- Evaluating Ray: Distributed Python for Massive Scalability - Mar 25, 2020.
If your team has started using Ray and you’re wondering what it is, this post is for you. If you’re wondering if Ray should be part of your technical strategy for Python-based applications, especially ML and AI, this post is for you.
- Scaling Your Data Strategy - Mar 17, 2020.
This article presents a particular vision for a cohesive data strategy for addressing large-scale problems with data-driven solutions, based on prior professional experiences.
- Uber Unveils a New Service for Backtesting Machine Learning Models at Scale - Mar 2, 2020.
The transportation giant built a new service and architecture for backtesting forecasting models.
- Large Scale Adversarial Representation Learning - Feb 7, 2020.
GANs can be used for unsupervised learning where a generator maps latent samples to generate data, but this framework does not include an inverse mapping from data to latent representation. BiGAN adds an encoder E to the standard generator-discriminator GAN architecture — the encoder takes input data x and outputs a latent representation z of the input.
- Uber Has Been Quietly Assembling One of the Most Impressive Open Source Deep Learning Stacks in the Market - Jan 27, 2020.
Many of the technologies used by Uber teams have been open sourced and received accolades from the machine learning community. Let’s look at some of my favorites.
- Accuracy vs Speed – what Data Scientists can learn from Search - Jan 2, 2020.
Delivering accurate insights is the core function of any data scientist. Navigating the development road toward this goal can sometimes be tricky, especially when cross-collaboration is required, and these lessons learned from building a search application will help you negotiate the demands between accuracy and speed.
- Scalable graph machine learning: a mountain we can climb? - Dec 10, 2019.
Graph machine learning is a developing area of research that brings many complexities. One challenge that both fascinates and infuriates those working with graph algorithms is — scalability. We take a close look at scalability for graph machine learning methods covering what it is, what makes it difficult, and an example of a method that tackles it head-on.
- Monitoring Models at Scale - Nov 7, 2019.
Catch this Domino webinar on monitoring models at scale, Dec 11 @ 10am PT, covering detecting changes in pattern of real-world data your models are seeing in production, tracking how model accuracy and other quality metrics are changing over time, and getting alerted when health checks fail so that resolution workflows can be triggered.
- Scaling a Massive State-of-the-art Deep Learning Model in Production - Jul 15, 2019.
A new NLP text writing app based on OpenAI's GPT-2 aims to write with you -- whenever you ask. Find out how the developers setup and deployed their model into production from an engineer working on the team.
- KDnuggets™ News 19:n23, Jun 19: Useful Stats for Data Scientists; Python, TensorFlow & R Winners in Latest Job Report - Jun 19, 2019.
This week on KDnuggets: 5 Useful Statistics Data Scientists Need to Know; Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS; How to Learn Python for Data Science the Right Way; The Machine Learning Puzzle, Explained; Scalable Python Code with Pandas UDFs; and much more!
- Why organizations fail in scaling AI and Machine Learning - May 29, 2019.
We explain why AI needs to understand business processes and how the business processes need to be able to change to bring insight from AI into the process.
- Most impactful AI trends of 2018: The rise of ML Engineering - Mar 1, 2019.
As both research and applied teams are doubling down on their engineering and infrastructure needs, the nascent field of ML Engineering will build upon 2018’s foundation and truly blossom in 2019.
- How to Engineer Your Way Out of Slow Models - Nov 27, 2018.
We describe how we handle performance issues with our deep learning models, including how to find subgraphs that take a lot of calculation time and how to extract these into a caching mechanism.
- One-Click Machine Learning Deployments with Anaconda Enterprise - Aug 20, 2018.
With Anaconda Enterprise, your organization can develop, govern, and automate machine learning pipelines, while scaling with ease.
- Deep Learning and Challenges of Scale Webinar - Jul 9, 2018.
Join Nvidia for an on-demand webinar to learn how to tackle the challenges of scaling and building complex deep learning systems.
- ebook: A Guide to Data Science at Scale - Jun 12, 2018.
Read our eBook to learn how easy it is to build and scale ML models with a unified analytics platform, how to collaborate across data teams to uncover insights faster, and more. Free download.
- Deep learning scaling is predictable, empirically - May 10, 2018.
This study starts with a simple question: “how can we improve the state of the art in deep learning?”
- Best Practices for Scaling Data Science Across the Organization - May 7, 2018.
Join Forrester and Anaconda for a webinar on Thursday, May 17, at 2:00 PM CT, to learn best practices for scaling data science across your entire organization. Learn how to tackle five key challenges facing organizations today!
- To SQL or not To SQL: that is the question! - May 7, 2018.
This article looks at the emergence of the NoSQL movement and compares it to a traditional relational database.
- Unlock the Next Era of Analytics – AI and Machine Learning at Scale - Apr 12, 2018.
Join us on Apr 19 for an interactive virtual event to hear from a panel of analytic experts as they dispel the myths and dive into the nitty-gritty of how AI and machine learning will impact analytic teams.
- Operational Best Practices for Enterprise Data Science - Jan 24, 2018.
Join Team Anaconda for a live webinar, Jan 30, 2pm CT, as we tackle the four main concerns we hear from our customers and show you best practices for managing enterprise data science: scalability, security, integration, and governance.
- Analytic Creation to Production: Bridging The Chasm, Webinar, Dec 7 - Dec 1, 2017.
Understand best practices for optimizing the handoff from analytic team to IT across your business as a core competency, how to create scalable peak model performance, and more.
- The Guts and Glory of Data Science - Nov 6, 2017.
Are you a data science leader, or aspiring to be one? Learn how industry leaders manage their data science initiatives as core capabilities that drive their company’s strategic objectives.
- Big Data Architecture: A Complete and Detailed Overview - Sep 19, 2017.
Data scientists may not be as educated or experienced in computer science, programming concepts, devops, site reliability engineering, non-functional requirements, software solution infrastructure, or general software architecture as compared to well-trained or experienced software architects and engineers.
- The Internet of Things in the Cloud - May 11, 2017.
Cloud computing is the next evolutionary step in Internet-based computing, which provides the means for delivering ICT resources as a service. Internet-of-Things can benefit from the scalability, performance and pay-as-you-go nature of cloud computing infrastructures.
- RCloud – DevOps for Data Science - Nov 28, 2016.
After almost two decades of software development, term – DevOps was coined and officially given importance to collaboration between development and deployment of software systems. In this early stage of Data Science field, use of standardized and empirical practises like DevOps will definitely speed up its evolution.