Search results for Startup Valuation

Found 71 documents, 5933 searched:

• Building a GPU Machine vs. Using the GPU Cloud

  The article examines the pros and cons of building an on-premise GPU machine versus using a GPU cloud service for projects involving deep learning and artificial intelligence, analyzing factors like cost, performance, operations, and scalability.

  https://www.kdnuggets.com/building-a-gpu-machine-vs-using-the-gpu-cloud

• The Generative AI Bubble Will Burst Soon

  Due to unsustainable hype, unrealistic valuations, limitations of current technology, and unproven business models.

  https://www.kdnuggets.com/the-generative-ai-bubble-will-burst-soon

• Who Will Make Money from the Generative AI Gold Rush?

  Buckle up for the Generative AI gold rush! Will BigTech rule with its picks and shovels? Which startups will strike it rich? Will “copilot for X” be the business strategy to hit pay dirt? How can startups dig moats to keep out other prospectors? And will the US once again have the richest gold seams?

  https://www.kdnuggets.com/2023/08/make-money-generative-ai-gold-rush.html

• Meet Gorilla: UC Berkeley and Microsoft’s API-Augmented LLM Outperforms GPT-4, Chat-GPT, and Claude

  The model is augmented with APIs from Torch Hub, TensorFlow Hub and HuggingFace.

  https://www.kdnuggets.com/2023/06/meet-gorilla-uc-berkeley-microsoft-apiaugmented-llm-outperforms-gpt4-chatgpt-claude.html

• LLM Apocalypse Now: Revenge of the Open Source Clones

  This is a story about how open-source projects are taking on the LLM industry.

  https://www.kdnuggets.com/2023/05/llm-apocalypse-revenge-open-source-clones.html

• Beyond Accuracy: Evaluating & Improving a Model with the NLP Test Library

  John Snow Labs has learned a lot about testing and delivering Responsible NLP models: not only in terms of policies and goals, but by building day-to-day tools for data scientists. The nlptest library aims to share these tools with the open-source community.

  https://www.kdnuggets.com/2023/04/john-snow-beyond-accuracy-nlp-test-library.html

• What’s With All the Layoffs in Tech?

  Answering all the questions that you've been asking about the layoffs in the tech industry.

  https://www.kdnuggets.com/2023/02/layoffs-tech.html

• Key Data Science, Machine Learning, AI and Analytics Developments of 2022

  It's the end of the year, and so it's time for KDnuggets to assemble a team of experts and get to the bottom of what the most important data science, machine learning, AI and analytics developments of 2022 were.

  https://www.kdnuggets.com/2022/12/key-data-science-machine-learning-ai-analytics-developments-2022.html

• The First ML Value Chain Landscape

  TheSequence recently released the first ever ML Chain Landscape shaped by data scientists, a new landscape that would be able to address the entire ML value chain.

  https://www.kdnuggets.com/2022/10/first-ml-value-chain-landscape-sequence.html

• How to land an ML job: Advice from engineers at Meta, Google Brain, and SAP

  Check out this video, summary and transcript of a discussion between co:rise co-founder Jake Samuelson and three outstanding ML engineers — Kaushik Rangadurai, Shalvi Mahajan, and Frank Chen — to hear their advice on landing a job in machine learning.

  https://www.kdnuggets.com/2022/08/corise-land-ml-job-advice-engineers-meta-google-brain-sap.html

• AI for Ukraine is a new educational project from AI HOUSE to support the Ukrainian tech community

  “AI for Ukraine” is a series of workshops and lectures held by international artificial intelligence experts to support the development of Ukraine’s tech community during the war. This is a non-commercial educational project by AI HOUSE – a company focused on building the AI/ML community in Ukraine and is part of the Roosh tech ecosystem.

  https://www.kdnuggets.com/2022/08/ai-house-ai-ukraine-new-educational-project-support-ukrainian-tech-community.html

