Pickl AI

APRIL 19, 2023

Machine Learning is the part of Artificial Intelligence and computer science that emphasizes on the use of data and algorithms, imitating the way humans learn and improving accuracy. Different industries from education, healthcare to marketing, retail and ecommerce require Machine Learning Engineers. Consequently.

Machine Learning 52

Machine Learning ML Engineer Data Scientist ML 52

IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

Data Science 144

Data Science DevOps Machine Learning Data Scientist 144

ODSC - Open Data Science

SEPTEMBER 21, 2023

The following is an extract from Andrew McMahon’s book , Machine Learning Engineering with Python, Second Edition. Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. First of all, the ultimate goal of your work is to generate value.

Machine Learning 98

Machine Learning ML Engineer ML Data Science 98

Pickl AI

APRIL 5, 2023

This makes it easier for you to understand the algorithm and the different techniques used in Machine Learning. You can choose between Python or R programming languages. Moreover, you will also learn the use of clustering and dimensionality reduction algorithms. What Are The Job Responsibilities of an ML Engineer?

Machine Learning 52

Machine Learning ML Engineer ML Data Scientist 52

ODSC - Open Data Science

FEBRUARY 22, 2024

By the end of this session, you’ll have a practical blueprint to efficiently harness feature stores within ML workflows. Using Graphs for Large Feature Engineering Pipelines Wes Madrigal | ML Engineer | Mad Consulting Feature engineering from raw entity-level data is complex, but there are ways to reduce that complexity.

Machine Learning 52

Machine Learning Data Science Python ML 52

Pickl AI

JANUARY 29, 2024

Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. Key takeaways Data Science lays the groundwork for Machine Learning, providing curated datasets for ML algorithms to learn and make predictions. Emphasises programming skills, understanding of algorithms, and expertise in Data Analysis.

Data Science 52

Data Science ML Machine Learning Artificial Intelligence 52

Viso.ai

JANUARY 30, 2024

Platforms like R and scikit-learn in Python are useful here. It’s something that engineers should always build into models. Algorithms like Random Forests or Gradient Boosting Machines are less sensitive to outliers in general. They use automation tools like the caret package in R and Pipelines in scikit-learn.

Data Drift 52

Data Drift Machine Learning Computer Vision Algorithm 52

The MLOps Blog

MAY 10, 2023

When working on real-world machine learning (ML) use cases, finding the best algorithm/model is not the end of your responsibilities. How to save a trained model in Python? In this section, you will see different ways of saving machine learning (ML) as well as deep learning (DL) models.

Python 106

Python Metadata ML Deep Learning 106

AWS Machine Learning Blog

NOVEMBER 21, 2023

NVIDIA Triton Inference Server provides two different kind backends: one for hosting models on GPU, and a Python backend where you can bring your own custom code to be used in preprocessing and postprocessing steps. Prior to NVIDIA, he worked at the energy industry, focusing on developing algorithms for computational imaging.

ML 82

ML Deep Learning Machine Learning DevOps 82

ODSC - Open Data Science

FEBRUARY 24, 2023

Sweetviz GitHub | Website Sweetviz is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with just two lines of code. Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst.

Data Analysis 126

Data Analysis Business Intelligence Data Science Python 126

AWS Machine Learning Blog

NOVEMBER 9, 2023

We build a model to predict the severity (benign or malignant) of a mammographic mass lesion trained with the XGBoost algorithm using the publicly available UCI Mammography Mass dataset and deploy it using the MLOps framework. She is passionate about developing, deploying, and explaining AI/ ML solutions across various domains.

Data Drift 99

Data Drift Auto-complete ML Automation 99

The MLOps Blog

MARCH 15, 2023

The system used advanced analytics and mostly classic machine learning algorithms to identify patterns and anomalies in claims data that may indicate fraudulent activity. For an experienced Data Scientist/ML engineer, that shouldn’t come as so much of a problem. In our case, this tool helped us to build and test the ML models.

ETL 52

ETL Data Drift Machine Learning ML 52

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

ML 52

ML Python Machine Learning Data Scientist 52

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

ML 52

ML Python Machine Learning Data Scientist 52

Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

Flipboard

SEPTEMBER 4, 2023

Envision yourself as an ML Engineer at one of the world’s largest companies. You make a Machine Learning (ML) pipeline that does everything, from gathering and preparing data to making predictions. Switching gears, imagine yourself being part of a high-tech research lab working with Machine Learning algorithms.

Machine Learning 109

Machine Learning Computer Vision Deep Learning Python 109

AWS Machine Learning Blog

AUGUST 1, 2023

It offers an easy-to-use API and Python SDK, balancing quality and affordability. To start working with a model to learn about the capabilities of ML, all you need to do is open SageMaker Studio, find a pre-trained model you want to use in the Hugging Face Model Hub , and choose SageMaker as your deployment method.

ML 74

ML Large Language Models NLP Natural Language Processing 74

AWS Machine Learning Blog

MAY 5, 2023

Solution overview Ground Truth is a fully self-served and managed data labeling service that empowers data scientists, machine learning (ML) engineers, and researchers to build high-quality datasets. For our example use case, we work with the Fashion200K dataset , released at ICCV 2017.

Metadata 75

Metadata Computer Vision Machine Learning Data Scientist 75

AWS Machine Learning Blog

NOVEMBER 30, 2023

Verifiable evaluation scores are provided across text generation, summarization, classification and question answering tasks, including customer-defined prompt scenarios and algorithms. It also integrates with Machine Learning and Operation (MLOps) workflows in Amazon SageMaker to automate and scale the ML lifecycle. What is FMEval?

