- Building Massively Scalable Machine Learning Pipelines with Microsoft Synapse ML - Nov 30, 2021.
The new platform provides a single API to abstract dozens of ML frameworks and databases.
Machine Learning, Microsoft, Pipeline, Scalability
Do You Read Excel Files with Python? There is a 1000x Faster Way - Sep 1, 2021.
In this article, I’ll show you five ways to load data in Python. Achieving a speedup of 3 orders of magnitude.
Excel, Microsoft, Pandas, Python, Scalability
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 5 - Jul 16, 2021.
Training efficient deep learning models with any software tool is nothing without an infrastructure of robust and performant compute power. Here, current software and hardware ecosystems are reviewed that you might consider in your development when the highest performance possible is needed.
Deep Learning, Efficiency, Google, Hardware, Machine Learning, NVIDIA, PyTorch, Scalability, TensorFlow
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 4 - Jul 9, 2021.
With the right software, hardware, and techniques at your fingertips, your capability to effectively develop high-performing models now hinges on leveraging automation to expedite the experimental process and building with the most efficient model architectures for your data.
Attention, Convolution, Deep Learning, Efficiency, Hyperparameter, Machine Learning, Scalability
Pandas not enough? Here are a few good alternatives to processing larger and faster data in Python - Jul 8, 2021.
While the Pandas library remains a crucial workhorse in data processing and management for data science, some limitations exist that can impact efficiencies, especially with very large data sets. Here, a few interesting alternatives to Pandas are introduced to improve your large data handling performance.
Dask, Modin, Pandas, Python, Scalability
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 3 - Jul 2, 2021.
Now that you are ready to efficiently build advanced deep learning models with the right software and hardware tools, the techniques involved in implementing such efforts must be explored to improve model quality and obtain the performance that your organization desires.
Compression, Deep Learning, Efficiency, Machine Learning, Scalability
- High-Performance Deep Learning: How to train smaller, faster, and better models – Part 2 - Jun 25, 2021.
As your organization begins to consider building advanced deep learning models with efficiency in mind to improve the power delivered through your solutions, the software and hardware tools required for these implementations are foundational to achieving high-performance.
Deep Learning, Efficiency, Machine Learning, Scalability
Vaex: Pandas but 1000x faster - May 17, 2021.
If you are working with big data, especially on your local machine, then learning the basics of Vaex, a Python library that enables the fast processing of large datasets, will provide you with a productive alternative to Pandas.
Big Data, Data Preprocessing, Pandas, Scalability, Vaex
- How to get started managing data quality with SQL and scale - May 4, 2021.
Silent data quality issues are the biggest problem facing data teams today, who are flying blind with no systems or processes in place to monitor and detect bad data before it has a downstream impact.
Data Preparation, Data Quality, Scalability, SQL
- Computer Vision at Scale With Dask And PyTorch - Nov 23, 2020.
A tutorial on conducting image classification inference using the Resnet50 deep learning model at scale with using GPU clusters on Saturn Cloud. The results were: 40x faster computer vision that made a 3+ hour PyTorch model run in just 5 minutes.
Computer Vision, Dask, Python, PyTorch, Scalability
- Compute Goes Brrr: Revisiting Sutton’s Bitter Lesson for AI - Nov 19, 2020.
"It's just about having more compute." Wait, is that really all there is to AI? As Richard Sutton's 'bitter lesson' sinks in for more AI researchers, a debate has stirred that considers a potentially more subtle relationship between advancements in AI based on ever-more-clever algorithms and massively scaled computational power.
AI, AlphaGo, Machine Learning, OpenAI, Richard Sutton, Scalability, Trends
- Microsoft and Google Open Sourced These Frameworks Based on Their Work Scaling Deep Learning Training - Nov 2, 2020.
Google and Microsoft have recently released new frameworks for distributed deep learning training.
Deep Learning, Google, Microsoft, Open Source, Scalability, Training
- Deploying Secure and Scalable Streamlit Apps on AWS with Docker Swarm, Traefik and Keycloak - Oct 23, 2020.
