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spicyramen

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Software developer. Singapore. Skills: Typescript, SQL, CSS, React, Node, Python, Flask, Django.

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Totally agree with you, today Cloud companies: GCP, AWS and Azure native ML solutions are not a E2E platform but different teams that work independently and as a whole release a ML solution, specifically I can talk about Vertex AI. Notebooks, Training, Prediction while they provide Enterprise features (Security, Network, IAM, Encryption) do not play well with users. (Expect ML users to be Cloud Engineers) and an ML E2E workflow is hard to achieve.

I would divide the main challenges for AI startups as follows: 1. Support Enterprise Features. (Security, IAM, VPC-SC, Ecripyion) 2. If providing Compute Resources do not make that your main source of income (i.e. DeepNote, Saturn Cloud) which that may not scale. 3. Data integration. (BigQuery, S3, GCS, etc)

Databricks is one of the ones that have integrated with each of the clouds and provide this E2E workflow nicely, in addition they have seen the nascent Analytics market and invest on it. My concern with some startups: Cohere.AI similar to OpenAI GPT3, is that some are only solving some part of the ML workflow: (Seldom, OctoML, etc.) they may get some customers now, but will be hard to scale, and probably best destiny is getting acquire by major players.

[dead] 5 years ago

As a black person that have lived in Oakland i can tell you we need more bugdet and police and criminals behind bars

In my experience I have seen some candidates that work very hard and mainly do boiler plate code in their projects, they struggle in the Algos/data structures but at the end of the day they get the job done. Others perform very good in Algos/data structures but produce very little at work, and also people that do good and perform above expectations. Is hard for me to actually filter good candidates, and at the end of the day, I value output and some quality.

The West normally lacks understanding what hard work, innovation and intelligence is a universal value. During my years at Stanford I had the opportunity to learn about the North Korea nuclear program and how the West never imagine the advancement they produced with very limited resources and without Russian help. Couple of professors visited the nuclear facilities as part of the UN investigation and were shocked with the intelligence, and innovation of Korean engineers. The rest is history and we have now a North Korea with nuclear capabilities

In foreign languages such as Spanish, ortography is very important: reading helps you get better at it. This is true normally pre-college years.

We used to have very long shell scripts and recently I refactor most of them to use functions and shellcheck in our presubmit. This has greatly helped catching bugs and improving readability

Leaving Google 5 years ago

Still depends on the org. Many orgs still engineering driven. This happens when engineers are more familiar with tech and business than PM, which surprisingly is not that uncommon (GCP)

Very limited and unfair comparison between Kubeflow and metaflow. Metaflow is dependent on AWS (it is mentioned but not emphasized). To me this is a non-starter. It makes sense for Netflix but not for the rest of the world

Similar chaos happened during Bush to Obama transition and now in Afghanistan. I assume government transition for a country which invaded yours is always a red flag.