Sign Up Now!

Sign up and get personalized intelligence briefing delivered daily.


Sign Up

Articles related to "need"


What does a Data Scientist actually do?

  • And so now the general public thinks of Data Scientists as researchers focused on machine learning and AI when in reality in some cases the industry is hiring data scientists who will never actually perform any AI or machine learning-related tasks (maybe because the company is still not ready for it).
  • Then, in case the medium-sized company does a lot of recommendation models or stuff that requires AI, then the Data Scientist will take care of the rest of the pyramid.
  • I hope that this article will provide you with a bit more clarity on the actual role of a Data Scientist and in case you are considering it as the next step in your career, you might want to have a look at the size of the company you want to work for, since that will drastically change your responsibilities and overall tasks.

save | comments | report | share on


What does a Data Scientist actually do?

  • And so now the general public thinks of Data Scientists as researchers focused on machine learning and AI when in reality in some cases the industry is hiring data scientists who will never actually perform any AI or machine learning-related tasks (maybe because the company is still not ready for it).
  • Then, in case the medium-sized company does a lot of recommendation models or stuff that requires AI, then the Data Scientist will take care of the rest of the pyramid.
  • I hope that this article will provide you with a bit more clarity on the actual role of a Data Scientist and in case you are considering it as the next step in your career, you might want to have a look at the size of the company you want to work for, since that will drastically change your responsibilities and overall tasks.

save | comments | report | share on


What does a Data Scientist actually do?

  • And so now the general public thinks of Data Scientists as researchers focused on machine learning and AI when in reality in some cases the industry is hiring data scientists who will never actually perform any AI or machine learning-related tasks (maybe because the company is still not ready for it).
  • Then, in case the medium-sized company does a lot of recommendation models or stuff that requires AI, then the Data Scientist will take care of the rest of the pyramid.
  • I hope that this article will provide you with a bit more clarity on the actual role of a Data Scientist and in case you are considering it as the next step in your career, you might want to have a look at the size of the company you want to work for, since that will drastically change your responsibilities and overall tasks.

save | comments | report | share on


Designing ML Orchestration Systems for Startups

  • This article covers the journey of architecture design, technical tradeoffs, implementation details, and lessons learned as a case study on designing a machine learning orchestration platform for startups.
  • This was my first time building a machine learning platform from scratch, so plenty of unanticipated implementation challenges arose that required some decision-making throughout the coding process.
  • For laying down the initial infrastructure and integration testing, we set a goal of deploying a neutral first launch that disregarded machine learning model performance.
  • Enabling fast and lightweight experimentation is more valuable in a machine learning product space than building out complex, feature-heavy R&D models.
  • Model portability and easy configuration of simple, lightweight ML models are consequently higher value initial goals than investing in complex, high-effort R&D algorithm support like Tensorflow, Pytorch, or Spark frameworks.

save | comments | report | share on


What does a Data Scientist actually do?

  • And so now the general public thinks of Data Scientists as researchers focused on machine learning and AI when in reality in some cases the industry is hiring data scientists who will never actually perform any AI or machine learning-related tasks (maybe because the company is still not ready for it).
  • Then, in case the medium-sized company does a lot of recommendation models or stuff that requires AI, then the Data Scientist will take care of the rest of the pyramid.
  • I hope that this article will provide you with a bit more clarity on the actual role of a Data Scientist and in case you are considering it as the next step in your career, you might want to have a look at the size of the company you want to work for, since that will drastically change your responsibilities and overall tasks.

save | comments | report | share on


Designing ML Orchestration Systems for Startups

  • This article covers the journey of architecture design, technical tradeoffs, implementation details, and lessons learned as a case study on designing a machine learning orchestration platform for startups.
  • This was my first time building a machine learning platform from scratch, so plenty of unanticipated implementation challenges arose that required some decision-making throughout the coding process.
  • For laying down the initial infrastructure and integration testing, we set a goal of deploying a neutral first launch that disregarded machine learning model performance.
  • Enabling fast and lightweight experimentation is more valuable in a machine learning product space than building out complex, feature-heavy R&D models.
  • Model portability and easy configuration of simple, lightweight ML models are consequently higher value initial goals than investing in complex, high-effort R&D algorithm support like Tensorflow, Pytorch, or Spark frameworks.

save | comments | report | share on


What does a Data Scientist actually do?

  • And so now the general public thinks of Data Scientists as researchers focused on machine learning and AI when in reality in some cases the industry is hiring data scientists who will never actually perform any AI or machine learning-related tasks (maybe because the company is still not ready for it).
  • Then, in case the medium-sized company does a lot of recommendation models or stuff that requires AI, then the Data Scientist will take care of the rest of the pyramid.
  • I hope that this article will provide you with a bit more clarity on the actual role of a Data Scientist and in case you are considering it as the next step in your career, you might want to have a look at the size of the company you want to work for, since that will drastically change your responsibilities and overall tasks.

save | comments | report | share on


Some Americans are just tuning into the election now. Here's the news they really need

  • New York (CNN Business) - With the 2020 election well underway and less than two weeks until the votes are counted, this reminder for reporters is simple but essential: Some Americans are plugging in to this election for the first time.
  • There are great examples of this already: CNN's Election 101 website and podcast; NBC's "Plan Your Vote" guide; the Washington Post's "how to vote in your state" feature.
  • The filmmakers at "Frontline" are out with the latest edition of their election season series "The Choice," with what they call "investigative biographies" of President Trump and Joe Biden.
  • Stories need to emphasize why this election season is unique and why it could take longer to know the results, due to the rise in mail-in ballots.
  • That's the headline on this New York Times op-ed by Stony Brook University professors Yanna Krupnikov and John Barry Ryan.

save | comments | report | share on


Exploiting the differences between model training and prediction.

  • In this post I clarify how we make sure that models trained using standard ML libraries such as PyTorch, Scikit-learn, and Tensorflow can be deployed efficiently on various edge devices.
  • The training process will effectively use both functions repeatedly: Initially, the parameters of the model are randomly instantiated.
  • In general, this training process is computationally demanding which explains why for complex models we resort to parallel computations and GPU or NPU acceleration to carry it out in a reasonable time.
  • That’s nice for several reasons; for one because we might like to contribute to the Paris Agreement and thus deploy models efficiently without wasting energy each time we generate predictions.
  • But, a small footprint and quick execution are also appealing because this is exactly what we need when putting model into production on the Edge: Good luck deploying your Docker container on (e.g.,) a ESP32 MCU board.

save | comments | report | share on


The case for a national COVID-19 plan

  • It is the lack of consultation and the inability to listen to the business community that has contributed to the health crisis and now will make economic recovery more difficult.
  • To give businesses the confidence they need to invest, we need all the states to publish their response plans for COVID-19 – or, better still, one response plan for the nation, if possible.
  • We need information on how to respond to outbreaks in metropolitan Australia, in country towns and major centres, and in Indigenous communities.
  • What is the membership of COVID-19 response teams and the planned response times – and will these teams include representatives from the Defence Force, police, health authorities, local government and industry?
  • There is a compelling need to collate the information that has been gathered, and to publish a plan for managing our health and economic future.

save | comments | report | share on