The presentation describes how Machine Learning Algorithms can be automated through a Flask Web API. It represents the effectivity of machine learning automation that would reduce operation time dramatically.
Flink Forward SF 2017: Erik de Nooij - StreamING models, how ING adds models ...Flink Forward
These days companies are collecting more and more data. It’s up to data scientists to create business value out of that data. Typically this is done by training models based on historical data stored on HDFS. Once the model has been trained it is ready to be scored. At ING Bank we need to score models in real time, blocking potential fraudulent transactions before causing damage to either the customer or the bank. As fraudsters invent new ways to commit fraud, we also need to add new models on a running system, without downtime. In this talk we’ll present our implementation of a real time streaming analytics platform that enables us to dynamically change the behaviour of our stateful Flink application. The end result is an environment where end users are provided a DSL they can use to dynamically stream in new models into the Flink job as well as to change the transformations within the operators. This will give them full control of the streaming analytics platform at runtime.
CyberMLToolkit: Anomaly Detection as a Scalable Generic Service Over Apache S...Databricks
Cybercrime is one the greatest threats to every company in the world today and a major problem for mankind in general. The damage due to Cybercrime is estimated to be around $6 Trillion By 2021. Security professionals are struggling to cope with the threat. As a result, powerful and easy to use tools are necessary to aid in this battle. For this purpose we created an anomaly detection framework focused on security which can identify anomalous access patterns. It is built on top of Apache Spark and can be applied in parallel over multiple tenants. This allows the model to be trained over the data of thousands of customers over a Databricks cluster within less than an hour. The model leverages proven technologies from Recommendation Engines to produce high quality anomalies. We thoroughly evaluated the model’s ability to identify actual anomalies by using synthetically generated data and also by creating an actual attack and showing that the model clearly identifies the attack as anomalous behavior. We plan to open source this library as part of a cyber-ML toolkit we will be offering.
Detecting Financial Fraud at Scale with Machine LearningDatabricks
Detecting fraudulent patterns at scale is a challenge given the massive amounts of data to sift through, the complexity of the constantly evolving techniques, and the very small number of actual examples of fraudulent behavior. In finance, added security concerns and the importance of explaining how fraudulent behavior was identified further increases the difficulty of the task. Legacy systems rely on rule-based detection that is difficult to implement and run at scale. The resulting code is very complex and brittle, making it difficult to update to keep up with new threats.
In this talk, we will go over how to convert a rule based financial fraud detection program to use machine learning on Spark as part of a scalable, modular solution. We will examine how to identify appropriate features and labels and how to create a feedback loop that will allow the model to evolve and improve overtime. We will also look at how MLflow may be leveraged throughout this effort for experiment tracking and model deployment.
Specifically, we will discuss:
-How to create a fraud-detection data pipeline
-How to leverage a framework for building features from large datasets
-How to create modular code to re-use and maintain new machine learning models
-How to choose appropriate models and algorithms for a given fraud-detection problem
Auto-Pilot for Apache Spark Using Machine LearningDatabricks
At Qubole, users run Spark at scale on cloud (900+ concurrent nodes). At such scale, for efficiently running SLA critical jobs, tuning Spark configurations is essential. But it continues to be a difficult undertaking, largely driven by trial and error. In this talk, we will address the problem of auto-tuning SQL workloads on Spark. The same technique can also be adapted for non-SQL Spark workloads. In our earlier work[1], we proposed a model based on simple rules and insights. It was simple yet effective at optimizing queries and finding the right instance types to run queries. However, with respect to auto tuning Spark configurations we saw scope of improvement. On exploration, we found previous works addressing auto-tuning using Machine learning techniques. One major drawback of the simple model[1] is that it cannot use multiple runs of query for improving recommendation, whereas the major drawback with Machine Learning techniques is that it lacks domain specific knowledge. Hence, we decided to combine both techniques. Our auto-tuner interacts with both models to arrive at good configurations. Once user selects a query to auto tune, the next configuration is computed from models and the query is run with it. Metrics from event log of the run is fed back to models to obtain next configuration. Auto-tuner will continue exploring good configurations until it meets the fixed budget specified by the user. We found that in practice, this method gives much better configurations compared to configurations chosen even by experts on real workload and converges soon to optimal configuration. In this talk, we will present a novel ML model technique and the way it was combined with our earlier approach. Results on real workload will be presented along with limitations and challenges in productionizing them. [1] Margoor et al,'Automatic Tuning of SQL-on-Hadoop Engines' 2018,IEEE CLOUD
Real-Time Fraud Detection at Scale—Integrating Real-Time Deep-Link Graph Anal...Databricks
As data grows in size and connectedness dramatically in all dimensions, the potential for graph-enriched machine learning grows likewise, but scalable technologies are needed to both build models and apply them in real-time. Real-time deep-link graph pattern matching and analytics provides new opportunities for enriching your machine learning models with graph features.
