InferenceIn a previous blog (The difference between statistics and data science), I discussed the significance of statistical inference. In this section, we expand on these ideas The goal of statistical inference is to make a statement about something that is not observed within a certain level of uncertainty. Inference is difficult because it is based on a sample i.e. the objective is to understand the population based on the sample. The population is a collection of objects that we want to study/test. For example, if you are studying quality of products from an assembly line for a given day, then the whole production for that day is the population. In the real world, it may be hard to test every product – hence we draw a sample from the population and infer the results based on the sample for the whole population. In this sense, the statistical model provides an abstract representation of the population and how the elements of the population relate to each other. Parameters are numbers that represent features or associations of the population. We estimate the value of the parameters from the data. A parameter represents a summary description of a fixed characteristic or measure of the target population. It represents the true value that would be obtained as if we had taken a census (instead of a sample). Examples of parameters include Mean (μ), Variance (σ²), Standard Deviation (σ), Proportion (π). These values are individually called a statistic. A Sampling Distribution is a probability distribution of a statistic obtained through a large number of samples drawn from the population. In sampling, the confidence interval provides a more continuous measure of un-certainty. The confidence interval proposes a range of plausible values for an unknown parameter (for example, the mean). In other words, the confidence interval represents a range of values we are fairly sure our true value lies in. For example, for a given sample group, the mean height is 175 cms and if the confidence interval is 95%, then it means, 95% of similar experiments will include the true mean, but 5% will not contain the sample. Image source and reference: An introduction to confidence intervalsHypothesis testingHaving understood sampling and inference, let us now explore hypothesis testing. Hypothesis testing enables us to make claims about the distribution of data or whether one set of results are different from another set of results. Hypothesis testing allows us to interpret or draw conclusions about the population using sample data. In a hypothesis test, we evaluate two mutually exclusive statements about a population to determine which statement is best supported by the sample data. The Null Hypothesis(H0) is a statement of no change and is assumed to be true unless evidence indicates otherwise. The Null hypothesis is the one we want to disprove. The Alternative Hypothesis: (H1 or Ha) is the opposite of the null hypothesis, represents the claim that is being testing. We are trying to collect evidence in favour of the alternative hypothesis. The Probability value (P-Value) represents the probability that the null hypothesis is true based on the current sample or one that is more extreme than the current sample. The Significance Level (α) defines a cut-off p-value for how strongly a sample contradicts the null hypothesis of the experiment. If P-Value < α, then there is sufficient evidence to reject the null hypothesis and accept the alternative hypothesis. If P-Value > α, we fail to reject the null hypothesis.Central limit theoremThe central limit theorem is at the heart of hypothesis testing. Given a sample where the statistics of the population is unknowable, we need a way to infer statistics across the population. For example, if we want to know the average weight of all the dogs in the world, it is not possible to weigh up each dog and compute the mean. So, we use the central limit theorem and the confidence interval which enables to infer the mean of the population within a certain margin.So, if we take multiple samples – say the first sample of 40 dogs and compute of the mean for that sample. Again, we take a next sample of say 50 dogs and do the same. We repeat the process by getting a large number of random samples which are independent of each other – then the ‘mean of the means’ of these samples will give the approximate mean of the whole population as per the central limit theorem. Also, the histogram of the means will represent the bell curve as per the central limit theorem. The central limit theorem is significant because this idea applies to an unknown distribution (ex: Binomial or even a completely random distribution) – which means techniques like hypothesis testing can apply to any distribution (not just the normal distribution) Image source: minitab.See More

