In this blog post, I will examine the performance of machine learning functions in AWS Lambda compared to Nuweba. In addition, I will demonstrate how to overcome the difficulties of deploying a simple Inception model to AWS Lambda to classify images using TensorFlow. Imagine you want to create a simple API endpoint that receives an image (uploaded or by URL) and outputs the detected class to the client using a trained AI model. Sounds pretty useful, right?

Machine learning algorithms are becoming more and more integral to many decision making processes in fields ranging from medicine and finance. While augmenting traditional intelligence with insights derived from machine learning algorithms is beneficial, it also introduces a host of questions. Can we be confident that machine learning systems will produce accurate decisions when deployed in critical settings? How easy is it to interpret machine learning models?

At .conf19, we released the fifth major version of the Splunk Machine Learning Toolkit. This release was all about improving and enhancing toolkits' abilities to provide insights into your data, including a brand new outlier detection assistant, an update to our Machine Learning examples showcase page, an upgrade from Python 2.x to Python 3.x and a new System Identification algorithm. Outlier detection is by far the most popular use case in the industry.

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, sticking with October’s cybersecurity awareness theme, we’ll take an in-depth look at both the good and the bad of artificial intelligence (AI) and machine learning (ML) in cybersecurity.

Despite a rocky year in the global economy, global venture deal volume grew by over 9.3 percent in the third quarter of 2019, up nearly 9.9 percent from Q3 2018, according to Crunchbase. The year 2018 was a banner year for dollar volume: startups raised $130.9 billion, which surpassed the epic year of 2000, according to Pitchbook.

As people who know me are aware, I am NOT a fan of the term Artificial Intelligence (AI). So I thought I’d kick off my very first Splunk blog with that exact phrase. Why would I betray my own beliefs so readily? Well mostly because Splunk were privileged to be invited along to the 2019 Digital Summit in London last month to speak about data and public trust where AI was a big topic on the day.

By Des Nnochiri Robotic process automation, or RPA, promises to increase efficiency and improve work rates at reduced cost to the enterprise. In this article, we’ve assembled eight of the top RPA tools currently on the market. Of course, there are considerations to bear in mind before implementing this emerging technology.

That’s the potential grandeur of applying AI in different fields and industries of the world today! The UK government seems to have made the right move and at an opportune time too. AI is the intelligence exhibited by machines. These machines have competence in reasoning, learning, language processing, planning, and perception to help improve decision making of businesses. Basically, AI’s data crunching power will outsmart the smartest technologies of today.

Because of the hefty amount of data that are there without any practical use. Think of data as the collection of our consciousness. Similar to how consciousness helps us make clear decisions in our day-to-day life, ultimately the data-backed decisions will help us make things right. With data comes wisdom, the epiphany about things we were missing out on, these many years.

Everyone knows that their apps aren’t running as well as they could be. They know that – with the right tweaks to container and VM resource settings – they could be costing less, and performing better. There are various reasons why attempts at performance tuning often fall short. Optimization is difficult to fold into a release cycle; in the roadmap, it tends to get crowded out by new features; engineers don’t find it very exciting.