‘In addition to the real-time deep-link aspect, the ability to process large datasets in a production pipeline provides a synergistic approach for the two distributed and performant platforms: Spark and TigerGraph. The TigerGraph graph database provides scalable real-time deep link graph analytics and augments Spark with graph analytics and predictions for a wide range of Machine Learning use cases.
In this session, we will explain the architecture and technical implementation for a TigerGraph+Spark graph-enhanced Machine Learning pipeline: Use TigerGraph both before training to extract (graph and non-graph) features and after training to apply the model on streaming data; use Spark to train and tune machine learning models at scale. As an example, we will present a solution in production at China Mobile that detects and prevents phone-based scams using machine learning with TigerGraph.
Specifically, the solution generates 118 graph features for 600 million users, to feed a machine learning system which detects three types of unwanted phone calls. TigerGraph then helps to deploy the model by extracting these 118 features in real-time for up to 10,000 calls per second, to give customers a real-time diagnosis of their incoming calls.
Flink Forward SF 2017: Erik de Nooij - StreamING models, how ING adds models ...Flink Forward
These days companies are collecting more and more data. It’s up to data scientists to create business value out of that data. Typically this is done by training models based on historical data stored on HDFS. Once the model has been trained it is ready to be scored. At ING Bank we need to score models in real time, blocking potential fraudulent transactions before causing damage to either the customer or the bank. As fraudsters invent new ways to commit fraud, we also need to add new models on a running system, without downtime. In this talk we’ll present our implementation of a real time streaming analytics platform that enables us to dynamically change the behaviour of our stateful Flink application. The end result is an environment where end users are provided a DSL they can use to dynamically stream in new models into the Flink job as well as to change the transformations within the operators. This will give them full control of the streaming analytics platform at runtime.
CyberMLToolkit: Anomaly Detection as a Scalable Generic Service Over Apache S...Databricks
Cybercrime is one the greatest threats to every company in the world today and a major problem for mankind in general. The damage due to Cybercrime is estimated to be around $6 Trillion By 2021. Security professionals are struggling to cope with the threat. As a result, powerful and easy to use tools are necessary to aid in this battle. For this purpose we created an anomaly detection framework focused on security which can identify anomalous access patterns. It is built on top of Apache Spark and can be applied in parallel over multiple tenants. This allows the model to be trained over the data of thousands of customers over a Databricks cluster within less than an hour. The model leverages proven technologies from Recommendation Engines to produce high quality anomalies. We thoroughly evaluated the model’s ability to identify actual anomalies by using synthetically generated data and also by creating an actual attack and showing that the model clearly identifies the attack as anomalous behavior. We plan to open source this library as part of a cyber-ML toolkit we will be offering.
Detecting Financial Fraud at Scale with Machine LearningDatabricks
Detecting fraudulent patterns at scale is a challenge given the massive amounts of data to sift through, the complexity of the constantly evolving techniques, and the very small number of actual examples of fraudulent behavior. In finance, added security concerns and the importance of explaining how fraudulent behavior was identified further increases the difficulty of the task. Legacy systems rely on rule-based detection that is difficult to implement and run at scale. The resulting code is very complex and brittle, making it difficult to update to keep up with new threats.
In this talk, we will go over how to convert a rule based financial fraud detection program to use machine learning on Spark as part of a scalable, modular solution. We will examine how to identify appropriate features and labels and how to create a feedback loop that will allow the model to evolve and improve overtime. We will also look at how MLflow may be leveraged throughout this effort for experiment tracking and model deployment.
