Blameless raises $30M to guide companies through their software lifecycle


Site reliability engineering platform Blameless announced Tuesday it raised $30 million in a Series B funding round, led by Third Point Ventures with participation from Accel, Decibel and Lightspeed Venture Partners, to bring total funding to over $50 million.

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The abolition of the data science cave

We’ve heard about explainability when it comes to understanding the decisions made by our data science models. But what about explainability during the development process? As data scientists we often work in interdisciplinary teams, where not everyone may be as familiar with our specialty, just as we are not familiar with theirs. These environments allow us to build the best possible…

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For successful AI Projects, celebrate your graveyard


AI teams invest a lot of rigor in defining new project guidelines. But the same is not true for killing existing projects. In the absence of clear guidelines, teams let infeasible projects drag on for months. AI projects are different from traditional software projects. They have a lot more unknowns: availability of right datasets, model training to meet required accuracy threshold, fairness and robustness of recommendations in production, and many more.

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What “Shifting Left” in Software really means for blameless DevOps

Shifting left - Blameless DevOps

A few really interesting ideas came out of this week’s panel on Enabling Smart Engineering Discussions. There’s a lot of talk these days around how to practice a “blameless culture” in software engineering, and I think it’s important to note the variety of views that make up that idea.

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4 factors for a successful PoC

Proof of concept

You have probably heard of the terms prototype or proof of concept before. These are well established terms in the manufacturing sector and product development circles that have seen a boom in recent years due to the explosion in software and technology, further popularized by the growth in the numbers of start-ups and software development philosophies such as lean and agile. But what do these terms mean and how are they relevant to building artificial intelligence and machine learning solutions?

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The “___Ops” in development LifeCycle

Ops lifecycle

Every tech company had a software engineering team that built software and an operations team that built infrastructure for running that software. And day by day it is become challenging to coordinate a group of people working toward a shared goal. Work involving large teams and complex processes is even more complicated. It is no wonder then that the technology industry sometimes struggles to find coherent terminology to describe its own processes and workflows.

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Interview with Mike Hanley, CSO at GitHub

Mike Hanely - Github

GitHub has always been leading the way in helping developers create secure software — from our early adoption of bug bounties, to the acquisitions of Dependabot and Semmle, the launch of the Security Lab, and more. I’ve joined as CSO to continue driving security both inside GitHub and for developers on the platform.

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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).

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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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