AI/ML model life cycle automation and orchestration ensures reliable model operations and governance at scale. The path to production for each enterprise model can vary, along with different monitoring, continuous improvement, retirement needs. Organizations must now consider ModelOps as a fundamental capability towards operational excellence and immediate ROIs.
Several fundamental statistical concepts must be well appreciated by every data scientist -- from the enthusiast to the professional. Here, we provide code snippets in Python to increase understanding to bring you key tools that bring early insight into your data.
How would it feel to know that without a doubt, the data projects you were working on would create TRUE ROI for your organization? Stick around until the end to get my data science process lifecycle framework so that each data project you run is a smashing success.
These two terms are often used interchangeably. However, there are key distinctions between the functionality and features each provide, and the AI value and scalability at your organization depend on them.
Also: How to be a Data Scientist without a STEM degree; Data Scientists Without Data Engineering Skills Will Face the Harsh Truth; 20 Machine Learning Projects That Will Get You Hired; How to Find Weaknesses in your Machine Learning Models
Deep learning isn’t just for placing ads or identifying cats anymore. Instead, a slew of young startups have started to incorporate the advances in computer vision made possible through larger and larger neural networks to real working robots in the fields.
Start your journey toward mastering all aspects of the field of Data Science with this focused list of in-depth self-learning resources. Curated with the beginner in mind, these recommendations will help you learn efficiently, and can also offer existing professionals useful highlights for review or help filling in any gaps in skills.
Managing large-scale data science infrastructure presents significant challenges. With Saturn Cloud, managing GPU-based infrastructure is made easier, allowing practitioners and enterprises to focus on solving their business challenges.
It is very important to choose the right tool for data analysis. On the Kaggle forums, where international Data Science competitions are held, people often ask which tool is better. R and Python are at the top of the list. In this article we will tell you about an alternative stack of data analysis technologies, based on Scala.
Using histograms on real-time data is not possible in most of the popular data science libraries. In this article you will learn how dynamically compute and display a histogram within a Python notebook.
Deep Learning continues to evolve as one of the most powerful techniques in the AI toolbox. Many software packages exist today to support the development of models, and we highlight important options available with key qualities and differentiators to help you select the most appropriate for your needs.
Is Python good for learning finance and working in the financial world? The answer is not only a resounding YES, but yes for nine very good reasons. This article gets into the details behind why Python is a must-know programming language for anyone who wants to work in the financial sector.
Once these types of data have been cleaned, they do more than show organized data sets. They reveal unlimited possibilities, and AI analytics can reveal these possibilities faster and more efficiently than ever before.
If you want to break into the machine learning and data science job market, then you will need to demonstrate the proficiency of your skills, especially if you are self-taught through online courses and bootcamps. A project portfolio is a great way to practice your new craft and offer convincing evidence that an employee should hire you over the competition.
This year’s Big Data and AI Toronto conference and expo, held virtually Oct 13-14, will provide attendees with a 360° view of the industry through a unique 4-in-1 experience: Artificial intelligence, big data, cloud, and cybersecurity.
This article is the second article of an ongoing series on using Dask in practice. Each article in this series will be simple enough for beginners, but provide useful tips for real work. The first article in the series is about using LocalCluster.
Breaking into data science as a professional does require technical skills, a well-honed knack for problem-solving, and a willingness to swim in oceans of data. Maybe you are coming in as a career change or ready to take a new learning path in life--without having previously earned an advanced degree in a STEM field. Follow these tips to find your way into this high-demand and interesting field.
Many of us self-learn data science from the very beginning. While continuing to self-learn on demand is crucial, especially after you become a professional, there can be many pitfalls early on for learning the wrong way or missing out on key ideas that are important for the real-world application of data science.
To launch your data career, you’ll need both theoretical knowledge and applied skills. Bootcamp programs like Springboard’s Data Science Career Track and Data Engineering Career Track can help make you job-ready through hands-on, project-based learning and one-on-one mentorship. Wondering which data career path is right for you? Read on to find out.