Specifically, we will discuss:
-How to create a fraud-detection data pipeline
-How to leverage a framework for building features from large datasets
-How to create modular code to re-use and maintain new machine learning models
-How to choose appropriate models and algorithms for a given fraud-detection problem
Auto-Pilot for Apache Spark Using Machine LearningDatabricks
At Qubole, users run Spark at scale on cloud (900+ concurrent nodes). At such scale, for efficiently running SLA critical jobs, tuning Spark configurations is essential. But it continues to be a difficult undertaking, largely driven by trial and error. In this talk, we will address the problem of auto-tuning SQL workloads on Spark. The same technique can also be adapted for non-SQL Spark workloads. In our earlier work[1], we proposed a model based on simple rules and insights. It was simple yet effective at optimizing queries and finding the right instance types to run queries. However, with respect to auto tuning Spark configurations we saw scope of improvement. On exploration, we found previous works addressing auto-tuning using Machine learning techniques. One major drawback of the simple model[1] is that it cannot use multiple runs of query for improving recommendation, whereas the major drawback with Machine Learning techniques is that it lacks domain specific knowledge. Hence, we decided to combine both techniques. Our auto-tuner interacts with both models to arrive at good configurations. Once user selects a query to auto tune, the next configuration is computed from models and the query is run with it. Metrics from event log of the run is fed back to models to obtain next configuration. Auto-tuner will continue exploring good configurations until it meets the fixed budget specified by the user. We found that in practice, this method gives much better configurations compared to configurations chosen even by experts on real workload and converges soon to optimal configuration. In this talk, we will present a novel ML model technique and the way it was combined with our earlier approach. Results on real workload will be presented along with limitations and challenges in productionizing them. [1] Margoor et al,'Automatic Tuning of SQL-on-Hadoop Engines' 2018,IEEE CLOUD
Real-Time Fraud Detection at Scale—Integrating Real-Time Deep-Link Graph Anal...Databricks
As data grows in size and connectedness dramatically in all dimensions, the potential for graph-enriched machine learning grows likewise, but scalable technologies are needed to both build models and apply them in real-time. Real-time deep-link graph pattern matching and analytics provides new opportunities for enriching your machine learning models with graph features.
‘In addition to the real-time deep-link aspect, the ability to process large datasets in a production pipeline provides a synergistic approach for the two distributed and performant platforms: Spark and TigerGraph. The TigerGraph graph database provides scalable real-time deep link graph analytics and augments Spark with graph analytics and predictions for a wide range of Machine Learning use cases.
In this session, we will explain the architecture and technical implementation for a TigerGraph+Spark graph-enhanced Machine Learning pipeline: Use TigerGraph both before training to extract (graph and non-graph) features and after training to apply the model on streaming data; use Spark to train and tune machine learning models at scale. As an example, we will present a solution in production at China Mobile that detects and prevents phone-based scams using machine learning with TigerGraph.
Specifically, the solution generates 118 graph features for 600 million users, to feed a machine learning system which detects three types of unwanted phone calls. TigerGraph then helps to deploy the model by extracting these 118 features in real-time for up to 10,000 calls per second, to give customers a real-time diagnosis of their incoming calls.
Graph-Powered Machine Learning - Meetup Paris - March 5, 2018
Graph -based machine learning is becoming an important trend in artificial intelligence, transcending a lot of other techniques. Using graphs as a basic representation of data for multiple purposes:
- the data is already modeled for further analysis
- graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs;
- improving computation performances and quality. The talk will present these advantages and present applications in the context of recommendation engines and natural language processing.
Speaker: Dr. Vlasta Kus (@VlastaKus) is a Data Scientist at GraphAware, specializing in graph-based Natural Language Processing and related topics, including deep learning techniques. He speaks English, Czech and some French and currently lives in Prague.
Churn prediction is big business. It minimizes customer defection by predicting which customers are likely to cancel a service. Though originally used within the telecommunications industry, it has become common practice for banks, ISPs, insurance firms, and other verticals. More: http://info.mapr.com/WB_PredictingChurn_Global_DG_17.06.15_RegistrationPage.html
The prediction process is data-driven and often uses advanced machine learning techniques. In this webinar, we'll look at customer data, do some preliminary analysis, and generate churn prediction models – all with Spark machine learning (ML) and a Zeppelin notebook.
