Learn About CueObserve: Help You Keep Track Of Your Metrics

CueObserve helps you monitor your metrics. Know when, where, and why a metric isn't right.

CueObserve uses timeseries Anomaly detection to find where and when a metric isn't right. It then offers one-click Root Cause analysis so that you know why a metric isn't right.

CueObserve works with data in your SQL data warehouses and databases. It currently supports Snowflake, BigQuery, Redshift, Druid, Postgres, MySQL, SQL Server and ClickHouse.

Getting Started

Install via Docker

wget https://raw.githubusercontent.com/cuebook/CueObserve/latest_release/docker-compose.yml -q -O cueobserve-docker-compose.ymldocker-compose -f cueobserve-docker-compose.yml up -d

Now visit http://localhost:3000 in your browser.

Demo Video

How it works

You write a SQL GROUP BY query, map its columns as dimensions and measures, and save it as a virtual Dataset.

You then define one or more anomaly detection jobs on the dataset.

When an anomaly detection job runs, CueObserve does the following:

1. Executes the SQL GROUP BY query on your data warehouse and stores the result as a Pandas dataframe.
2. Generates one or more timeseries from the dataframe, as defined in your anomaly detection job.
3. Generates a forecast for each timeseries using Prophet.
4. Creates a visual card for each timeseries. Marks the card as an anomaly if the last data point is anomalous.

Features

• Automated SQL to timeseries transformation.
• Run anomaly detection on the aggregate metric or split it by any dimension. Limit the split to significant dimension values.
• Use Prophet or simple mathematical rules to detect anomalies.
• In-built Scheduler. CueObserve uses Celery as the executor and celery-beat as the scheduler.
• Slack alerts when anomalies are detected.
• Monitoring. Slack alert when a job fails. CueObserve maintains detailed logs.

Limitations

• Currently supports Prophet for timeseries forecasting.
• Not being built for real-time anomaly detection on streaming data.

Support

For general help using CueObserve, read the documentation, or go to Github Discussions.

To report a bug or request a feature, open an issue.

Contributing

We'd love contributions to CueObserve. Before you contribute, please first discuss the change you wish to make via an issue or a discussion. Contributors are expected to adhere to our code of conduct.

Author: cuebook
Source code: https://github.com/cuebook/CueObserve
License: Apache-2.0 License

#cueobserve  #sql 

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Buddha Community

Neural Networks Intuitions: 9. Distance Metric Learning

Welcome back to my series _Neural Networks Intuitions. _In this ninth segment, we will be looking into deep distance metric learning, the motivation behind using it, wide range of methods proposed and its applications.

Note: All techniques discussed in this article comes under Deep Metric Learning (DML) i.e distance metric learning using neural networks.

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Distance Metric Learning:

Distance Metric Learning means learning a distance in a low dimensional space which is consistent with the notion of semantic similarity. (as given in [No Fuss Distance Metric Learning using Proxies])

What does the above statement mean w.r.t image domain?

It means learning a distance in a low dimensional space(non-input space) such that similar images in the input space result in similar representation(low distance) and dissimilar images result in varied representation(high distance).

Okay, this sounds exactly what a classifier does. Isn’t it? Yes.

So how is this different from supervised image classification? Why different terminology?

Metric learning addresses the problem of open-set setup in machine learning i.e generalize to new examples at test time.

This is not possible by a feature-extractor followed by fully connected layer Classification network.

Why?

This is a very important question. The answer is as follows:

1. A classifier learns**class-specific features and not necessarily generic features.**
2. A classifier with a standard cross entropy loss maximizes inter-class distances such that the features before FC layer are linearly separable.

#metric-learning #deep-learning #siamese-networks #triplet-loss #representation-learning #deep learning

Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Learn About CueObserve: Help You Keep Track Of Your Metrics

CueObserve helps you monitor your metrics. Know when, where, and why a metric isn't right.

CueObserve uses timeseries Anomaly detection to find where and when a metric isn't right. It then offers one-click Root Cause analysis so that you know why a metric isn't right.

CueObserve works with data in your SQL data warehouses and databases. It currently supports Snowflake, BigQuery, Redshift, Druid, Postgres, MySQL, SQL Server and ClickHouse.

Getting Started

Install via Docker

wget https://raw.githubusercontent.com/cuebook/CueObserve/latest_release/docker-compose.yml -q -O cueobserve-docker-compose.ymldocker-compose -f cueobserve-docker-compose.yml up -d

Now visit http://localhost:3000 in your browser.

Demo Video

How it works

You write a SQL GROUP BY query, map its columns as dimensions and measures, and save it as a virtual Dataset.

You then define one or more anomaly detection jobs on the dataset.

When an anomaly detection job runs, CueObserve does the following:

1. Executes the SQL GROUP BY query on your data warehouse and stores the result as a Pandas dataframe.
2. Generates one or more timeseries from the dataframe, as defined in your anomaly detection job.
3. Generates a forecast for each timeseries using Prophet.
4. Creates a visual card for each timeseries. Marks the card as an anomaly if the last data point is anomalous.

Features

• Automated SQL to timeseries transformation.
• Run anomaly detection on the aggregate metric or split it by any dimension. Limit the split to significant dimension values.
• Use Prophet or simple mathematical rules to detect anomalies.
• In-built Scheduler. CueObserve uses Celery as the executor and celery-beat as the scheduler.
• Slack alerts when anomalies are detected.
• Monitoring. Slack alert when a job fails. CueObserve maintains detailed logs.

Limitations

• Currently supports Prophet for timeseries forecasting.
• Not being built for real-time anomaly detection on streaming data.

Support

For general help using CueObserve, read the documentation, or go to Github Discussions.

To report a bug or request a feature, open an issue.

Contributing

We'd love contributions to CueObserve. Before you contribute, please first discuss the change you wish to make via an issue or a discussion. Contributors are expected to adhere to our code of conduct.

Author: cuebook
Source code: https://github.com/cuebook/CueObserve
License: Apache-2.0 License

#cueobserve  #sql 

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert

Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

• Temporal difference learning (TD learning)
• Parameters
• QL & SARSA
• Comparison
• Implementation
• Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning