Public and private organizations have come out with their own set of AI principles, focusing on AI-related risks from their perspective. However, it’s imperative d=to have a global consensus on Responsible AI – based on data governance, transparency and accountability – on how to utilize and benefit from AI in a way that is both consistent and ethical.
ETL and related techniques remain a powerful and foundational tool in the data industry. We explain what ETL is and how ETL and ELT processes have evolved over the years, with a close eye toward how third-generation ETL tools are about to disrupt standard data processing practices.
There is ample opportunity for data scientists in the financial services sector. The career experience can be very different, however, from similar roles at pure technology organizations. So, it's best to first consider if this industry is right for your interests, preferences for how you work, and long-term goals.
The field of data science is growing into one that features a variety of job titles This guide reviews different positions available for you to consider if you have a data science background.
The ML model management and the delivery of highly performing model is as important as the initial build of the model by choosing right dataset. The concepts around model retraining, model versioning, model deployment and model monitoring are the basis for machine learning operations (MLOps) that helps the data science teams deliver highly performing models.
Octoparse makes it easy to collect data from websites and automate workflows on the web. Zapier is an online platform that allows you to automate workflows by connecting the apps and services you use. Zapier connection, the new feature in Octoparse, makes it possible to connect the product with apps including Google Drive, Google Sheets, Dropbox, Trello, Slack, and load more apps in a second with NO CODE.
As a library designed for production research, PyTorch Lightning streamlines hardware support and distributed training as well, and we’ll show how easy it is to move training to a GPU toward the end.
How many times have you taken yet another online course on machine learning or read yet another paper on a new emerging topic, to be up-to-date in this crazy fast-paced AI/ML world -- only to keep feeling like an ML engineer impostor? These three personal tips can help you overcome the classic (and common) impostor syndrome behind every emerging ML engineer who wants to be better at what you do.
What we would like to do here is introduce four very basic and very general steps in data preparation for machine learning algorithms. We will describe how and why to apply such transformations within a specific example.
Also: Data Scientist vs Data Engineer Salary; The 20 Python Packages You Need For Machine Learning and Data Science; Exclusive: OpenAI summarizes KDnuggets; Real Time Image Segmentation Using 5 Lines of Code
365 Data Science, an online educational platform providing beginner-to-advanced courses for data science and business analytics professionals, will unlock the entire library of courses, hands-on exercises, certificate exams, and resume builder for a full 30-day period from Oct. 18 to Nov. 18.
What happens to a life so dependent on machines, when that particular machine breaks down? This is precisely why there’s a dire need for predictive maintenance with machine learning.
Data science and data privacy are deeply interwoven, and must be carefully considered by practitioners. In comparing the Safe Harbour and Expert Determination data obfuscation approaches, Safe Harbour has been very popular among data engineers but has fundamental limitations, where Expert Determination offers important advantages.
OpenAI has recently done amazing work summarizing full-length books. We have asked OpenAI to summarize two recent KDnuggets posts, and the results have a very human-like quality. Only the last line betrays the inhuman intelligence at work.
Data transformation is the biggest bottleneck in the analytics workflow. The modern approach to data pipelines is ELT, or extract, transform, and load, with data transformation performed in your Snowflake data warehouse. A new breed of “no-/low-code” data transformation tools, such as Datameer, are emerging to allow the wider analytics community to transform data on their own, eliminating analytics bottlenecks.
Autoencoders and their variants are interesting and powerful artificial neural networks used in unsupervised learning scenarios. Learn how autoencoders perform in their different approaches and how to implement with Keras on the instructional data set of the MNIST digits.
How to find the best-matching statistical distributions for your data points — in an automated and easy way. And, then how to extend the utility further.
Tech Tree Root is excited to introduce you to our DATAnalyze 2021 sponsors Microsoft, WorldData.AI, and HBCU Connect! Our online analytics hackathon is offering up to $125,000 USD in prizes!
At ODSC West 2021 this November 16th-18th, we’ll have 80+ training sessions and workshops on essential tools and languages led by some of the best and brightest minds in data science and AI.
Choosing what to include in your data science portfolio during the job search is the most important part of the process. Each project should be well-structured so that a hiring manager can assess your skills quickly. To help you get started, we highlight a few data science project ideas that you should consider for your portfolio.
Over the past few years, the data engineering market has seen tremendous growth. The acceleration of the data engineering market prompted us to create a new report specifically for data engineering professionals. You can download both the 2021 Data Engineering and 2021 Data Science & Analytics salary reports from our website for free.
While there may be plenty of room for advancement even when busy, how to achieve that isn’t always clear. In that spirit, here are five ways you can impress your company leadership.
While the field of data science continues to evolve with exciting new progress in analytical approaches and machine learning, there remain a core set of skills that are foundational for all general practitioners and specialists, especially those who want to be employable with full-stack capabilities.
Also: How to Ace Data Science Interview by Working on Portfolio Projects; AutoML: An Introduction Using Auto-Sklearn and Auto-PyTorch; How to Build Strong Data Science Portfolio as a Beginner; 8 Must-Have Git Commands for Data Scientists
Ontotext is thrilled to invite you to the Ontotext & partners virtual Knowledge Graph Forum, Oct 26 & 27, 2021. This event is shaped by Ontotext’s vision that knowledge graphs serve as a hub for data, metadata and content. 35+ speakers from around the globe will share their experiences through real-life cases and platforms demonstrations. Save your spot now.
PixelLib Library is a library created to allow easy integration of object segmentation in images and videos using few lines of python code. PixelLib now provides support for PyTorch backend to perform faster, more accurate segmentation and extraction of objects in images and videos using PointRend segmentation architecture.
If you are new to the Data Science industry or a well-versed veteran in all things data and analytics, there are always key pitfalls that each of us can easily slide into if we are not careful. These behaviors not only make us appear like novices, but they can risk our position as a trustworthy, likable data partner with stakeholder.
Over the past couple years, we've seen 4 common patterns of machine learning in production: pipeline, ensemble, business logic, and online learning. In the ML serving space, implementing these patterns typically involves a tradeoff between ease of development and production readiness. Ray Serve was built to support these patterns by being both easy to develop and production ready.
The September blogs that earned KDnuggets Rewards include: Do You Read Excel Files with Python? There is a 1000x Faster Way; Data Scientists Without Data Engineering Skills Will Face the Harsh Truth; Path to Full Stack Data Science; Nine Tools I Wish I Mastered Before My PhD in Machine Learning
Build the essential technical, analytical, and leadership skills needed for careers in today's data-driven world in Northwestern’s Master of Science in Data Science program. Apply now.
Deep Learning radically improved Machine Learning as a whole. The Data-Centric revolution is about to do the same. In this post, we’ll take a look at the pitfalls of mainstream Computer Vision (CV) and discuss why Synthetic Computer Vision (SCV) is the future.
As larger deep neural networks are trained on the latest and fastest chip technologies, an important challenge remains that bottlenecks performance -- and it is not compute power. You can try to calculate a DNN as fast as possible, but there is data -- and it has to move. Data pipelines on the chip are expensive and new solutions must be developed to advance capabilities.
Register now for this webinar, Oct 28, to learn how using third-party data enhances applications to better prioritize your target customer - helping you build a more customer-centric business.
Do you do Python? Do you do data science and machine learning? Then, you need to do these crucial Python libraries that enable nearly all you will want to do.
Let us examine how clusters with different properties are produced by different clustering algorithms. In particular, we give an overview of three clustering methods: k-Means clustering, hierarchical clustering, and DBSCAN.
Recruiters of Data Science professionals around the world focus on portfolio projects rather than resumes and LinkedIn profiles. So, learning early how to contribute and share your work on GitHub, Deepnote, and Kaggle can help you perform your best during data science interviews.
Also: Data Scientists Without Data Engineering Skills Will Face the Harsh Truth; Nine Tools I Wish I Mastered Before My PhD in ML; A Data Science Portfolio That Will Land You The Job
Faster, trusted decisions are in the cloud. See how you can use the flexibility, scalability and agility of modern technologies to advance your organization’s goals. Read our blog with 3-part video demo.
A simple and intuitive way to create synthetic (artificial) time-series data with customized anomalies — particularly suited to industrial applications.
Data-driven decisions, actionable insights, business impact—you've seen these buzzwords in data science jobs descriptions. But, just focusing on these terms doesn't automatically lead to the best results. Learn from this real-world scenario that followed data-driven indecisiveness, found misleading insights, and initially created a negative business impact.