• 15 Trending MLOps Talks You can Access for Free at ODSC East 2022

  Covering topics like workflows and full-stack machine learning, these are 15 free MLOps talks coming to #ODSCEast 2022 that you can see with a free Bronze Pass.

  https://www.kdnuggets.com/2022/04/odsc-15-trending-mlops-talks-access-free-odsc-east-2022.html

• People Management for AI: Building High-Velocity AI Teams

  Practical advice for managers and directors who are looking to build AI/ML teams.

  https://www.kdnuggets.com/2022/03/people-management-ai-building-highvelocity-ai-teams.html

• MLOps Is a Mess But That’s to be Expected

  In this post, I want to focus the discussion about the state of machine learning operations (MLOps) today, where we are, where we are going.

  https://www.kdnuggets.com/2022/03/mlops-mess-expected.html

• The Chatbot Transformation: From Failure to the Future

  The all-knowing chatbots we once thought to be the future have been replaced by specialized bots, and the results are outstanding.

  https://www.kdnuggets.com/2021/12/chatbot-transformation-failure-future.html

• Django’s 9 Most Common Applications">Django’s 9 Most Common Applications

  Django is a Python web application framework enjoying widespread adoption in the data science community. But what else can you use Django for? Read this article for 9 use cases where you can put Django to work.

  https://www.kdnuggets.com/2021/08/django-9-common-applications.html

• 10 Mistakes You Should Avoid as a Data Science Beginner

  Read this article on how to gain a competitive advantage in the data science job market.

  https://www.kdnuggets.com/2021/06/10-mistakes-avoid-data-science-beginner.html

• 10 Amazing Machine Learning Projects of 2020">10 Amazing Machine Learning Projects of 2020

  So much progress in AI and machine learning happened in 2020, especially in the areas of AI-generating creativity and low-to-no-code frameworks. Check out these trending and popular machine learning projects released last year, and let them inspire your work throughout 2021.

  https://www.kdnuggets.com/2021/03/10-amazing-machine-learning-projects-2020.html

• Hugging Face Transformers Package – What Is It and How To Use It

  The rapid development of Transformers have brought a new wave of powerful tools to natural language processing. These models are large and very expensive to train, so pre-trained versions are shared and leveraged by researchers and practitioners. Hugging Face offers a wide variety of pre-trained transformers as open-source libraries, and you can incorporate these with only one line of code.

  https://www.kdnuggets.com/2021/02/hugging-face-transformer-basics.html

• Machine learning is going real-time

  Extracting immediate predictions from machine learning algorithms on the spot based on brand-new data can offer a next level of interaction and potential value to its consumers. The infrastructure and tech stack required to implement such real-time systems is also next level, and many organizations -- especially in the US -- seem to be resisting. But, what even is real-time ML, and how can it deliver a better experience?

  https://www.kdnuggets.com/2021/01/machine-learning-real-time.html

• Support Vector Machine for Hand Written Alphabet Recognition in R

  We attempt to break down a problem of hand written alphabet image recognition into a simple process rather than using heavy packages. This is an attempt to create the data and then build a model using Support Vector Machines for Classification.

  https://www.kdnuggets.com/2021/01/support-vector-machine-hand-written-alphabet-r.html

• Model Experiments, Tracking and Registration using MLflow on Databricks

  This post covers how StreamSets can help expedite operations at some of the most crucial stages of Machine Learning Lifecycle and MLOps, and demonstrates integration with Databricks and MLflow.

  https://www.kdnuggets.com/2021/01/model-experiments-tracking-registration-mlflow-databricks.html

• Unpopular Opinion – Data Scientists Should Be More End-to-End

  Can a do-it-all Data Scientist really be more effective at delivering new value from data? While it might sound exhausting, important efficiencies can exist that might bring better value to the business even faster.

  https://www.kdnuggets.com/2020/09/data-scientists-should-be-more-end-to-end.html

• Here’s what you need to look for in a model server to build ML-powered services

