H2O
cockroach
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H2O | cockroach | |
---|---|---|
10 | 100 | |
6,730 | 29,076 | |
1.1% | 1.2% | |
9.7 | 10.0 | |
1 day ago | 1 day ago | |
Jupyter Notebook | Go | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
H2O
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Really struggling with open source models
I would use H20 if I were you. You can try out LLMs with a nice GUI. Unless you have some familiarity with the tools needed to run these projects, it can be frustrating. https://h2o.ai/
- Democratizing Large Language Models
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Interview AI Coach - by email
Here is the transcribed portion of what you sent: Within this project, or another example, for some examples of maybe encountering resistance or someone who's just like a specific person who seemed really opposed to your ideas that you had to influence or win over, and how you approach that sort of personality-based problem. Yeah, great question. So, at Lineate, I mentioned earlier that I helped to kind of upscale the entire workforce. We're talking 200 engineers, marketing folks, sales folks, account managers. And I had just, so in an effort to kind of upscale this and identify opportunities for machine learning, I followed Andrew Ng's framework for approaching ML in the enterprise. Basically, it's like one-pagers, where I define the problem statement. Do we have access to the data? Do we have data privacy or regulation concerns? What are some risk assumptions, success criteria, all that stuff. So, I put together like 20 plus one-pagers across all the different opportunities, and I generated a successful proof of concept with the team after it was a failure, of course, at first, but we turned it into a success. And part of this Andrew Ng framework in AI in the enterprise, it's like you want to generate a center of AI excellence, where it's like you share best practices with the rest of the organization. So, nobody told me that I had to do this, but this is kind of like something that I aspire towards. And in the process of trying to be inclusive with the 200 engineers, there was one engineer who was unwilling to participate. There was a phase two of his project that had an AI component that used the same tool that we used in Google Cloud. And I opened a Slack channel with our team and himself to try to get him to share what he's working with so that my team can also share what learnings we had with that tool. He just wasn't willing to participate. I just couldn't understand. It's like, how can you not? I mean, this isn't your benefit. This is a team. You got to be a team player. So, my first reaction was like, seek to understand, what's the context here? What's the background? I asked around. I talked to engineers who worked with him. I talked to higher ups without kind of like mentioning that this person was problematic, but just to understand what the nature is. And it turns out he doesn't report to the director of Solution Architecture Engineering. Instead, he reports directly to the CEO. I was like, oh, that's interesting. It turns out he came into the company through an acquisition. He was like a startup founder. So, he's used to running the show. So, when it comes to working with a team of 200 engineers, he's a superstar in terms of performance, but maybe team play-wise, not so much. So, understanding that context really helped me understand where he's coming from. And the next thing I did was I tried to anticipate, what are some of his needs? What can I do to help him reach his goals? And he wanted to, of course, do well on his project because he's a high performer. He wants to be aware of any risks early on. So, what I did was I got a hold of a sample dataset from the work that he was doing. And since I had access to some tools that he did not have, like h2o.ai, DataRobot, I took some of his data samples, put it into these tools, ran different algorithms on them, like GBM, different neural networks, to get a sense of what does a confusion matrix look like? What is this two by two matrix of true positive, false positive, and stuff like that. So, I was able to deliver some of these confusion matrices to him so that he's aware of it. And another thing is, I said, the tool that you're using is the same tool that we used. Well, guess what? It doesn't do so well in a sub-10 millisecond environment, which is one of the needs of your project. You might want to consider SageMaker endpoint where you can deploy artifact there so that this latency requirement is not a problem. So, I kind of anticipated where his needs are, being proactive to help him, offer advice where I anticipated that he needed help and extra guidance. He started kind of like more open up. And guess where I shared some of these insights? I shared it in the channel that he originally did not want to participate in. And I said, I'm going to share it in this channel. So, then he takes a look there and he starts replying to that. So, now I kind of like, kind of guided him to take one step into like this channel. So, now whatever reply he says, then my engineers can see that reply. And now it's like we have a team spirit going on now. So, that's like how I kind of got him from not wanting to participate to now participating. And on top of that, I also did like these company wide webinars where I showcase our teams. I put their profile pictures on the front slide. So, when everybody dials in, then they could see like, these are the people on my team. Here's what we're working on. And I asked him, you're really good at what you do. I would love to include you in this team in the next meeting. Are you okay with it if I put your profile picture on the front page? And he said, yes, right away. So, like helping to kind of like, because it's not like I need the credit. I just distribute some of the visibility to some of these star engineers and kind of like in exchange, you get like better collaboration. And that goal of the AI Center for Excellence for better kind of sharing best practices and learnings. So, I think by doing that, I was able to kind of like turn an icky situation into something that became a team effort. That's awesome. Love it.
