🥗 Will Machine Learning Data Infrastructures Become Commoditized?
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The value capture in software technology trends fluctuates between infrastructure and applications. Some market cycles are dominated by momentum in infrastructure companies. Then the pendulum swings gradually towards applications. This cycle repeats constantly. However, after long market cycles, many infrastructure building blocks become commoditized. Think about the trajectory of storage and compute infrastructure of platforms like AWS and Azure; or database infrastructure of platforms like Oracle. Following that thesis, we should expect many of the infrastructure building blocks of machine learning (ML) to become increasingly commoditized. But will they?
ML has proven to challenge many traditional conventions in traditional software markets. Infrastructure commoditization might not be an exception. The current ML market cycle is, without a doubt, dominated by infrastructure companies, but the value capture seems to be increasing rather than decreasing. Nothing should be more a commodity in ML than storage and compute infrastructure. And yet, this week, Databricks announced a monster funding round that values the company at $38 billion, which seems inconceivable for a pure data-compute infrastructure platform. That valuation is about nine times the market capitalization of big data pioneer Cloudera. Given ML’s tight dependency on data and compute, it seems likely that the value capture dynamics of this new market are different from other technology trends. From that perspective, the idea that ML infrastructure will get commoditized is not trivial at all. Certainly, the best days of ML infrastructure seem to be ahead of us.
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🗓 Next week in TheSequence Edge:
Edge#121: we discuss transformers and time-series; we explore Google Research’s paper about Temporal Fusion Transformers; we overview GluonTS.
Edge#122: in this ‘What’s New in AI’ edition, we deep dive into Unified VLP, a transformer model for visual question answering.
Now, let’s review the most important developments in the AI industry this week
🔎 ML Research
Self-Supervised Learning for Anomaly Detection
Google Research published a paper proposing a self-supervised learning method for anomaly detection in classification problems ->read more on Google Research blog
Dataset for 3D Object Reconstruction
Facebook AI Research published a research paper and open-source version of Common Objects in 3D (CO3D), a dataset to train models in 3D object reconstruction problems ->read more on FAIR blog
Fast Reinforcement Learning
Salesforce Research published a research paper and open-source version of WarpDrive, a framework for fast performance multi-agent reinforcement learning models ->read more on Salesforce Research blog
Computational Graph in PyTorch
The PyTorch team published a very insightful blog post explaining the internal mechanisms used to build computational graphs in the deep learning framework ->read more on PyTorch blog
🛠 Real World ML
Detecting Abusive Activity at LinkedIn
The LinkedIn engineering team detailed the deep learning techniques used to detect abusive user activity ->read more on LinkedIn blog
Real-Time Streaming Analytics at Uber
The Uber engineering team explained the architecture used to power real-time streaming analytics at the transportation giant ->read more on Uber Engineering blog
🤖 Cool AI Tech Releases
Apache Drill v1.19
Apache Drill just reached a new version with enhanced SQL querying capabilities ->read more in their press release
Run:AI’s ResearcherUI and MLOps Support
ML compute management platform Run:AI announced a new release that includes integrations with several MLOps stacks such as KubeFlow, Airflow, and MLFlow ->read more on Run:AI blog
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