Talend

Discover how developers can implement machine learning solutions, where to apply to machine learning solutions, and which machine learning algorithm is right for your use case.

Learn how to use masking, shuffling, and encryption to meet the latest data privacy regulations and prevent unauthorized access to sensitive data.

Join data integration expert Darren Brunt for a unique perspective on the benefits and implementation of machine learning.

Explore what machine learning is, and why it's important, how machine learning is different from traditional programming, and what it takes to enable machines to learn.

Process Big Data with Spark on Microsoft Azure HDInsight

Machine learning helps pinpoint errors in large datasets for cleansing before entering the analytics pipeline. This on-demand webinar shows you how to set it up.

Big data brings tremendous opportunity to better target customers and improve operations. Yet, data-driven insights are only as good and trusted as the data going into them.

Find out how you can build data quality into your structured, semi-structured, or unstructured data on Microsoft Azure Data Lake Store and HDInsight using Talend’s native support for Spark machine learning algorithms.

Watch Microsoft and Talend to see how to:

- Process data faster using Talend’s native support for Spark On Azure HDInsight
- Quickly import bulk data into Azure Data Lake Store
- Deploy Spark machine learning to match and dedupe records at scale
Enable best practices for data quality using Talend Data Stewardship

In the last few years, Machine Learning has quickly gone from a niche subject to one with significant relevance to many companies and organizations. Across industries ranging from pharmaceuticals and healthcare, to retail and financial services, Machine Learning has become more widely used for solving new business requirements. But just what is Machine Learning and how does it work? Just how do you teach a machine to learn?

Watch the Webinar on Demand and get an overview of the following methods:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning

This session is an Intermediate level talk. It is geared towards Architects, Data Scientists, Developers, Software Engineers and anyone with an interest in Data Matching.

With ever increasing volumes and varieties of data forming the basis of our analysis universe, Machine Learning promises to help automate and condense cycle time to actionable insight. But how can you overcome the stubborn complexities of adoption associated with this subset of Data Science?

In this on-demand webinar see how Talend helps its data-driven customers embrace and exploit Machine Learning capabilities faster and more efficiently than traditional, coding-based approaches.

In this on-demand webinar you will learn about:

- The challenges of generating insight from data
- Principles of machine learning
- How to train, test and operationalise Machine Learning solutions using Talend
- How other Talend customers are exploiting Big Data solutions

In this on-demand webinar, Google and Talend experts demonstrate how to implement machine learning algorithms into analytics pipelines, and extract sentiment analysis to achieve a new level of insight and opportunity.

A strong data governance program ensures that you have the policies, standards, and controls in place to protect data effectively and access it for decision making. Data governance may become one of the most important functions of your data integration architecture when it comes to data agility.

Talend and MapR break down the steps for turning a data lake into a data hub with appropriate data management and governance activities: from capturing and reconciling personal data to providing for consent management, data anomyzation, and the rights of the data subject.

GDPR has major implications for the IT landscape of every business. Companies are shifting away from data silos and closing down shadow IT to create a governed personal data hub and data services.

In this webinar we demonstrate how to achieve the 5 pillars of data management for GDPR compliance with a practical example: matching 3rd party consent data to existing records with appropriate data governance.

See how easily you can put the pieces together:

Data capture and integration into a data lake
Data cleansing, loading and matching
Self-service curation and certification
Data classification and lineage using metadata manager