Apache Superset: Exploring big data canvas with future-ready custom data visualization tool
Data visualization can be the new storyboard for your business strategy and planning. Extracting business-relevant and contextual insights from raw business data and putting them into visually engaging dashboards and intuitive visual reports is what leading business intelligence (BI) and data visualization tools promise.
Data build tool (dbt) – Transforming your data like no other
Data plays a crucial role in providing insights and making decisions. According to Gartner, a whopping 72% of Data and Analytics leaders now have rigorous involvement with digital transformation initiatives, and cutting-edge data transformation will play a mission-critical role in these initiatives.
Transforming raw data into datasets using Airbyte with dbt
Raw data should be consolidated from multiple sources and transformed for better organization and insights. Transformed data is easier to consume for humans and machines alike. However, the data pipelines built using ETL processes have been known to present businesses with unique challenges.
In this blog, we will discuss some general challenges with Machine Learning models, why we need model monitoring and different ways of implementing the same. We will also discuss different aspects of Amazon SageMaker Model Monitor, the AWS Service for Fully Managed Automatic Monitoring of Machine Learning Models.
This is the first of a series of blogs on AI services provided by AWS. This blog discusses an NLP service that uses machine learning to unravel valuable insights from text – Amazon Comprehend, and in particular how Comprehend can be used for Named Entity Recognition.
The best business intelligence platform enables gaining insights from data simple and easy to convey to stakeholders. This is especially true now that modern businesses can collect data on virtually every element of their operations, from sales and marketing to workflows and productivity, hiring and HR, and overall performance and profitability.
Orchestrating Pipelines using MLOps Workload Orchestrator
In our previous blogs, we discussed SageMaker Pipelines, their different implementations  and the benefits  of using SageMaker Project Templates for workflow automation.
In this blog, we discuss how to provision different kinds of pipelines using a different AWS MLOps framework – the Workload Orchestrator.
Combining SageMaker Pipelines with SageMaker Projects
In this blog, we will see how pipelines can be combined with SageMaker projects and what benefits this approach provides. This blog is also centered around the deployment of the customer churn prediction model, as was the case before, but the implementation is done as part of projects in this case.
Leveraging Power BI for advanced analytics through rich data visualization and excellent dashboards
Business intelligence (BI) technologies are transforming how firms conduct business across the board, from sales to operations to finance and even executive management. Today many businesses have jumped on board and are reaping the benefits.
Generating ML Workflows using Amazon SageMaker Pipelines
MLOps is a set of practices that pertains to deploying and maintaining machine learning models in production. Not only does MLOps focus on increasing automation and improving the quality of production models but it also takes business and regulatory requirements into full consideration.
Building predictive models is a minor portion of a data scientist’s everyday work, and is well recognized in the data science community. In general 80 percent of the effort is spent analyzing the data, cleaning it, and performing feature engineering.
The connectivity requirements of today’s businesses are constantly changing. In addition to linking numerous mobile and IoT devices to their networks, organizations are rapidly shifting workloads and interactions online and into the cloud.
Picture a lake. Now, instead of water, fill this lake with data. The way the data flows into this lake is exactly how water would flow into any lake – from an external source. Typically, water flowing into a lake does not go through a filtration process. Using that analogy to our data lake we can draw a parallel and say that a data lake contains all kinds of data – structured and unstructured.
Loading data into Redshift using ETL jobs in AWS GLUE
In continuation of our previous blog on loading data in Redshift, in the current blog of this blog series, we will explore another popular approach to loading data into Redshift using ETL jobs in AWS Glue.
AWS Glue is a serverless data integration service that makes the entire process of data integration very easy by facilitating data preparation, analysis and finally extracting insights from it.
Successfully Funneling User Feedback into Problem Statements
Organizations today have a plethora of technologies to use. These technologies have helped them improve their services by a large extent. However, customer demands keep changing and when these demands aren’t met, you will hear about them in various ways. To meet these demands we write problem statements by identifying and cataloging services that need improvement.
Have you ever noticed how there are patterns to almost everything? Patterns bring order to chaos and structure to operations. When an enterprise tries to improve operations, it often does this by implementing new variables and adjusting existing strategies – by which they partake in Process Improvement.
The two technologies that everyone is talking about are Digital Transformation and Artificial Intelligence. It is worth noting that while businesses understand the value of digital transformation, somehow they missed out on how AI can help with this. Not any more.
Data-Driven Business Outcome – Making AI Available for All
Data is the new oil! This message rhymes well in any intelligent conversation among the business leaders. Executives are always excited about the prospect of using AI/ML and other new technologies. There is often a significant budget allocated for Big Data projects.
Building a Mask Detection App: Using Flutter and TensorFlow Lite
In recent times, due to the effects of the pandemic, a mask detecting app is in great need. Such an app can go a long way in helping to contain the spread of the disease. How so?
The pandemic has brought to light the necessary steps one can take to help prevent the spread of the virus. One small yet highly effective measure is wearing masks.
The BlueOcean editor is the simplest way to get started with creating Pipelines in Jenkins. It is also a great way for existing Jenkins users to start adopting Pipeline. BlueOcean allows users to create and edit Declarative Pipelines and add stages and tasks that can run at the same time. This blog will give you a detailed demonstration on how to get started with the BlueOcean editor.
An Executive’s Approach to Machine Learning – Part 1
Machine learning has been closely observed and implemented by the executive community across the globe. As the computing power of machines increases every minute, organizations can automate tons of applications. As a result, there is an increased expectation for machines to solve problems that were being exclusively solved by humans in the past.