The Sequence Scope: End-to-End vs. Best-Of-Breed Machine Learning Platforms

Weekly newsletter that discusses impactful ML research papers, cool tech releases, the money in AI, and real-life implementations.

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The Sequence Scope is a summary of the most important published research papers, released technology and startup news in the AI ecosystem in the last week. This compendium is part of TheSequence newsletter. Data scientists, scholars, and developers from Microsoft Research, Intel Corporation, Linux Foundation AI, Google, Lockheed Martin, Cardiff University, Mellon College of Science, Warsaw University of Technology, Universitat Politècnica de València and other companies and universities are already subscribed to TheSequence.

📝 Editorial: End-to-End vs. Best-Of-Breed Machine Learning Platforms

What platform to choose for machine learning development is one of the questions that torments organizations embarking in the space. The rapid growth of the machine learning market has translated into an explosion of startups dedicated to machine learning development. At the same token, we have cloud platform giants such as Microsoft, AWS and Google providing very complete platforms that cover almost every aspect of a machine learning pipeline. As a result, it’s becoming increasingly hard to determine which platforms to use for a given machine learning problem. Should you go with end-to-end platforms like Azure ML or AWS SageMaker, or bank on innovative startups that are focusing on specific capabilities of a machine learning pipeline?

The lifecycle of machine learning solutions is very complex and, consequently, very difficult for a single platform to deliver quality value on all of its stages. At the same time, the current state of the machine learning market makes it really hard to determine which categories will remain as standalone submarkets. Data labeling, continuous deployment, model optimization are some of the areas that have the potential of creating a new generation of standalone platforms. However, companies like Microsoft, Amazon and Google are becoming highly acquisitive and rapidly building some of those capabilities into their platforms. When comes to machine learning, deciding between end-to-end or best-of-breed platforms is far from trivial.

Would love to hear from you about this debate. When the time comes to select a machine learning platform do you favor the consistency of end-to-end machine learning platforms like Azure ML or SageMaker or the fast innovation of best-of-breed startups? What do you base your choice on?

Let’s discuss

Now, to the most important developments in the AI industry this week

🔎 ML Research

Reasoning About Abstract Concepts

Researchers from MIT published a paper proposing a model that can identify abstract concepts in videos ->read more on MIT News

Quantum Chemistry Simulations

Researchers from the Google AI Quantum published a paper exploring ideas to conduct large chemical simulations in quantum computers ->read more on Google Research blog

Traffic Predictions with Graph Neural Networks

DeepMind and Google unveiled research that improves the accuracy of Google Maps real-time ETAs using graph neural networks ->read more on DeepMind blog

🤖 Cool AI Tech Releases


Facebook open-sourced Opacus, a framework for differential privacy in PyTorch models ->read more on Facebook blog


Microsoft open-sourced the platform for situated intelligence (PSI), a framework for research and implementation of AI models that use heterogeneous input data streams ->read more on Microsoft Research GitHub


Researchers from the Allen Institute for AI open-sourced AllenAct, a research framework for embodied AI ->read more on AllenAct GitHub

💸 Money in AI

  • Mustard app created to analyze an athlete’s mechanics and offer corrective tips to help them improve their technics, has raised $1.7 million to improve its tool

🔺🔻TheSequence Scope — our Sunday edition with the industry’s development overview — is free. To receive high-quality educational content every Tuesday and Thursday, please subscribe to TheSequence Edge 🔺🔻

🗓 Next week in TheSequence Edge:

Edge#17: the concept of Bayesian Neural Networks; a method created by DeepMind to use Bayesian Neural Networks to assess the fairness of a dataset; Pyro, a probabilistic programming language created by Uber.

Edge#18: the concept of Production-Ready notebooks; Microsoft research paper about the challenges with computational notebooks; Polynote, a next-generation notebook platform created by Netflix.

Written by

CEO of IntoTheBlock, Chief Scientist at Invector Labs, Guest lecturer at Columbia University, Angel Investor, Author, Speaker.

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