Spark’s ML library goal is to make machine learning scalable and easy. Zeppelin with Spark provides a web-based notebook that enables interactive machine learning and visualization.
In this tutorial, we'll do the following:
Review classification and decision trees
Use Spark DataFrames with Spark ML pipelines
Predict customer churn with Apache Spark ML decision trees
Use Zeppelin to run Spark commands and visualize the results
Native ads (ads that match the look and feel of the embedding page) have become a multi-billion dollar business in recent years. Gemini native is Yahoo’s native advertisement platform and this talk will overview some of the science behind its ad ranking.
The accurate prediction of an ad’s click-through rate (CTR) for a given impression is a key component of any such ad ranking system as it allows one to rank the ads according to their expected revenue. I will give a short overview of different CTR prediction models and deep dive into the major components of large-scale logistic regression models; a special focus will be given to implementing such a logistic regression model in Apache Spark.
Augmenting Machine Learning with Databricks Labs AutoML ToolkitDatabricks
Instead of better understanding and optimizing their machine learning models, data scientists spend a majority of their time training and iterating through different models even in cases where there the data is reliable and clean. Important aspects of creating an ML model include (but are not limited to) data preparation, feature engineering, identifying the correct models, training (and continuing to train) and optimizing their models. This process can be (and often is) laborious and time-consuming.
In this session, we will explore this process and then show how the AutoML toolkit (from Databricks Labs) can significantly simplify and optimize machine learning. We will demonstrate all of this financial loan risk data with code snippets and notebooks that will be free to download.
This session took place at New York City on November 4th, 2019.
Speaker Bio:
Chemere is a Senior Data Science Training Specialist for H2O.ai. Chemere has a Master's in Business Administration with focus in Marketing Analytics from the University of North Carolina at Charlotte. She is an experienced data scientist with a diverse background in transformational decision-making in various industries including Banking, Manufacturing, Logistics, and Medical Devices. Chemere joins us from Venus Concept/2two5, where she was the Lead Data Scientist focused on building predictive models with Internet of Things (IoT) data and for a subscription-based marketing product for B2B customers. Prior to that, Chemere worked as a Senior Data Scientist at Wells Fargo Bank focused on various applied predictive analytic solutions.
More details about the event can be had here: https://www.eventbrite.com/e/dive-into-h2o-new-york-tickets-76351721053
The Road to Production: Automating your Anomaly Detectors - by jao (Jose A. Ortega), Co-Founder and Chief Technology Officer at BigML.
*Machine Learning School in The Netherlands 2022.
This is an elaborate presentation on how to predict employee attrition using various machine learning models. This presentation will take you through the process of statistical model building using Python.
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regre...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regression Model Performance, Optimizing Support Vector Machine Classifier, Accuracy of results and efficiency, Logistic Regression Feature Importance, interpretation of support vectors, Density Graph
AWS re:Invent 2016: Auto Scaling – the Fleet Management Solution for Planet E...Amazon Web Services
Scaling allows cloud resources to scale automatically in reaction to the dynamic needs of customers. This session will show how Auto Scaling offers an advantage to everyone – whether it’s basic fleet management to keep instances healthy as an EC2 best practice, or dynamic scaling to manage “extremes”. We’ll share examples of how Auto Scaling is helping customers of all sizes and industries unlock use cases and value. We’ll also discuss how Auto Scaling is evolving to scaling different types of elastic AWS resources beyond EC2 instances. NASA Jet Propulsion Laboratory (JPL) / California Institute of Technology will share how Auto Scaling is used to scale science data processing of Interferometric Synthetic Aperture Radar (InSAR) data from earth-observing satellite missions, and reduce response times during hazard response events such as those from earthquakes, floods, and volcanoes. JPL will also discuss how they are integrating their science data systems with the AWS ecosystem to expand into NASA’s next two large-scale missions with remote-sensing radar-based observations. Learn how Auto Scaling is being used at a global scale – and beyond!