  More applications are being infused with machine learning while MLOps processes and best practices are becoming well established. Critical to these software and systems are the servers that run the models, which should feature key capabilities to drive successful enterprise-scale productionizing of machine learning.

  https://www.kdnuggets.com/2020/09/model-server-build-ml-powered-services.html

• What I learned from looking at 200 machine learning tools

  While hundreds of machine learning tools are available today, the ML software landscape may still be underdeveloped with more room to mature. This review considers the state of ML tools, existing challenges, and which frameworks are addressing the future of machine learning software.

  https://www.kdnuggets.com/2020/07/200-machine-learning-tools.html

• A Layman’s Guide to Data Science. Part 3: Data Science Workflow">A Layman’s Guide to Data Science. Part 3: Data Science Workflow

  Learn and appreciate the typical workflow for a data science project, including data preparation (extraction, cleaning, and understanding), analysis (modeling), reflection (finding new paths), and communication of the results to others.

  https://www.kdnuggets.com/2020/07/laymans-guide-data-science-workflow.html

• Are Tera Operations Per Second (TOPS) Just hype? Or Dark AI Silicon in Disguise?

  This article explains why TOPS isn’t as accurate a gauge as many people think, and discusses other criteria that should be considered when evaluating a solution to a real application.

  https://www.kdnuggets.com/2020/05/tops-just-hype-dark-ai-silicon-disguise.html

• State of the Machine Learning and AI Industry

  Enterprises are struggling to launch machine learning models that encapsulate the optimization of business processes. These are now the essential components of data-driven applications and AI services that can improve legacy rule-based business processes, increase productivity, and deliver results. In the current state of the industry, many companies are turning to off-the-shelf platforms to increase expectations for success in applying machine learning.

  https://www.kdnuggets.com/2020/04/machine-learning-ai-industry.html

• Peer Reviewing Data Science Projects">Peer Reviewing Data Science Projects

  In any technical development field, having other practitioners review your work before shipping code off to production is a valuable support tool to make sure your work is error-proof. Even through your preparation for the review, improvements might be discovered and then other issues that escaped your awareness can be spotted by outsiders. This peer scrutiny can also be applied to Data Science, and this article outlines a process that you can experiment with in your team.

  https://www.kdnuggets.com/2020/04/peer-reviewing-data-science-projects.html

• Time Series Classification Synthetic vs Real Financial Time Series">Time Series Classification Synthetic vs Real Financial Time Series

  This article discusses distinguishing between real financial time series and synthetic time series using XGBoost.

  https://www.kdnuggets.com/2020/03/time-series-classification-synthetic-real-financial-time-series.html

• Deepfakes Security Risks

  Deepfakes have instilled panic in experts since they first emerged in 2017. Microsoft and Facebook have recently announced a contest to identify deepfakes more efficiently.

  https://www.kdnuggets.com/2020/01/deepfakes-security-risks.html

• Choosing a Machine Learning Model

  Selecting the perfect machine learning model is part art and part science. Learn how to review multiple models and pick the best in both competitive and real-world applications.

  https://www.kdnuggets.com/2019/10/choosing-machine-learning-model.html

• 5 Fundamental AI Principles

  While AI may appear magical at times, these five principles will help guide you to avoid pitfalls when leveraging this tech.

  https://www.kdnuggets.com/2019/10/5-fundamental-ai-principles.html

• How AI will transform healthcare (and can it fix the US healthcare system?)">How AI will transform healthcare (and can it fix the US healthcare system?)