If you are a data scientist who just wants to get the work done but doesn’t necessarily want to go down the DevOps rabbit hole, this tutorial offers a relatively straightforward deployment solution leveraging Docker Swarm and Traefik, with an option of adding user authentication with Keycloak.
AWS, Deployment, Docker, Scalability, Security, Streamlit
- 5 Challenges to Scaling Machine Learning Models - Oct 7, 2020.
ML models are hard to be translated into active business gains. In order to understand the common pitfalls in productionizing ML models, let’s dive into the top 5 challenges that organizations face.
Deployment, Machine Learning, Scalability
- LinkedIn’s Pro-ML Architecture Summarizes Best Practices for Building Machine Learning at Scale - Sep 23, 2020.
The reference architecture is powering mission critical machine learning workflows within LinkedIn.
Best Practices, LinkedIn, Machine Learning, Scalability
- Here’s what you need to look for in a model server to build ML-powered services - Sep 15, 2020.
More applications are being infused with machine learning while MLOps processes and best practices are becoming well established. Critical to these software and systems are the servers that run the models, which should feature key capabilities to drive successful enterprise-scale productionizing of machine learning.
Deployment, Life Cycle, MLOps, Model Drift, Model Performance, Monitoring, Production, Scalability
- Showcasing the Benefits of Software Optimizations for AI Workloads on Intel® Xeon® Scalable Platforms - Sep 1, 2020.
The focus of this blog is to bring to light that continued software optimizations can boost performance not only for the latest platforms, but also for the current install base from prior generations. This means customers can continue to extract value from their current platform investments.
AI, Intel, Optimization, Scalability
- Scaling Computer Vision Models with Dataflow - Jul 31, 2020.
Scaling Machine Learning models is hard and expensive. We will shortly introduce the Google Cloud service Dataflow, and how it can be used to run predictions on millions of images in a serverless way.
Computer Vision, Dataflow, Google, Python, Scalability
- Is depth useful for self-attention? - Jul 27, 2020.
Learn about recent research that is the first to explain a surprising phenomenon where in BERT/Transformer-like architectures, deepening the network does not seem to be better than widening (or, increasing the representation dimension). This empirical observation is in contrast to a fundamental premise in deep learning.
Attention, BERT, Deep Learning, Research, Scalability, Transformer
The Bitter Lesson of Machine Learning - Jul 15, 2020.
Since that renowned conference at Dartmouth College in 1956, AI research has experienced many crests and troughs of progress through the years. From the many lessons learned during this time, some have needed to be re-learned -- repeatedly -- and the most important of which has also been the most difficult to accept by many researchers.
AI, AlphaGo, Chess, Machine Learning, Reinforcement Learning, Richard Sutton, Scalability, Trends
- Some Things Uber Learned from Running Machine Learning at Scale - Jul 7, 2020.
Uber machine learning runtime Michelangelo has been in operation for a few years. What has the Uber team learned?
Machine Learning, Scalability, Uber
- 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.
Domino, Python, Scalability
- 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.
Data Science, Scalability, Strategy
- 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.
Machine Learning, Scalability, Uber
- 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.
Deep Learning, Interpretability, NLP, Probability, Programming, Scalability, Uber
- 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.
Accuracy, Advice, Lucidworks, Scalability, Search, Search Engine
- 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.
Deep Learning, Graph Analytics, Graph Databases, Machine Learning, Scalability
- 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.
Deep Learning, Deployment, NLP, OpenAI, Scalability, Transformer
- 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.
AI, Deployment, Failure, Machine Learning, Scalability
- 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.
Deep Learning, Scalability
- 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?”
Deep Learning, Machine Learning, Scalability
- 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.
Analytics, Big Data, Big Data Architecture, Cloud, Cloud Computing, Scalability, Software, Software Engineering
- 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.
Cloud, Cloud Computing, Internet of Things, IoT, Scalability
