation metric to assess the performance of LLMs and traditional ML models. Sovik Kumar Nath is an AI/ML and Generative AI Senior Solutions Architect with AWS. The number of epochs is a crucial hyperparameter that affects model performance and training efficiency.
The MLOps Blog
MARCH 20, 2023
Long-term ML project involves developing and sustaining applications or systems that leverage machine learning models, algorithms, and techniques. An example of a long-term ML project will be a bank fraud detection system powered by ML models and algorithms for pattern recognition. 2 Ensuring and maintaining high-quality data.
FEBRUARY 2, 2023
With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. Next, we present the data preprocessing and other transformation methods applied to the dataset.
AWS Machine Learning Blog
MAY 15, 2023
Getir used Amazon Forecast , a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts, to increase revenue by four percent and reduce waste cost by 50 percent. He joined Getir in 2019 and currently works as a Senior Data Science & Analytics Manager.
ODSC - Open Data Science
SEPTEMBER 21, 2023
Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. Some typical examples are given in the following table, along with some discussion as to whether or not ML would be an appropriate tool for solving the problem: Figure 1.1:
AWS Machine Learning Blog
SEPTEMBER 10, 2024
As part of its goal to help people live longer, healthier lives, Genomics England is interested in facilitating more accurate identification of cancer subtypes and severity, using machine learning (ML). We provide insights on interpretability, robustness, and best practices of architecting complex ML workflows on AWS with Amazon SageMaker.
AWS Machine Learning Blog
AUGUST 7, 2023
AWS ProServe solved this use case through a joint effort between the Generative AI Innovation Center (GAIIC) and the ProServe ML Delivery Team (MLDT). However, LLMs are not a new technology in the ML space. The new ML workflow now starts with a pre-trained model dubbed a foundation model.
The MLOps Blog
AUGUST 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. Stefan: Yeah.
AWS Machine Learning Blog
JULY 3, 2024
For this purpose, we use Amazon Textract, a machine learning (ML) service for entity recognition and extraction. Once the input data is processed, it is sent to the LLM as contextual information through API calls. 2019 Apr;179(4):561-569. Epub 2019 Jan 31. Am J Med Genet A. doi: 10.1002/ajmg.a.61055. Int J Nurs Stud.
AWS Machine Learning Blog
FEBRUARY 10, 2023
Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. Advances in neural information processing systems 32 (2019).
AWS Machine Learning Blog
OCTOBER 25, 2023
Amazon SageMaker geospatial capabilities make it easier for data scientists and machine learning engineers to build, train, and deploy models using geospatial data. In our example, the approximation process suggests October 6, 2019 (Sentinel-2 tile: S2B_32SKA_20191006_0_L2A ), as the most suitable baseline candidate.
DataSeries
OCTOBER 13, 2024
There has been growing speculation that by 2030, the role of traditional data scientists might face a significant decline or transformation. This prediction is driven by advancements in technology, automation, and shifts in how businesses utilize data. healthcare, finance), rather than generalist data scientists.
AWS Machine Learning Blog
OCTOBER 2, 2023
It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. The quality of our labels will affect the quality of our ML model. This three-step process is generic and can be used for any model architecture and ML framework of your choice.
Snorkel AI
MAY 24, 2023
Our speakers lead their fields and embody the desire to create revolutionary ML experiences by leveraging the power of data-centric AI to drive innovation and progress. chief data scientist, a role he held under President Barack Obama from 2015 to 2017. Bush, and has co-authored several books on data science.
Snorkel AI
MAY 24, 2023
Our speakers lead their fields and embody the desire to create revolutionary ML experiences by leveraging the power of data-centric AI to drive innovation and progress. chief data scientist, a role he held under President Barack Obama from 2015 to 2017. Bush, and has co-authored several books on data science.
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.
Ocean Protocol
JUNE 28, 2023
Ocean Protocol provided two datasets for this exercise: one contained a record of all tweets featuring “$OCEAN” since 2020, while the other included the price history of the OCEAN token since 2019. Nicolas dug deep to find correlation coefficients, explain his query scripts, and test more than one ML model in his findings.
AWS Machine Learning Blog
SEPTEMBER 14, 2023
“Data locked away in text, audio, social media, and other unstructured sources can be a competitive advantage for firms that figure out how to use it“ Only 18% of organizations in a 2019 survey by Deloitte reported being able to take advantage of unstructured data. The majority of data, between 80% and 90%, is unstructured data.
DrivenData Labs
DECEMBER 10, 2023
Srinivas Alva is a Data Scientist at ZS Associates, specializing in the transformation of high-grade research into commercial solutions. degree in AI and ML specialization from Gujarat University, earned in 2019. His expertise and experience make him a valuable asset in the field of data science and Generative AI.
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. Neptune Neptune is a platform for tracking and registering ML experiments and models.
DataRobot
JUNE 2, 2021
DataRobot has led the democratization of data science with AutoML that allows both data scientists and non-technical domain experts to participate in the data science process. In 2019, it acquired ParallelM, the leader in machine learning operations. DataRobot Continues Investment in MLOps and New Features.
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