  This thorough review focuses on the impact of AI, 5G, and edge computing on the healthcare sector in the 2020s as well as a look at quantum computing's potential impact on AI, healthcare, and financial services.

  https://www.kdnuggets.com/2019/09/ai-transform-healthcare.html

• Introducing Gen: MIT’s New Language That Wants to be the TensorFlow of Programmable Inference">Introducing Gen: MIT’s New Language That Wants to be the TensorFlow of Programmable Inference

  Researchers from MIT recently unveiled a new probabilistic programming language named Gen, a language which allow researchers to write models and algorithms from multiple fields where AI techniques are applied without having to deal with equations or manually write high-performance code.

  https://www.kdnuggets.com/2019/07/introducing-gen-language-progammable-inference.html

• A Gentle Guide to Starting Your NLP Project with AllenNLP

  For those who aren’t familiar with AllenNLP, I will give a brief overview of the library and let you know the advantages of integrating it to your project.

  https://www.kdnuggets.com/2019/07/gentle-guide-starting-nlp-project-allennlp.html

• Why you’re not a job-ready data scientist (yet)">Why you’re not a job-ready data scientist (yet)

  Trying to snag a dream Data Science job, but can't seem to land one? Check out these four skills that companies really want and be prepared for your next interview.

  https://www.kdnuggets.com/2019/07/not-job-ready-data-scientist-yet.html

• The Emergence of Cooperative and Competitive AI Agents

  Without specific training in collaboration or competition, a recent AI model from DeepMind uses reinforcement learning to evolve these behaviors in game-playing agents. Learn how this emergent collective intelligence outperforms their human counterparts in 3D multiplayer games.

  https://www.kdnuggets.com/2019/06/emergence-cooperative-competitive-ai-agents.html

• Advice for New Data Scientists">Advice for New Data Scientists

  We provide advice for junior data scientists as they begin their career, with tips and commentary from a tech lead at Airbnb.

  https://www.kdnuggets.com/2019/04/advice-new-data-scientists.html

• 4 Reasons Why Your Machine Learning Code is Probably Bad">4 Reasons Why Your Machine Learning Code is Probably Bad

  Your current ML workflow probably chains together several functions executed linearly. Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. That is your data science workflow should be a DAG.

  https://www.kdnuggets.com/2019/02/4-reasons-machine-learning-code-probably-bad.html

• How to Monitor Machine Learning Models in Real-Time

  We present practical methods for near real-time monitoring of machine learning systems which detect system-level or model-level faults and can see when the world changes.

  https://www.kdnuggets.com/2019/01/monitor-machine-learning-real-time.html

• Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools">Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools

  We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.

  https://www.kdnuggets.com/2018/12/machine-learning-data-visualization-deep-learning-tools.html

• Text Preprocessing in Python: Steps, Tools, and Examples

  We outline the basic steps of text preprocessing, which are needed for transferring text from human language to machine-readable format for further processing. We will also discuss text preprocessing tools.

  https://www.kdnuggets.com/2018/11/text-preprocessing-python.html

• What is Minimum Viable (Data) Product?

  This post gives a personal insight into what Minimum Viable Product means for Machine Learning and the importance of starting small and iterating.

  https://www.kdnuggets.com/2018/07/minimum-viable-data-product.html

• Top 10 Technology Trends of 2018">Top 10 Technology Trends of 2018

  In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.

  https://www.kdnuggets.com/2018/04/top-10-technology-trends-2018.html

• The Current Hype Cycle in Artificial Intelligence

  Over the past decade, the field of artificial intelligence (AI) has seen striking developments. As surveyed in, there now exist over twenty domains in which AI programs are performing at least as well as (if not better than) humans.

  https://www.kdnuggets.com/2018/02/current-hype-cycle-artificial-intelligence.html

• A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018">A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018

  In this article, we will compare the most commonly used platforms and analyze their main features to help you choose one or several platforms that will provide indispensable aid for your work communication.

  https://www.kdnuggets.com/2018/02/comparative-analysis-top-6-bi-data-visualization-tools-2018.html

• Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI">Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI

  A complete and unbiased comparison of the three most common Cloud Technologies for Machine Learning as a Service.

  https://www.kdnuggets.com/2018/01/mlaas-amazon-microsoft-azure-google-cloud-ai.html