Comparative Study of Machine Learning Algorithms for Sentiment Analysis with ...Sagar Deogirkar
Comparing the State-of-the-Art Deep Learning with Machine Learning algorithms performance on TF-IDF vector creation for Sentiment Analysis using Airline Tweeter Data Set.
Graph-Powered Machine Learning - Meetup Paris - March 5, 2018
Graph -based machine learning is becoming an important trend in artificial intelligence, transcending a lot of other techniques. Using graphs as a basic representation of data for multiple purposes:
- the data is already modeled for further analysis
- graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs;
- improving computation performances and quality. The talk will present these advantages and present applications in the context of recommendation engines and natural language processing.
Speaker: Dr. Vlasta Kus (@VlastaKus) is a Data Scientist at GraphAware, specializing in graph-based Natural Language Processing and related topics, including deep learning techniques. He speaks English, Czech and some French and currently lives in Prague.
Churn prediction is big business. It minimizes customer defection by predicting which customers are likely to cancel a service. Though originally used within the telecommunications industry, it has become common practice for banks, ISPs, insurance firms, and other verticals. More: http://info.mapr.com/WB_PredictingChurn_Global_DG_17.06.15_RegistrationPage.html
The prediction process is data-driven and often uses advanced machine learning techniques. In this webinar, we'll look at customer data, do some preliminary analysis, and generate churn prediction models – all with Spark machine learning (ML) and a Zeppelin notebook.
Spark’s ML library goal is to make machine learning scalable and easy. Zeppelin with Spark provides a web-based notebook that enables interactive machine learning and visualization.
In this tutorial, we'll do the following:
Review classification and decision trees
Use Spark DataFrames with Spark ML pipelines
Predict customer churn with Apache Spark ML decision trees
Use Zeppelin to run Spark commands and visualize the results
Native ads (ads that match the look and feel of the embedding page) have become a multi-billion dollar business in recent years. Gemini native is Yahoo’s native advertisement platform and this talk will overview some of the science behind its ad ranking.
The accurate prediction of an ad’s click-through rate (CTR) for a given impression is a key component of any such ad ranking system as it allows one to rank the ads according to their expected revenue. I will give a short overview of different CTR prediction models and deep dive into the major components of large-scale logistic regression models; a special focus will be given to implementing such a logistic regression model in Apache Spark.
Augmenting Machine Learning with Databricks Labs AutoML ToolkitDatabricks
Instead of better understanding and optimizing their machine learning models, data scientists spend a majority of their time training and iterating through different models even in cases where there the data is reliable and clean. Important aspects of creating an ML model include (but are not limited to) data preparation, feature engineering, identifying the correct models, training (and continuing to train) and optimizing their models. This process can be (and often is) laborious and time-consuming.
In this session, we will explore this process and then show how the AutoML toolkit (from Databricks Labs) can significantly simplify and optimize machine learning. We will demonstrate all of this financial loan risk data with code snippets and notebooks that will be free to download.
This session took place at New York City on November 4th, 2019.
Speaker Bio:
Chemere is a Senior Data Science Training Specialist for H2O.ai. Chemere has a Master's in Business Administration with focus in Marketing Analytics from the University of North Carolina at Charlotte. She is an experienced data scientist with a diverse background in transformational decision-making in various industries including Banking, Manufacturing, Logistics, and Medical Devices. Chemere joins us from Venus Concept/2two5, where she was the Lead Data Scientist focused on building predictive models with Internet of Things (IoT) data and for a subscription-based marketing product for B2B customers. Prior to that, Chemere worked as a Senior Data Scientist at Wells Fargo Bank focused on various applied predictive analytic solutions.
More details about the event can be had here: https://www.eventbrite.com/e/dive-into-h2o-new-york-tickets-76351721053
The Road to Production: Automating your Anomaly Detectors - by jao (Jose A. Ortega), Co-Founder and Chief Technology Officer at BigML.
*Machine Learning School in The Netherlands 2022.
This is an elaborate presentation on how to predict employee attrition using various machine learning models. This presentation will take you through the process of statistical model building using Python.
