The potential for Machine Learning to make DevOps faster, better and smarter has been hotly contested as one of the main areas to watch in the future of technology for 2018 and beyond. The rise in enterprises, of all sizes, adopting DevOps methodologies has risen in parallel to the prevalence and accessibility of Machine Learning, therefore it only logical that there has been a trend towards considering how these two areas can collide to produce successful outcomes in the endeavour to further streamline workflows.
On 29 - 30 November, RE•WORK is hosting the Machine Learning for DevOps Summit in Houston, Texas, to bring together influential industry experts, disruptive startups and leading researchers to discover how to optimize DevOps and enhance automation capabilities with Machine Learning. The summit will be an opportunity to learn the best practices for implementing Machine Learning tools in DevOps to ultimately deliver more value to your business and achieve better automation through more efficient problem solving reduced operational complexity, and increased collaboration.
In the run-up to the summit, we want to keep you informed on the up-to-date news surrounding Machine Learning, it’s latest effects on DevOps methodologies, and why organizations should waste no time in exploring how Machine Learning can benefit their DevOps strategy.
“ML Ops encapsulates aspects of data engineering, software engineering, and data science to provide an end-to-end view of applying intelligence from data to a business use case. A majority of data science projects stay in the labs because integration with production environments is extremely complicated, manual and prone to error. The lack of sophisticated ML Ops therefore hinders any company or business to extract intelligence from the data they already have and apply them to their business processes and triggers disillusionment of data science and machine learning in general.”
“A typical automation process includes iterative life cycles in data engineering (preparation, cleaning, refining and transformation), data science (model development, training, testing, validation, and optimization) and deployment (further testing, deployment, experimentation, monitoring, performance engineering and operating). Each of these are very complex processes and have separate tools and systems that typically don’t integrate well, include lots of manual touch points and handoffs and sometimes don’t even interoperate. The first order problem is lack of visibility and transparency in the end-to-end process. A modern ML ops engineering platform will stitch together these disparate steps into a seamless workflow that will enable collaboration between everybody involved in solving the business problem.”
“Dell Technologies announced it is infusing machine learning algorithms across its portfolio of server and storage infrastructure to optimize the performance of each individual application using artificial intelligence (AI).”
“Ashley Gorakhpurwalla, president and general manager for server and infrastructure systems at Dell EMC, noted that despite advances in DevOps, provisioning resources in a local data center on demand is still too time-consuming. Many IT organizations have adopted converged and hyper-converged infrastructure (HCI) platforms to unify the management of compute, storage and networking. But machine learning algorithms combined with other emerging technologies should make it possible to make IT infrastructure resources on demand in a cloud-like manner. That increased level of automation should enable IT organizations to allocate more resources to developing applications than managing the infrastructure those applications run on.”
“A startup, 8base, still in beta, leverages open blockchain platforms to help programmers to collaborate on development and deployment of new applications. The company uses machine learning to auto-generate application code that is declaratively specified in a visual front-end tool. Developers collaborate around a common repository of code, data and models persisted to a blockchain. The environment is agnostic about the underlying blockchain environment, giving developers a choice of whether to run on top of public (aka “permissionless”) or private (aka “permissioned”) blockchains.
As 8base founder and Chief Executive Albert Santalo recently told SiliconANGLE co-CEO John Furrier on theCUBE at Blockchain Unbound 2018:
“Data science was hard to access, expensive to access, only a few people in the world really could do it, and then these layers of abstractions have facilitated a much wider group of people being able to do it. So that’s exactly what we’re doing. We’re at the same time bringing the development of software closer to where the requirements live because literally, the people defining the requirements can develop their own software. So what you’re going to see is a rollout of blockchain and non-blockchain software just accelerated and put into the hands of more people, which strikes at the heart of digital transformation. We’ve all heard about this theme digital transformation. [If] businesses don’t evolve and adopt blockchain, AI, all these other things, they have a threat of being out of business.”
Instant Extends AI Powered APM Making It The Only Solution To Holistically Monitor Kubernetes Health Along With The Applications Under Management
“Instana, the leader in APM solutions for monitoring dynamic containerized microservice applications, announced enhanced observability of Kubernetes systems, making it the first APM solution that can monitor and understand orchestrated applications, the orchestration system and the interdependencies between them”
“The new Kubernetes monitoring features extend Instana’s AI-powered APM solution to automatically discover, monitor and analyze Kubernetes labels, clusters and pods in addition to the orchestrated application, itself. Not only does this provide unmatched Kubernetes system visibility, it contributes additional information to Instana’s AI-assisted monitoring and troubleshooting algorithms giving users holistic observability and relational understanding between their orchestrators and application performance”
Does Machine Learning interest you as a DevOps specialist but you are still sceptical about how to actually implement it? To give you a quick taste of the type of information you can expect to gain at the Machine Learning for DevOps Summit …
According to Torsten Volk at TechTarget, “there are seven basic rules to implement ML and AI in real-life DevOps situations. Done right, ML/AI and DevOps can turn enterprises into digital attackers that release higher-quality software faster and at a lower cost.”
“To release on time and within budget, there is little to no room for error when it comes to the choice of the technology path. ML- and AI-driven solutions often promise much better end results. So should a DevOps team go slowly and safely, or jump all in?”
Has this sparked your interest in discovering more about the current Machine Learning for DevOps landscape? Register now to join us at the Machine Learning for DevOps Summit in Houston, 29 - 30 November 2018! Meet and network with people leading the Machine Learning in DevOps revolution and hear them speak about their successes as well as the lessons they have learnt from navigating this rapidly developing space.
Are you applying Machine Learning to your DevOps processes that you would like to share with the community? Tell us in the comments below or suggest a speaker for the summit here!
The Applied AI Summit is co-located with the Machine Learning for DevOps Summit, register now to access both summits, gain a wealth of knowledge and discover the latest developments in the worlds of AI, Machine Learning and DevOps! Take advantage of our Super Early Bird ticket prices until the 20th July - confirm your place here.