• 70 Amazing Free Data Sources You Should Know">70 Amazing Free Data Sources You Should Know

  70 free data sources for 2017 on government, crime, health, financial and economic data, marketing and social media, journalism and media, real estate, company directory and review, and more to start working on your data projects.

  https://www.kdnuggets.com/2017/12/big-data-free-sources.html

• The Artificial ‘Artificial Intelligence’ Bubble and the Future of Cybersecurity

  What’s going on now in the field of ‘AI’ resembles a soap bubble. And we all know what happens to soap bubbles eventually if they keep getting blown up by the circus clowns (no pun intended!): they burst.

  https://www.kdnuggets.com/2017/06/kaspersky-artificial-intelligence-bubble-future-cybersecurity.html

• Learning to Learn by Gradient Descent by Gradient Descent

  What if instead of hand designing an optimising algorithm (function) we learn it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!

  https://www.kdnuggets.com/2017/02/learning-learn-gradient-descent.html

• Nigeria: Telling Internally Displaced Persons Stories Using Visual Data and Infographics

  Read a data-driven discussion on the plight of internally displaced persons (IDPs) in Nigeria, and see the real power of data science and data visualization.

  https://www.kdnuggets.com/2016/08/nigeria-displaced-persons-stories-visual-data-infographics.html

• Deep Learning for Chatbots, Part 1 – Introduction

  The first in a series of tutorial posts on using Deep Learning for chatbots, this covers some of the techniques being used to build conversational agents, and goes from the current state of affairs through to what is and is not possible.

  https://www.kdnuggets.com/2016/04/deep-learning-chatbots-part-1.html

• Lift Analysis – A Data Scientist’s Secret Weapon

  Gain insight into using lift analysis as a metric for doing data science. Understand how to use it for evaluating the performance and quality of a machine learning model.

  https://www.kdnuggets.com/2016/03/lift-analysis-data-scientist-secret-weapon.html

• The Data Science Machine, or ‘How To Engineer Feature Engineering’

  MIT researchers have developed what they refer to as the Data Science Machine, which combines feature engineering and an end-to-end data science pipeline into a system that beats nearly 70% of humans in competitions. Is this game-changing?

  https://www.kdnuggets.com/2015/10/data-science-machine.html

• Paradoxes of Data Science

  There are many paradoxes, ironies and disconnects in today’s world of data science: pain points, things ignored, shoved under the rug, denied or paid lip.

  https://www.kdnuggets.com/2015/08/paradoxes-data-science.html

• Cognitive Computing: Solving the Big Data Problem?

  With a shortage of data scientists, what are the alternatives for making sense of Big Data? We examine Cognitive Computing, its strengths, and how it can fit into the current Big Data landscape.

  https://www.kdnuggets.com/2015/06/cognitive-computing-solving-big-data-problem.html

• How to Lead a Data Science Contest without Reading the Data

  We examine a “wacky” boosting method that lets you climb the public leaderboard without even looking at the data . But there is a catch, so read on before trying to win Kaggle competitions with this approach.

  https://www.kdnuggets.com/2015/05/data-science-contest-leaderboard-without-reading-data.html

• Machine Learning Wars: Amazon vs Google vs BigML vs PredicSis

  Comparing 4 Machine Learning APIs: Amazon Machine Learning, BigML, Google Prediction API and PredicSis on a real data from Kaggle, we find the most accurate, the fastest, the best tradeoff, and a surprise last place.

  https://www.kdnuggets.com/2015/05/machine-learning-wars-amazon-google-bigml-predicsis.html

• Automatic Statistician and the Profoundly Desired Automation for Data Science

  The Automatic Statistician project by Univ. of Cambridge and MIT is pushing ahead the frontiers of automation for the selection and evaluation of machine learning models. In general, what does automation mean to Data Science?

  https://www.kdnuggets.com/2015/02/automated-statistician-data-science.html

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