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regre...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regression Model Performance, Optimizing Support Vector Machine Classifier, Accuracy of results and efficiency, Logistic Regression Feature Importance, interpretation of support vectors, Density Graph
AWS re:Invent 2016: Auto Scaling – the Fleet Management Solution for Planet E...Amazon Web Services
Scaling allows cloud resources to scale automatically in reaction to the dynamic needs of customers. This session will show how Auto Scaling offers an advantage to everyone – whether it’s basic fleet management to keep instances healthy as an EC2 best practice, or dynamic scaling to manage “extremes”. We’ll share examples of how Auto Scaling is helping customers of all sizes and industries unlock use cases and value. We’ll also discuss how Auto Scaling is evolving to scaling different types of elastic AWS resources beyond EC2 instances. NASA Jet Propulsion Laboratory (JPL) / California Institute of Technology will share how Auto Scaling is used to scale science data processing of Interferometric Synthetic Aperture Radar (InSAR) data from earth-observing satellite missions, and reduce response times during hazard response events such as those from earthquakes, floods, and volcanoes. JPL will also discuss how they are integrating their science data systems with the AWS ecosystem to expand into NASA’s next two large-scale missions with remote-sensing radar-based observations. Learn how Auto Scaling is being used at a global scale – and beyond!
Comparative Study of Machine Learning Algorithms for Sentiment Analysis with ...Sagar Deogirkar
Comparing the State-of-the-Art Deep Learning with Machine Learning algorithms performance on TF-IDF vector creation for Sentiment Analysis using Airline Tweeter Data Set.
Exploration of Supervised Machine Learning Techniques for Runtime Selection o...Akihiro Hayashi
Fourth Workshop on Accelerator Programming Using Directives (WACCPD2017, co-located with SC17)
While multi-core CPUs and many-core GPUs are both viable platforms for parallel computing, programming models for them can impose large burdens upon programmers due to their complex and low-level APIs. Since managed languages like Java are designed to be run on multiple platforms, parallel language constructs and APIs such as Java 8 Parallel Stream APIs can enable high-level parallel programming with the promise of performance portability for mainstream (“non-ninja”) programmers. To achieve this goal, it is important for the selection of the hardware device to be automated rather than be specified by the programmer, as is done in current programming models. Due to a variety of factors affecting performance, predicting a preferable device for faster performance of individual kernels remains a difficult problem. While a prior approach uses machine learning to address this challenge, there is no comparable study on good supervised machine learning algorithms and good program features to track. In this paper, we explore 1) program features to be extracted by a compiler and 2) various machine learning techniques that improve accuracy in prediction, thereby improving performance. The results show that an appropriate selection of program features and machine learning algorithm can further improve accuracy. In particular, support vector machines (SVMs), logistic regression, and J48 decision tree are found to be reliable techniques for building accurate prediction models from just two, three, or four program features, achieving accuracies of 99.66%, 98.63%, and 98.28% respectively from 5-fold-cross-validation.
Lab 2: Classification and Regression Prediction Models, training and testing ...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Lab 2: Classification and Regression Prediction Models, training and testing splits, optimization of K Nearest Neighbors (KD tree), optimization of Random Forest, optimization of Naive Bayes (Gaussian), advantages and model comparisons, feature importance, Feature ranking with recursive feature elimination, Two dimensional Linear Discriminant Analysis
How to Use a Semantic Layer on Big Data to Drive AI & BI ImpactDATAVERSITY
Learn about using a semantic layer to make data accessible and how to accelerate the business impact of AI and BI at your organization.
This session will offer practical advice on how to drive AI & BI business outcomes with an effective data strategy that leverages a semantic layer.
You will learn how to achieve quantifiable results by modernizing your data and analytics stack with a semantic layer that delivers an order of magnitude better query performance, increased data team productivity, lower query compute costs, and improved Speed-to-Insights.
Attend this session to learn about:
- Gaining business alignment and reducing data prep for your AI and BI teams.
- Making a consistent set of business metrics “analytics-ready” and accessible.
- Accelerating end-to-end query performance while optimizing cloud resources.
- Treating “data as a product” and how to drive business value for all consumers.
Similar to Machine Learning Automation using Flask API (20)
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
1. DRIVERLESS ML
Automation of ML using Driverless API
Sayantan Ghosh
Kalinga Institute Of Industrial
technology
2. Key Capabilities of Driverless API
1
2
It can produce interactive
graphical visualization using
advanced pygal library.
