Time-series (TS) filters are often used in digital signal processing for distributed acoustic sensing (DAS). The goal is to remove a subset of frequencies from a digitised TS signal. To filter a signal you must touch all of the data and perform a convolution. This is a slow process when you have a large amount of data. The purpose of this post is to investigate which filters are fastest in Python.
Read moreReinforcement Learning (RL) frameworks help engineers by creating higher level abstractions of the core components of an RL algorithm. This makes code easier to develop, easier to read and improves efficiency.
But choosing a framework introduces some amount of lock in. An investment in learning and using a framework can make it hard to break away. This is just like when you decide which pub to visit. It’s very difficult not to buy a beer, no matter how bad the place is.
Read moreI’m excited to announce that I have agreed with O’Reilly Media to write a new book on Reinforcement Learning. The contracts have just been signed and I’ve started the writing process. It is likely to take around a year to be released so I’m hoping that it will be ready around Summer 2020.
Read moreAt their Cloud’s Next 19 conference, Google has announced the launch of an expanded AI platform. For a number of years Google has been expanding it’s portfolio to compete with AI products from Azure and AWS. But this is the first time that the platform can be considered “end-to-end”.
Read moreI’ve seen a few posts recently about the emergence of a new field that is kind of like DevOps, but not quite, because it involves too much data. Verbally, about two years ago and in blog form about a year ago, I used the word DataDevOps, because that’s what I did. I develop and operate Data Science platforms, products and services.
But more recently I have read of the emergence of DataOps. Apparently enterprises have realised that it takes more than a PhD in Data Science to create products and value (not that I begrudge the value of a PhD, I have one, after all!). It also takes engineering. Specifically, software engineering, to perform a series of tasks that support the wafer-thin slice of the product cake that represents the Data Science model.
Read moreDeveloping Jenkinsfile pipelines is hard. I think my world record for the number of attempts to get a working Jenkinsfile is around 20. When you have to continually push and run your pipeline on a managed Jenkins instance, the feedback cycle is long. And the primary bottleneck to developer productivity is the length of the feedback cycle.
Read moreNearly everyone using Python for Data Science has used or is using the Pandas Data Analysis/Preprocessing library. It is as much of a mainstay as Scikit-Learn. Despite this, one continuing bugbear is the different core data types used by each: pandas.DataFrame and np.array. Wouldn’t it be great if we didn’t have to worry about converting DataFrames to numpy types and back again? Yes, it would. Step forward Scikit Pandas.
Helm is billed as “the package manager for Kubernetes”. The goal was to provide a high-level package management-like experience for Kubernetes. This was a goal for all the major containerisation platforms. For example, Apache Mesos has Mesos Frameworks. And given the standardisation on package management at an OS level (yum, apt-get, brew, choco, etc.) and an application level (npm, pip, gem, etc.), this makes total sense, right?
Read moreWinder.AI was engaged by Bitsensor to research and implement Data Science algorithms that could automate the detection and classification of web attackers. After gathering data, researching a Machine Learning solution and implementing Cloud-Native software, we delivered three new features:
Bitsensor is a startup in the Netherlands that specializes in protecting public-facing websites and applications. They distribute their web-application firewall product to a range of customers throughout Europe. The goal is to provide an outstanding out-of-the-box experience that can protect exposed services from hackers, with little setup.
Read moreWinder.AI worked with its partner, Container Solutions, to deliver core components of the Weave Cloud Platform-as-a-Service (PaaS).
Weaveworks makes it fast and simple for developers and DevOps teams to build and operate powerful containerized applications. They minimize the complexity of operating workloads in Kubernetes by providing automated continuous delivery pipelines, observability and monitoring. Weaveworks also contributes to several open source projects, including Weave Scope, Weave Cortex and Weave Flux. It was one of the first members of the Cloud Native Computing Forum. Founded in 2014, the Company is backed by Google Ventures and Accel Partners. For more information, visit www.weave.works.
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