Amazon CodeGuru is a machine learning service for automated code reviews and application performance recommendations. It helps you find the most expensive lines of code that hurt application performance and keep you up all night troubleshooting, then gives you specific recommendations to fix or improve your code. CodeGuru is powered by machine learning, best practices, and hard-learned lessons across millions of code reviews and thousands of applications profiled on open source projects and internally at Amazon. With CodeGuru, you can find and fix code issues such as resource leaks, potential concurrency race conditions, and wasted CPU cycles. And with low, on-demand pricing, it is inexpensive enough to use for every code review and application you run. CodeGuru supports Java applications today, with support for more languages coming soon. CodeGuru helps you catch problems faster and earlier, so you can build and run better software.

AI is emerging as the most important technology in a new wave of digital innovation that is transforming industries around the world. Businesses in Europe are at the forefront of some of the latest advancements in the field, and European universities are home to the greatest concentration of AI researchers in the world. Every week, new case studies emerge showing the potential opportunities that can arise from greater use of the technology. To fully realize its vision for AI, Europe needs an influx of resources and talent, plus some important policy changes. Join the Center for Data Innovation to discuss why European success in AI is important, how the EU compares to other world leaders today, and what steps European policymakers should take to be more competitive in AI.

There’s no doubt about it, probability and statistics is an enormous field, encompassing topics from the familiar (like the average) to the complex (regression analysis, correlation coefficients and hypothesis testing to name but a few). If you want to be a great data scientist, you have to know some basic statistics. The following picture shows which statistics topics you must know if you’re going to excel in data science.

As deep neural networks are applied to an increasingly diverse set of domains, transfer learning has emerged as a highly popular technique in developing deep learning models. In transfer learning, the neural network is trained in two stages: 1) pretraining, where the network is generally trained on a large-scale benchmark dataset representing a wide diversity of labels/categories (e.g., ImageNet); and 2) fine-tuning, where the pretrained network is further trained on the specific target task of interest, which may have fewer labeled examples than the pretraining dataset. The pretraining step helps the network learn general features that can be reused on the target task.

Companies all over the world across a wide variety of industries have been going through what people are calling a digital transformation. That is, businesses are taking traditional business processes such as hiring, marketing, pricing, and strategy, and using digital technologies to make them 10 times better. Data Science has become an integral part of those transformations. With Data Science, organizations no longer have to make their important decisions based on hunches, best-guesses, or small surveys. Instead, they’re analyzing large amounts of real data to base their decisions on real, data-driven facts. That’s really what Data Science is all about – creating value through data. This trend of integrating data into the core business processes has grown significantly, with an increase in interest by over four times in the past 5 years according to Google Search Trends. Data is giving companies a sharp advantage over their competitors. With more data and better Data Scientists to use it, companies can acquire information about the market that their competitors might not even know existed. It’s become a game of Data or perish.

While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning.

Deep Learning (DL) models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another – image classification, object detection, object tracking, pose recognition, video analytics, synthetic picture generation – just to name a few. However, they are like anything but classical Machine Learning (ML) algorithms/techniques. DL models use millions of parameters and create extremely complex and highly nonlinear internal representations of the images or datasets that are fed to these models. Whereas for the classical ML, domain experts and data scientists often have to write hand-crafted algorithms to extract and represent high-dimensional features from the raw data, deep learning models, on the other hand, automatically extracts and work on these complex features.

We venture to suggest a curriculum roadmap after receiving multiple requests for one from academic partners. As a group, we have spent the vast majority of our time in industry, although many of us have had spent time in one academic capacity or another. What follows is a set of broad recommendations, and it will inevitably require a lot of adjustments in each implementation. Given that caveat, here are our curriculum recommendations.

We all understand that more data means better AI. That sounds great! But, with the recent blast of information, we often end in a problem of too much data! We need all that data. But it turns out to be too much for our processing. Hence we need to look into ways of streamlining the available data so that it can be compressed without losing value. Dimensionality reduction is an important technique that achieves this end.

Three weeks into my journey to become a data scientist and I’ve officially been baptized… by fire, that is! I chose to attend Flatiron’s Data Science 15-week bootcamp to transition out of finance. So far, the program has exceeded expectations (and my expectations were high). While the curriculum is rigorous and fast-paced, it’s well constructed, the instructors are dedicated to helping students learn, and my cohort is amazing – everyone is friendly, helpful, smart, and undoubtedly will go on to accomplish great things. This series of blog posts is dedicated to them… Here’s to the future data scientists!

Process capability analysis represents a significant component of the Measure phase from the DMAIC (Define, Measure, Analysis, Improve, Control) cycle during a Six Sigma project. This analysis measures how a process performance fits the customer’s requirements, which are translated into specification limits for the interesting characteristics of the product to be manufactured or produced. The results from this analysis may help industrial engineers identify variation within a process and develop further action plans that lead to better yield, lower variation and less number of defects.

Understanding Various Terminologies & Roles in Artificial Intelligence Projects. Artificial Intelligence (AI) is a complex and evolving field. The first challenge an AI aspirant faces is understanding the landscape and how he could navigate through it. Consider this, if you are travelling to a new city, and if you don’t have the map, you will have trouble to navigate the city and you will need to ask a lot of random people during your travel without knowing how much they know about the place. Similarly, all the newcomers to AI have this trouble, and there are two ways to deal with this, arrange the map (or a guide) or travel yourself and learn with experience.