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IntroductionDue to COVID19, we already have 1000 deaths in Italy.Two months ago, these folks, many of them elderly, would have been celebrating Christmas and looking forward to the next Christmas – knowing that northern Italy has one of the best healthcare systems in the worldBut apart from the human cost, the economic impact of COVID19 will also be severeIt’s still too early to think of ‘after the virus’ – but policy makers will start to think of new strategies soon.One thing is sure, the world has changed completely, and old ways of bulk consumption and cheap imports will not work.Azeem Azhar proposes in exponential view that “we have underinvested in the things that make us individually and collectively resilient to shocks” and also that “Firms will respond by distributing their manufacturing and supply base.”What will this re-alignment of supply chains look like?What will the post COVID19 world look like?AI and other technologies are already helping to fight COVID-19In this post, we explore scenarios beyond the virus i.e. how AI and next generation manufacturing technologies(FMS) can lead to more local manufacturing jobs if we invest in educating the workforce to work with AI The end of the (assembly) lineThe assembly line has been an innovation from the 1920s onwards (Henry Ford’s era).The success of the assembly line leads to a reluctance to change something that is not broken.The assembly line suited mass production and offshore manufacturing.But both these factors may have now changed.For reasons I mention below, the Assembly line could be replaced by a flexible manufacturing system (FMS) which has artificial intelligence at its core.When that happens, the impact will be profound – and positive – for local jobsA flexible manufacturing system (FMS) is a manufacturing system in which there is some amount of flexibility that allows the s to react in case of changes, whether predicted or unpredicted. This flexibility falls under two broad categories:Routing flexibility: the system's ability to be changed to produce new product types and the ability to change the order of operations executed on a part andMachine flexibility, which consists of the ability to use multiple machines to perform the same operation on a part, as well as the system's ability to absorb large-scale changes, such as in volume, capacity, or capability. Most FMS consist of three main systems.The work machines which are often automated CNC machinesconnected by a material handling system to optimize parts flow andthe central control computer which controls material movements and machine flow The main advantages of an FMS is its high flexibility in managing manufacturing resources like time and effort in order to manufacture a new product.The best application of an FMS is found in the production of small sets of products in contrast to mass production. FMS requires a higher skilled work force – but that is a problem which is easier to overcome. Three day a week job for manufacturing?Another surprising incentive is robots and AI.Let’s ask ourselves:How about a 3 day working week in factory and two days from home for manufacturing jobs?And what about the other two days?You could work from ‘home’ with AI/ robotics managing some of the production.This idea is not so far-fetched through the rise of Cobots – how humans and AI are working together in 1500 companies (HBR) There is increasing support for flexible manufacturing – BCG – will flexible cell manufacturing revolutionise car manufacturing MIT Technology Review asks “Are you prepared for what's next?” - As automation and hybrid work environments become a necessity to keep up with customer demands, the question isn't if these technologies will impact your business, but how. If you aren't preparing for the new world of work, you risk being left behind by the market. A change driven by necessity and valuesSo why now?A COVID 19 vaccine is at least 9 months away – to the end of the year.It will take longer to be fully available.The virus may mutate.So, travel will not be comfortable for the foreseeable future.This will impact current supply chains and even if they are restored, governments will seek to distribute their supply chains. Last week, a friend described the Corona virus (COVID 19) as the ‘Berlin wall of capitalism’.Old ways of cheapest imports will no longer work.Not just for companies but for people, policy makers and values – including workers rights. Capitalism is defined as “an economic and political system in which a country's trade and industry are controlled by private owners for profit, rather than by the state.”.In its purest form, capitalism is founded on ethics ex: The Protestant Ethic and the Spirit of Capitalism - a book which I have liked (from a secular sense) Consumerism on the other hand is a social and economic order that encourages an acquisition of goods and services in ever-increasing amounts. COVID 19 could mark the end of consumerism and bring us back to old-fashioned capitalism and in doing so, could change manufacturing to a local focus (assisted by AI,Robotics and Education) There are other factors at play – for example - Climate change – new rules could spell the end of throwaway culture. There is a wider undercurrent – for example the slow food movement in Switzerland and the rise of veganism These ideas are all values driven. We are thus not talking about local / non-local - but rather people will buy based on values and from areas where there is an alignment of values instead of the cheapest source ConclusionTo conclude, in this post I outline how AI and next generation manufacturing technologies(FMS) can lead to more local manufacturing jobs if we invest in educating the workforce to work with AI.Welcome comments.These views are personal and are not aligned to any organisation I am associated with. Image source; ford assembly line 1913 See More

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IntroductionKubernetes is being described as the next ‘Java’ i.e. it is fast becoming an endemic/ underlying platform for the whole industry just like the Java programming language.For the first time, we are seeing the entire ecosystem aligning around the single platform via the Cloud native foundation.Kubernetes is the underlying technology behind a term called ‘Cloud Native’The Cloud Native foundation defines the term ‘Cloud Native’ as: "Cloud native technologies empower organizations to build and run scalable applications in modern, dynamic environments such as public, private, and hybrid clouds. Containers, service meshes, microservices, immutable infrastructure, and declarative APIs exemplify this approach.These techniques enable loosely coupled systems that are resilient, manageable, and observable. Combined with robust automation, they allow engineers to make high-impact changes frequently and predictably with minimal toil.The Cloud Native Computing Foundation seeks to drive adoption of this paradigm by fostering and sustaining an ecosystem of open source, vendor-neutral projects. We democratize state-of-the-art patterns to make these innovations accessible for everyone."But this definition does not really convey the full significance of the term.Cloud native is a term used to describe container-based environments. Cloud-native technologies are used to develop applications built with services packaged in containers, deployed as microservices and managed on elastic infrastructure through agile DevOps processes and continuous delivery workflows.The article descries the 10 Key Attributes of Cloud-Native ApplicationsPackaged as lightweight containers:Developed with best-of-breed languages and frameworks:Designed as loosely coupled microservices:Centered around APIs for interaction and collaboration:Architected with a clean separation of stateless and stateful services:Isolated from server and operating system dependencies:Deployed on self-service, elastic, cloud infrastructure:Managed through agile DevOps processesAutomated capabilitiesDefined, policy-driven resource allocationBased on this background, we now explore the significance of Kubernetes and Cloud Native Applications in this articleBackgroundWe can understand Cloud Native Applications based on the evolution of application developmentTraditional deployment: involved running applications on physical serversVirtualized deployment: Virtualization allowed us to run multiple Virtual Machines (VMs) on a single physical server’s CPU.Container deployment: Containers are similar to VMs but are more lightweight because they use a platform like docker.So, in this evolution, Kubernetes can be seen as a platform for container orchestration which helps to run container-based applications resiliently. For example, if a container goes down, another container needs to start autonomously.Significance of Cloud Native Applications and KubernetesBut this background also does not explain the real significance of KubernetesKubernetes provides a flexible, loosely-coupled mechanism for service discovery. Because this mechanism sits above the Cloud – it potentially provides a cloud agnostic way of providing service delivery. Cloud agnosticity may or may not be an objective – but it is certainly an unintended consequence of Kubernetes – which also explains it’s popularity.So, developers in a cloud-native (kubernetes based) application engage with a layer which abstracts the underlying compute, storage and networking primitives from the infrastructure providers. ConclusionKubernetes is synonymous with ‘cloud-native’ and provides an abstraction layer above individual cloud providers. This provides container orchestration (at a technical level) and Cloud agnosticity at a business level. Cloud-native is a computing paradigm that has just got started and is here to stayReferenceshttps://medium.com/@lancengym/kubernetes-in-layman-terms-d9c307d2ef1dhttps://kubernetes.io/docs/concepts/overview/what-is-kubernetes/ https://en.wikipedia.org/wiki/Kuberneteshttps://thenewstack.io/kubernetes-an-overview/ https://blog.newrelic.com/engineering/what-is-kubernetes/ Source wikipediaSee More

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