IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. It signifies a shift in human-digital interaction, offering enterprises innovative ways to engage with their audience, optimize operations, and further personalize their customer experience. billion by 2030.

Enterprise 104

Enterprise Interactive Data-driven Key Performance Indicator 104

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 59

Machine Learning Data Science Statistics Deep Learning 59

O'Reilly on Data

MARCH 31, 2020

This has serious implications for software testing, versioning, deployment, and other core development processes. At measurement-obsessed companies, every part of their product experience is quantified and adjusted to optimize user experience. These companies eventually moved beyond using data to inform product design decisions.

Management 332

Management Machine Learning Experimentation Metrics 332

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. Choosing your loss function and optimizer Finally, in the last block of code, we must compile the model that we just built.

Metrics 58

Metrics Optimization Modeling Testing 58

Cloudera

FEBRUARY 16, 2022

They define each stage from data ingest, feature engineering, model building, testing, deployment and validation. Figure 04: Applied Machine Learning Prototypes (AMPs). By logging the performance of every combination of search parameters within an experiment, we can choose the optimal set of parameters when building a model.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning in marketing and sales According to Forbes , marketing and sales teams prioritize AI and ML more than any other enterprise department. Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). Then, it can tailor marketing materials to match those interests.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

Domino Data Lab

JULY 28, 2021

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. In fact, deep learning was first described theoretically in 1943. Reinforcement learning focuses on optimizing a specific decision.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

Edwin Chen

OCTOBER 6, 2014

Click-through rate may be your initial hope…but after a bit of thought, it's not clear that it's the best metric after all. Metrics like CTR, or even number of favorites and retweets, will probably optimize for showing quick one-liners and pictures of funny cats. Amazon, and Moving Beyond Log-Based Metrics.

Metrics 79

Metrics Measurement Machine Learning Testing 79

DataKitchen

APRIL 13, 2021

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Testing and Data Observability. Production Monitoring and Development Testing.

Testing 307

Testing Machine Learning Consulting Data Quality 307

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

O'Reilly on Data

JUNE 19, 2019

In a previous post , we noted some key attributes that distinguish a machine learning project: Unlike traditional software where the goal is to meet a functional specification, in ML the goal is to optimize a metric. A catalog or a database that lists models, including when they were tested, trained, and deployed.

Modeling 194

Modeling Machine Learning Testing Metrics 194

DataRobot Blog

APRIL 22, 2022

In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. testing every possible combination) Hyperparameter tuning is beneficial to some extent, but the real efficiency gains are in finding the right data.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

Smarten

APRIL 19, 2024

Benefits include customized and optimized models, data, parameters and tuning. Anomaly Alerts KPI monitoring and Auto Insights allows business users to quickly establish KPIs and target metrics and identify the Key Influencers and variables for the target KPI.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

AWS Big Data

OCTOBER 26, 2023

Some recent examples of performance optimizations driven by fleet telemetry include: String query optimizations – By analyzing how Amazon Redshift processed different data types in the Redshift fleet, we found that optimizing string-heavy queries would bring significant benefit to our customers’ workloads.

Data Warehouse 91

Data Warehouse Cost-Benefit Dashboards Optimization 91

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

For example, consider the following simple example fitting a two-dimensional function to predict if someone will pass the bar exam based just on their GPA (grades) and LSAT (a standardized test) using the public dataset (Wightman, 1998). Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Domino Data Lab

APRIL 21, 2021

deep learning) there is no guaranteed explainability. from sklearn import metrics. This is to prevent any information leakage into our test set. 2f%% of the test set." 2f%% of the test set." Fraudulent transactions are 0.17% of the test set. 2f%% of the test set." Model training.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

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

DataRobot Blog

DECEMBER 20, 2022

In this context, an augmented intelligence approach around the data will be increasingly more critical for asset managers, investors, and real estate developers to ensure a better understanding of the real estate assets and take better decisions aimed at optimizing both the Net Asset Value and the Net Operating Income.

Management 59

Management Machine Learning Optimization Modeling 59

Domino Data Lab

MARCH 3, 2019

What metrics are used to evaluate success? I’m here mostly to provide McLuhan quotes and test the patience of our copy editors with hella Californian colloquialisms. The data types used in deep learning are interesting. The data types used in deep learning are interesting. Who builds their models?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

MAY 8, 2019

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. If your “performance” metrics are focused on predictive power, then you’ll probably end up with more complex models, and consequently less interpretable ones.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

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