Cycode raises $20M to secure DevOps pipelines


Israeli security startup Cycode, which specializes in helping enterprises secure their DevOps pipelines and prevent code tampering, today announced that it has raised a $20 million Series A funding round led by Insight Partners. Seed investor YL Ventures also participated in this round, which brings the total funding in the company to $24.6 million.

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Opsera raises $15M for its continuous DevOps orchestration platform

Opsera platform

Opsera, a startup that’s building an orchestration platform for DevOps teams, today announced that it has raised a $15 million Series A funding round led by Felicis Ventures. New investor HMG Ventures, as well as existing investors Clear Ventures, Trinity Partners and Firebolt Ventures also participated in this round, which brings the company’s total funding to $19.3 million.

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A paper summarizer with Python and GPT-3

When access to the GPT-3 api was released to researchers and engineers (upon request) I immediately requested it so I could see what kind of interesting tools one could write and what kind of interesting research questions could be asked. Upon trying the api and the awesome tools that come with it, I realized that one of my favorite applications of GPT-3 was for paper summarization, so I decided to test it out.

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The DevOps Mindset: A step-by-step Plan to implement DevOps

DevOps implementation

People, processes, and tools are the three foundational pillars of DevOps — the term coined by Patrick Debois in 2009 to describe a new culture of collaboration and shared ownership in software development. But DevOps implementation is not all hard stuff. According to Patrick Debois, DevOps is more a human problem. Or, as Viktor Farcic put it in his book The DevOps Paradox, it’s a people game at its roots.

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Software product planning platform Productboard raises $72M

Product-board team

Productboard, a startup developing a DevOps orchestration system for enterprises, today announced that it raised $72 million in a series C round. The company says that the funds will be put toward expanding its team and customer base while supporting product research and development.

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12 No-Code Platforms for some DIY Machine Learning

Top 12 No-Code AI Platforms for Machine Learning

Enter the no-code machine learning movement. Only 25% of organizations are using artificial intelligence (AI) in their businesses today. Why? Custom AI-enabled solutions are expensive to build, as talented data scientists are a hot commodity today and don’t come cheap. Top performers can easily command over $250,000 in annual salary, which seriously makes us question the money we wasted invested in getting our MBAs. Not to mention, it can take months or even years to implement. CTOs are understandably suspicious of the latest buzzword du jour, so you need to show results fast.

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Scientists working on Continual Learning to overcome ‘Catastrophic Forgetting’ 

ContinualAI recently announced Avalanche, a library of tools compiled over the course of a year from over 40 contributors with the goal of making CL research easier and more reproducible. The organization was launched three years ago and has attracted support for its mission to advance CL, which its members see as fundamental for the future of AI.  

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Delivering AI/ML without proper Dataops is just wishful thinking!

DataOps Team Process

Given the iterative nature of AI/ML projects, having an agile process of building fast and reliable data pipelines (referred to as DataOps) has been the key differentiator in the ML projects that succeeded (unless there was a very exhaustive feature store available which is typically never the case).

Read More, which matches software engineers with jobs, raises $9.5M

Celential AI

AI-powered software engineering recruitment platform today announced that it raised $9.5 million in series A funding. It comes as Celential appoints Amer Akhtar, former Yahoo small business president and ex-CEO of ADP China, as the company’s new CEO.

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Use of Synthetic Data, in early stage, seen as an answer to Data Bias 

Assuring that the huge volumes of data on which many AI applications rely is not biased and complies with restrictive data privacy regulations is a challenge that a new industry is positioning to address: synthetic data production. Synthetic data is computer-generated data that can be used as a substitute for data from the real world.

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The rise of DataPrepOps

Night map USA

Modern data development tools and how data quality impacts ML results. ML is all around us! Data quality carries a very important and significant role in the development of AI solutions — just like the old “Garbage in, garbage out” — we can easily understand the weight of data quality and its potential impact in solutions like cancer detection or autonomous driving systems. But, paradoxically…

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7 reasons why you should consider a Data Lake (and Event-Driven ETL)

Man by a lake

A data lake doesn’t need to be the end destination of your data. Data is constantly flowing, moving, changing its form and shape. A modern data platform should facilitate the ease of ingestion and discoverability, while at the same time allowing for a thorough and rigorous structure for reporting needs.

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End to End Deep Learning: A different perspective

Whenever there is an article on an end-to-end deep learning project, it consists of training a deep learning model, deploying a Flask API, and then making sure it works or it extensively consists of creating a web demo using Streamlit or something similar. The problem with this approach is that it talks about a straight-forward and typical path that has been tried and tested. It merely takes replacing a single piece of the puzzle with an equivalent, such as a sentiment analysis model with a classification model, etc, and a new project can be created, but the wireframe remains mostly the same.

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Six stage gates to a successful AI governance


Responsible use of AI should start with a detailed assessment of the key risks posed by AI [1], followed by a good understanding of the principles that should be followed [2], and then the governance of AI from a top-down and end-to-end perspective [3]. We have discussed these in our previous articles [1, 2, 3]. In this article, we focus on the first line of defense and dive into the nine-step data science process [4] of value scoping, value discovery, value delivery, and value stewardship and highlight the dimensions of governance.

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Rule-Based AI vs. Machine Learning for Development – Which is best? 

Woman with idea

Rule-based AI systems borrow from rule-based expert system development, which tapped the knowledge of human experts to solve complex problems by reasoning through bodies of knowledge. Expert systems emerged in the 1970s and 1980s. Today rule-based AI models include a set of rules and a set of facts, described in a recent account in BecomingHuman/ Medium. “You can develop a basic AI model with the help of these two components,” the article states. 

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3 ways to get into reinforcement learning


When I was in graduate school in the 1990s, one of my favorite classes was neural networks. Back then, we didn’t have access to TensorFlow, PyTorch, or Keras; we programmed neurons, neural networks, and learning algorithms by hand with the formulas from textbooks. We didn’t have access to cloud computing, and we coded sequential experiments that often ran overnight. There weren’t platforms like Alteryx, Dataiku, SageMaker, or SAS to enable a machine learning proof of concept or manage the end-to-end MLops lifecycles.

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Scrum Vs. Waterfall: What is the Difference?

Office scene

In the last two decades, a lot of robust methodologies and frameworks for project management have established their roots deeply in the market. And to get effective collaboration and team management in the workplace, many Industries prefer methodologies to accomplish the project. However, having several methodologies as options makes the task hard, especially when each of them is unique in one way or the other.

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How to start a machine learning project with an external AI company — a practical guide

Blue hand

Today’s topic has resulted directly from discussions with our clients and discovering their concerns. I won’t beat around the bush — machine learning projects are characterized by a high risk. Unlike software development, which is difficult but still way easier to plan, there are many uncertainties involved. Starting an ML project pretty often you don’t know if your problem can be solved by technology at all because no one has ever solved it.

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