After years in the making, this extensive and comprehensive ebook resource is now available and open for data scientists and ML engineers. Learn from and contribute to this tome of valuable information to support all your work in data science from engineering to strategy to management.
The DATAcated Expo, hosted by DATAcated founder Kate Strachnyi, is coming up on October 5, 2021 from 11am - 6pm ET. Live-streamed on LinkedIn, the free event provides the community with an opportunity to explore and discover innovative technologies in data science & analytics.
AutoML enables developers with limited ML expertise (and coding experience) to train high-quality models specific to their business needs. For this article, we will focus on AutoML systems which cater to everyday business and technology applications.
Follow this journey of personal experience -- with useful tips and learning resources -- to help you achieve the PCAP Certification, one of the most reputed Python Certifications, to validate your knowledge against International Standards.
The goal of data visualization is to communicate data or information clearly and effectively to readers. Here are 5 must try awesome Python libraries for helping you do so, with overviews and links to quick start guides for each.
Automate Microsoft Excel and Word Using Python; The Difference Between Data Scientists and ML Engineers; Most Common Data Science Interview Questions and Answers; 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks
A common moment when training a neural network is when you realize the model isn’t training quickly enough on a CPU and you need to switch to using a GPU. It turns out multi-GPU model training across multiple machines is pretty easy with Dask. This blog post is about my first experiment in using multiple GPUs with Dask and the results.
Although the role of the data scientist is still evolving, data remains at its core. Setting the right expectations for what you will do as a data scientist is important, and, to be sure, knowing the tools of data engineering will get yourself ready for the real world.
Also: How to Create Stunning Web Apps for your Data Science Projects; A Data Science Portfolio That Will Land You The Job; Top 18 Low-Code and No-Code Machine Learning Platforms; 8 Deep Learning Project Ideas for Beginners
Have you studied Deep Learning techniques, but never worked on a useful project? Here, we highlight eight deep learning project ideas for beginners that will help you sharpen your skills and boost your resume.
This article discusses the 7 key differences between data analysts and data scientists with an aim to help potential data analysts/scientists determine which is the right one for them. I touch on day-to-day tasks, skill requirements, typical career progression, and salary and career prospects for both.
Machine learning becomes more accessible to companies and individuals when there is less coding involved. Especially if you are just starting your path in ML, then check out these low-code and no-code platforms to help expedite your capabilities in learning and applying AI.
Check out this new book for data science beginners with many practical examples that covers statistics, R, graphing, and machine learning. As a source to learn the full breadth of data science foundations, "Learn Data Science with R" starts at the beginner level and gradually progresses into expert content.
Microsoft Researchers have developed FLAML (Fast Lightweight AutoML) which can now utilize Ray Tune for distributed hyperparameter tuning to scale up FLAML’s resource-efficient & easily parallelizable algorithms across a cluster.
Our intuition for most products, processes, and even some models might be that they either will get worse over time, or if they fail, they will experience an cascade of more failure. But, what if we could intentionally design systems and models to only get better, even as the world around them gets worse?
Download this eBook to learn how innovative teams are shifting their focus from data-driven business intelligence to accelerating insight-driven decision-making and now are turning to third-party datasets as a differentiator.
Becoming a machine learning engineer pro is your goal? Sure, online ML courses and Kaggle-style competitions are great resources to learn the basics. However, the daily job of a ML engineer requires an additional layer of skills that you won’t master through these approaches.
This innovative project brings together industry thought leaders from top tech companies such as Google, PwC, King, DNB, Piab, Scania, Telefonica, and more to discuss what the future holds for data and AI. Watch Future Says Series as industry experts discuss real-life examples how they are scaling AI successfully within their organizations.
Mobile app developers have a lot to gain by implementing AI & Machine Learning from the revolutionary changes that these disruptive technologies can offer. This is due to AI and ML's potential to strengthen mobile applications, providing for smoother user experiences capable of leveraging powerful features.