Large Language Models 88

Large Language Models LLM Algorithm Responsible AI 88

The MLOps Blog

SEPTEMBER 7, 2023

This situation is not different in the ML world. Data Scientists and ML Engineers typically write lots and lots of code. These combinations of Python code and SQL play a crucial role but can be challenging to keep them robust for their entire lifetime. There’re very different sets of codebases these profiles work with.

Software Engineer 52

Software Engineer Machine Learning ETL ML Engineer 52

Pickl AI

JANUARY 23, 2023

People don’t even need the in-depth knowledge of the various machine learning algorithms as it contains pre-built libraries. It supports languages like Python and R and processes the data with the help of data flow graphs. It is an open-source framework that is written in Python and can efficiently operate on both GPUs and CPUs.

Machine Learning 52

Machine Learning ML Neural Network Auto-classification 52

AWS Machine Learning Blog

JANUARY 25, 2023

Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. Amazon SageMaker Clarify is a feature of Amazon SageMaker that enables data scientists and ML engineers to explain the predictions of their ML models.

Explainability 70

Explainability Natural Language Processing Algorithm Machine Learning 70

TheSequence

MARCH 10, 2024

We are witnessing glimpses of the potential impact of 'AI for science' with models such as those discovering new computer science and math algorithms, or the famous AlphaFold, which is actively used for discovering new proteins. Is AI going to discover everything? Can AI help explain the universe?

LLM 52

LLM Chatbots ML Explainability 52

AWS Machine Learning Blog

FEBRUARY 29, 2024

Configuration files (YAML and JSON) allow ML practitioners to specify undifferentiated code for orchestrating training pipelines using declarative syntax. The following are the key benefits of this solution: Automation – The entire ML workflow, from data preprocessing to model registry, is orchestrated with no manual intervention.

Automation 92

Automation Python Machine Learning ML 92

AWS Machine Learning Blog

NOVEMBER 20, 2023

SageMaker customers can also access built-in Docker images with various pre-installed deep learning frameworks and the necessary Linux, NCCL, and Python packages for model training. region_name}.amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-sagemaker' amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-sagemaker'

Deep Learning 91

Deep Learning Machine Learning ML Data Scientist 91

Viso.ai

FEBRUARY 27, 2024

Since its inception in 2015, the YOLO (You Only Look Once) object-detection algorithm has been closely followed by tech enthusiasts, data scientists, ML engineers, and more, gaining a massive following due to its open-source nature and community contributions. For instance, the YOLOv8m has a 50.2 mAP score at 1.83

Neural Network 52

Neural Network Computer Vision Convolutional Neural Networks Python 52

AWS Machine Learning Blog

OCTOBER 23, 2023

The performance of Talent.com’s matching algorithm is paramount to the success of the business and a key contributor to their users’ experience. The system is developed by a team of dedicated applied machine learning (ML) scientists, ML engineers, and subject matter experts in collaboration between AWS and Talent.com.

NLP 88

NLP Deep Learning Machine Learning Natural Language Processing 88

The MLOps Blog

JUNE 27, 2023

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. For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc.,

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

AWS Machine Learning Blog

JANUARY 17, 2024

The GPU-powered interactive visualizer and Python notebooks provide a seamless way to explore millions of data points in a single window and share insights and results. Amazon SageMaker geospatial capabilities make it easy for data scientists and ML engineers to build, train, and deploy models using geospatial data.

ETL 103

ETL ML Machine Learning Data Scientist 103

Chatbots Life

MAY 12, 2023

We will discuss how models such as ChatGPT will affect the work of software engineers and ML engineers. Will ChatGPT replace software engineers? Will ChatGPT replace ML Engineers? We can ask the model to generate a python function or a recipe for a cheesecake. Will ChatGPT replace ML Engineers?

ChatGPT 105

ChatGPT Software Engineer NLP Prompt Engineer 105

Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. Columbia PhD student Zachary Huang presents JoinBoost, a lightweight Python library that transforms tree training algorithms over normalized databases into pure SQL queries.

Data Scientist 64

Data Scientist AI AI ML 64

Dlabs.ai

OCTOBER 23, 2019

Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. Python and Java often top the list. Python for its simplicity: The language has an abundance of out-the-box libraries to shorten development time. Whereas a language such as JavaScript?

Machine Learning 52

Machine Learning Convolutional Neural Networks ML Python 52

Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. Columbia PhD student Zachary Huang presents JoinBoost, a lightweight Python library that transforms tree training algorithms over normalized databases into pure SQL queries.

Data Scientist 59

Data Scientist AI AI ML 59

The MLOps Blog

AUGUST 3, 2023

This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. You could almost think of Hamilton as DBT for Python functions. Piotr: This is procedural Python code.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

Bugra Akyildiz

MAY 14, 2023

Simulation of consumption of queue up to drivers estimated position becomes an easy simple algorithm and results in wait time classification. Google built a no-code end to end ML based framework called Visual blocks and published a post on this. The Python scientific visualisation landscape is huge.

Auto-classification 52

Auto-classification LLM ML Large Language Models 52

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.

ETL 59

ETL ML Machine Learning Data Scientist 59

AWS Machine Learning Blog

FEBRUARY 5, 2024

SageMaker geospatial capabilities make it easy for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. Incorporating ML with geospatial data enhances the capability to detect anomalies and unusual patterns systematically, which is essential for early warning systems.

ML 83

ML Machine Learning Software Engineer Python 83