PASS Data Community Summit 2021 is the year’s largest gathering of Microsoft data platform professionals. This FREE online conference (taking place November 8 – 12, 2021) features 200+ world-class speakers and sessions, and gives you the opportunity to connect, share, and learn with thousands of your peers from the global data platform community.
Also: Data science SQL interview questions from top tech firms; Here’s Why You Need Python Skills as a Machine Learning Engineer; 8 Must-Have Git Commands for Data Scientists; Introduction to PyTorch Lightning
AutoML is a broad category of techniques and tools for applying automated search to your automated search and learning to your learning. In addition to Auto-Sklearn, the Freiburg-Hannover AutoML group has also developed an Auto-PyTorch library. We’ll use both of these as our entry point into AutoML in the following simple tutorial.
The foundational idea of Artificial Intelligence is that it should demonstrate human-level intelligence. So, unless a model can perform as a human might do, its intended purpose is missed. Here, recent OpenAI research into full-length book summarization focuses on generating results that make sense to humans with state-of-the-art results that leverage scalable AI-enhanced human-in-the-loop feedback.
Software engineers seeking jobs at data companies face a new problem: choosing the right job out of all the options. Learn the 5 signs that signal an agile and innovative engineering culture.
Git is a must-have skill for data scientists. Maintaining your development work within a version control system is absolutely necessary to have a collaborative and productive working environment with your colleagues. This guide will quickly start you off in the right direction for contributing to an existing project at your organization.
Target leakage and data leakage represent challenging problems in machine learning. Be prepared to recognize and avoid these potentially messy problems.
Join RapidMiner live on LinkedIn, Oct 28, to learn how you can lead a digital transformation—not by starting from scratch, but by getting more from what you already have. We’ll walk through a series of real-world examples to demonstrate how your data, when paired with machine learning, can be used to make smarter process decisions.
Though we have SOTA algorithms for tokenization, it's always a good practice to understand the evolution trail and learning how have we reached here. Read this introduction to Byte Pair Encoding.
With more enterprises implementing machine learning to improve revenue and operations, properly operationalizing the ML lifecycle in a holistic way is crucial for data teams to make their projects efficient and effective.
If you want to learn how to apply Python programming skills in the context of AI applications, the UC San Diego Extension Machine Learning Engineering Bootcamp can help. Read on to find out more about how machine learning engineers use Python, and why the language dominates today’s machine learning landscape.
The magrittr package supplies the pipe operator (%>%), but it turns out that the package actually contains four pipe operators in total. Let's go into them a bit.
There are so many online resources for learning data science, and a great deal of it can be used at no cost. This collection of free courses hosted by Coursera will help you enhance your data science and machine learning skills, no matter your current level of expertise.
With so many Data Science specializations, where should you focus? The Pace University online Master of Science in Data Science features elective courses which allow you to focus on topics that suit your career path so that you can begin to develop a unique specialization.
It's the question so many are asking: will data analysts be replaced by AI? Read this well-reasoned and concise opinion by someone with insight into the matter.
As a data scientist, there is one thing you really need to understand and know how to handle: data. With SQL being a foundational technical approach for working with data, it should not be surprising that the top tech companies will ask about your SQL skills during an interview. Here, we cover the key concepts tested so you can best prepare for your next data science interview.
Also: How To Build A Database Using Python; Surpassing Trillion Parameters and GPT-3 with Switch Transformers – a path to AGI?; Nine Tools I Wish I Mastered Before My PhD in Machine Learning; 20 Machine Learning Projects That Will Get You Hired
This article reviews some common options for parallelizing Python code, including process-based parallelism, specialized libraries, ipython parallel, and Ray.
Ever larger models churning on increasingly faster machines suggest a potential path toward smarter AI, such as with the massive GPT-3 language model. However, new, more lean, approaches are being conceived and explored that may rival these super-models, which could lead to a future with more efficient implementations of advanced AI-driven systems.
When read_csv( ) reads e.g. “2021-03-04” and “2021-03-04 21:37:01.123” as mere “object” datatypes, often you can simply auto-convert them all at once to true datetime datatypes.
A Guide to 14 Different Data Science Jobs
Machine Learning Model Development and Model Operations: Principles and Practices
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