IBM Big Data Hub

JULY 6, 2023

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. Python is the most common programming language used in machine learning.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Sisense

MAY 26, 2020

For more details, refer to this article. While training a model for NLP, words not present in the training data commonly appear in the test data. Because of this, predictions made using test data may not be correct. Test data then contains this sentence: Pasta is delicious. For more details, check out this article.

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98

Sisense

AUGUST 6, 2020

In this article, we’ll dig into the ways AI can help you accomplish these goals, allowing you and your team to envision the future of your product or service. An obvious mechanical answer is: use relevance as a metric. Another important method is to benchmark existing metrics. Improving performance with AI. The perfect fit.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Cloudera

FEBRUARY 16, 2022

In this article, we explore model governance, a function of ML Operations (MLOps). We will learn what it is, why it is important and how Cloudera Machine Learning (CML) is helping organisations tackle this challenge as part of the broader objective of achieving Ethical AI. Figure 04: Applied Machine Learning Prototypes (AMPs).

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

O'Reilly on Data

JANUARY 25, 2021

Both SRE and DevOps emphasize similar practices: version control (62% growth for GitHub, and 48% for Git), testing (high usage, though no year-over-year growth), continuous deployment (down 20%), monitoring (up 9%), and observability (up 128%). It’s particularly difficult if testing includes issues like fairness and bias.

Machine Learning 363

Machine Learning Software Testing Technology 363

Domino Data Lab

JUNE 23, 2019

Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. It is similar to R&D. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Insight

MARCH 25, 2020

After reading this, I hope you can learn how to build deep learning models using TensorFlow Keras, productionalize the model as a Streamlit app, and deploy it as a Docker container on Google Cloud Platform (GCP) using Google Kubernetes Engines (GKE). In this project, I was curious to see if deep learning approaches?—?specifically

Deep Learning 53

Deep Learning Metrics Data Processing Interactive 53

Domino Data Lab

APRIL 21, 2021

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. deep learning) there is no guaranteed explainability. from sklearn import metrics. 2f%% of the test set."

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

MARCH 3, 2019

Seriously, this entire article merely skims the surface of those reports. Check the end of this article for key guidance synthesized from the practices of the leaders in the field. Ben and I also wrote articles for each of the surveys, summarizing the highlights. What metrics are used to evaluate success? Or something.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

JULY 28, 2021

In this article, take a deep dive into data science and how Domino’s Enterprise MLOps platform allows you to scale data science in your business. Communication cannot be emphasized enough, for it is this trait that ensures results are effectively translated from the white board to impact on business metrics.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

Insight

JANUARY 14, 2020

Keras is an open source deep learning API that was written in Python and runs on top of Tensorflow, so it’s a little more user-friendly and high-level than Tensorflow. Check out this great Medium article that discusses the difference between tensors and multidimensional arrays. Check out the Keras documentation here.

Metrics 58

Metrics Optimization Modeling Testing 58

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Introduction. Welcome back to our monthly burst of themes and conferences.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

DataRobot Blog

DECEMBER 20, 2022

In this article, we’ll showcase the ability of AI to improve the quality of the potential investment’s future performance, with a specific example from the real estate segment. In this article, we’ll first take a closer look at the concept of Real Estate Data Intelligence and the potential of AI to become a game changer in this niche.

Management 59

Management Machine Learning Optimization Modeling 59

DataRobot Blog

FEBRUARY 2, 2023

The creation of foundation models is one of the key developments in the field of large language models that is creating a lot of excitement and interest amongst data scientists and machine learning engineers. These models are trained on massive amounts of text data using deep learning algorithms.

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Modeling Machine Learning Data Processing Interactive 52