It can preprocess the dataset
very efficiently.( Ex-It can handle
categorical Data as well as NaN
or missing Values).
It can do Feature Scaling very
efficienty to increase
accuracy and acceptbility.
The API can process
dataset analization in a
very less amount of time.
3
4
It can be used for Binary as well as
Multiclass Classification, Churn
Modeling, Credit Card Fraud
Detection, Marketing Analysis.
It can preprocess the dataset very
efficiently.( Ex-It can handle
categorical Data as well as NaN or
missing Values).
Data Preprocessingt
Visualization
Time Efficient
Feature Scaling
5
6
2
4. Methodology &
Implementations
WorkFlow Diagram of the API
4
Data Collection Stage
.csv Split
Amount Epoch
Featur Selection &
Dimensionality Reduction
Compute the feature Importances and reduce the
dataset using relevant features
Data Preprocessing
(Categorical,Missing Value Handling)
Merging of Classification
Algorithm
All the ML Classifiers are implemented into the dataset
through K-Fold Cross Validation and results are stored.
Analyzation Report &
Visualization of Predicted results
using pygal
All the Categorical datas are One Hot Encoded and
Missing Values are handed using mean values..
At the Input Phase the user will Provide the .csv
file, Split amount of the dataset and the epoch
Count and the optimizer Algorithms.
5. Keras Flask Scikit-Learn
Keras is used for implementing the
Artificial Neural Network.
Flask is used for implementing
the Web API.
Scikit-lEarn is used for Implementing the
overall Classification Algorithms and overall
inn the preprocessing Phase.
TECHNOLOGIES USED
5
Pygal is used for implementing the
visualizations using Support Vector
Graphics
1
4 Pygal
Numpy is used for computing the
numeracal Operations.
5 Numpy5
Pandas is used for Implementing all the
DataFrame processing.
6 Pandas
6. Automation Of Classification Algorithms
6
For the Automation Process I have used 6 Classification Algorithm and each Algorithm is
feed into the K-Fold Cross Validation into 10 Splits.
Accuracy Comparasion of
Classification Algorithms which can
help to choose proper classifiers in
less amount of Time.
K-Fold
Cross
Validation
(10 splits)
7. Result Analysis
On
Various Datasets
Dataset : titanic_train.csv
Target Column : Survived
Split Amount: 0.3
Epoch Count: 100
Optimizer : adam
7
79.01 80.36
73.63
80.26
62.86
82.27
0
10
20
30
40
50
60
70
80
90
Logistic
Regression
KNN Decision
Tree
Random
Forest
Naive
Bayes
SVM
Chart Title
Logistic Regression KNN Decision Tree
Random Forest Naive Bayes SVM
8. Comparative
Accuracy
Analysis of
Classifiers
Dataset :
Breast_Tumor_Classification.csv
Target Column : diagnosis
Split Amount: 0.3
Epoch Count: 100
Optimizer : adam
8
97.36 96.48
92.612
95.96
93.15
97.88
0
20
40
60
80
100
120
Logistic
Regression
KNN Decision
Tree
Random
Forest
Naive
Bayes
SVM
Chart Title
Logistic Regression KNN Decision Tree
Random Forest Naive Bayes SVM
9. Auto-Visualization of
Feature Importance and Data details
The API is proved to analyze and visualize the feature-
Importances much more efficiently.
It is the Feature Importance Report of the titanic
Datset.
9
10. Future Applications
of the API
10
Financial Analysis
and Bank Churn
Model
Business
Modeling
Health Care
Applications
Weather
Prediction
11. CONCLUSION
Machine learning has become one of the main engines of the current era. The
production pipeline of a machine learning models passe through different phases
and stages that require wide knowledge of several available tools, and algorithms.
However, as the scale of data produced daily is increasing continuously at an
exponential scale, it has become essential to automate this process. In this
project, I have covered comprehensively the state-of-the-art research effort in the
domain of Driverless ML frameworks. I have also highlighted research directions
and open challenges that need to be addressed in order to achieve the vision
and goals of the Driverless ML process. I have already built the working API and
currently targeting to integrate Convolution Neural Network to order to automate
disease recognition using Image processing.