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Top 10+ OpenAI Alternatives
H2O.ai
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Best machine learning framework(s) for production
Thanks for the input. To clarify, I am more focused on choosing the modeling framework(s) that makes the most sense to use for future production. For example, is h2o.ai a good framework for training models for later deployment (through something like elastic beanstalk, Flask API's etc.)? I came across a number of mentions of Tensorflow, however it is focused on neural nets while I also want to use classic models such as random forests, etc.
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Time Series Analysis - Too Narrow a Dataset / Feature Set?
I've also initialised an instance of H2O.ai, so I can parse into the server each product, by store, segmented. It can then train the models, determine which model is the most performant, and then save it. Because the variability of different product SKU, at different hospitals, is substantial.
- A Tiny Grammar of Graphics
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20+ Free Tools & Resources for Machine Learning
H2O.ai H2O is a deep learning tool built in Java. It supports most widely used machine learning algorithms and is a fast, scalable machine learning application interface used for deep learning, elastic net, logistic regression, and gradient boosting.
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Data Science Competition
H20
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[PAID] Looking for Phaser.js game developer
Built and founded various web3 projects for last 2 years such as OpenArt and 8RealmDojo for last 2 years as well as being high performing student in CTU in Prague and SeoulTech. Was offered internships in Amazon and H2O.ai. Created robots assistants using robots from SoftBank.
cockroach
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11 Planetscale alternatives with free tiers
CockroachDB is an open source distributed SQL database designed for scalability and resilience. While it offers SQL databases, CockroachDB is also compatible with PostgreSQL.
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A MySQL compatible database engine written in pure Go
cockroachdb might be close: https://github.com/cockroachdb/cockroach
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No More Free Tier on PlanetScale, Here Are Free Alternatives
CockroachDB - SQL
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Is it bad to create a publicly accessible RDS database for my serverless web app?
For example, when you create a serverless postgres database with a platform like CockroachDB or Neon, you effectively get a connection string with a strong password. Anyone can connect to your database from anywhere so long as they have the right connection string. There are no security settings in these services to change this behavior.
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Linux surpasses the Mac among Steam gamers
> Yes you can on the android emulator. The biggest issue is compu arch in that case.
I can also download VirtualBox and run all Windows programs, that would mean that all Windows apps are Linux apps?
> Yes you can for the most part
You can't statically link glibc: https://github.com/cockroachdb/cockroach/issues/3392
glibc can break stuff: https://www.gamingonlinux.com/2022/08/valve-dev-understandab...
I had binaries break because the newer version if openssl was put under a slightly different name.
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How do small SaaS's handle databases?
Also, worth noting, if you're already using PostgreSQL (or plan to) you might want to take a look at https://www.cockroachlabs.com/ they have a free tier too and CockroachDB has a PostgreSQL interface.
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Go Dependency management in large company projects - How do you do it?
I know that some projects like cockroach use custom build tools like bazel. But we actually really like to use to be able to build our projects simply with the great go toolchain and don't really aim to dive deep into custom build solutions.
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Eli5: Why do companies use the products of Oracle to store information, when they can just use spreadsheets like Excel, or make their own spreadsheet software?
CockroachDB is designed to be globally distributed. It has to handle causality when resolving collisions. It has to account for having a write operation to arrive after another and still have time priority because it was sent out a few milliseconds earlier.
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rage - a minimalistic load testing tool
Cockroachdb created a go runtime patch which measures the Grunning time of a goroutine: https://github.com/cockroachdb/cockroach/pull/82356. It doesn't entirely solve the problem though.
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Data Engineering Tools in Go
Our entire backend is written in Go. We've built a platform that allows other companies to offer automatic data syncing to their customers' data warehouses. Go works great for building distributed systems like this (see K8s). We're not the only ones in the space building data intensive applications with Go. Pachyderm, Pinecone, Cockroach Labs and are all also doing it. We've been quite happy with how Go has worked for us.
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
vitess - Vitess is a database clustering system for horizontal scaling of MySQL.
scikit-learn - scikit-learn: machine learning in Python
neon - Neon: Serverless Postgres. We separated storage and compute to offer autoscaling, branching, and bottomless storage.
pycaret - An open-source, low-code machine learning library in Python
tidb - TiDB is an open-source, cloud-native, distributed, MySQL-Compatible database for elastic scale and real-time analytics. Try AI-powered Chat2Query free at : https://tidbcloud.com/free-trial
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
yugabyte-db - YugabyteDB - the cloud native distributed SQL database for mission-critical applications.
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
InfluxDB - Scalable datastore for metrics, events, and real-time analytics