Issue #295 - The ML Engineer ??
Alejandro Saucedo
Tech Executive @ Zalando | Chair/Advisor @ UN, EU, ACM, etc | Join 60k+ ML Newsletter
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This week in Machine Learning:
Salesforce AI steps into the foundation LLM race with their latest model MINT-1T as the first open-source multimodal model with one trillion tokens: MINT-1T drives quite a few interesting innovations such as expanding on the previously experimented datasets by incorporating diverse data sources including HTML documents, PDFs, and ArXiv papers. This scale seems to allow for better domain coverage, particularly in scientific documents with results suggesting that models trained on MINT-1T outperform those trained on prior datasets in tasks such as captioning and visual question answering.
As organisations are looking to develop their internal machine learning experimentations platform they wrestle with the question of "build vs buy" - this article showcases how Eppo replaced Airflow with an in-house solution built using NodeJs, BullMQ, and Postgres. This article provides interesting view of Airflow's limitations and high maintenance as their workloads scale, as well as showing that alternatives like Dagster and Argo CD had similar limitations. There are interesting perspectives to developing an in-house solution however it indeed is surprising to see that organisations are still not able to build their internal e2e machine learning platform with best-of-breed open source tools and have to resort to complex custom-built platforms.
25 Computer Science Papers every programmer and MLOps practitioner should read: In Distributed Systems and Databases: 1.1 Google File System (GFS), 1.2 Amazon Dynamo, 1.3 BigTable (Google), 1.4 Cassandra (Apache), 1.5 Google Spanner, 1.6 FoundationDB, and 1.7 Amazon Aurora, focusing on scalable, fault-tolerant storage and database systems. In Data Processing and Analysis (2): 2.1 MapReduce (Google), 2.2 Hadoop (Apache), 2.3 Flink (Apache), 2.4 Kafka (Apache), 2.5 Dapper (Google), and 2.6 Monarch (Google), which revolutionized data processing and streaming. In Complex Challenges in Distributed Systems (3): 3.1 Google Borg, 3.2 Uber Schemaless, 3.3 Google Zanzibar, 3.4 Thrift (Facebook), 3.5 Raft Consensus Algorithm, and 3.6 Time, Clocks, and Ordering of Events (1978), addressing containerization, consensus algorithms, and access control. In Groundbreaking Concepts and Architectures (4): 4.1 Attention is All You Need (Transformer), 4.2 Bitcoin White Paper, and 4.3 Go-To Statement Considered Harmful, which introduced transformative ideas in NLP, blockchain, and programming practices. Finally, in Specific Applications and Optimizations (5): 5.1 Memcached, 5.2 RocksDB (MyRocks), 5.3 Twitter's Who to Follow Service, and 5.4 Survey on Vector Databases (2021), focusing on optimizations in caching, storage, recommendations, and high-dimensional data handling.
A great set of lessons from the Frontlines of AI Training from leading research labs in AI: This is a great resource that highlights how AI labs are innovating in data strategies, including the use of synthetic data, advanced data curation techniques, and scalable management solutions, to overcome challenges like potential data scarcity. This is a great reminder of how high-quality data is absolutely key in developing effective AI models.
Apple also joins the race of foundation AI models with an advanced ~3 billion parameter AI system integrated into their devices: This is an insightful innovation in on-device / edge deep learning optimized for efficiency and tailored to everyday tasks like text editing, notifications, and image creation. Apple showcases how these models are trained using Apple's AXLearn framework, and how they were able to focus on privacy and responsible AI principles.
Upcoming MLOps Events
The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below.
Upcoming conferences where we're speaking:
Other upcoming MLOps conferences in 2024: ?
In case you missed our talks:
Open Source MLOps Tools
Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ? github stars. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. Four featured libraries in the GPU acceleration space are outlined below.
? If you know of any open source and open community events that are not listed do give us a heads up so we can add them!
OSS: Policy & Guidelines
As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following:
If you know of any guidelines that are not in the "Awesome AI Guidelines" list, please do give us a heads up or feel free to add a pull request!
About us
The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning.