CodeIsGo.com

By making off-the-rack machine learning models accessible for anyone to use, cloud ML services like Amazon Personalize help make ML-driven customer experiences available to teams at any scale. By @mparticle. You no longer need in-house data science and machine learning experts to get the benefit of propensity scoring or product recommendations.

The article then describes:

• Key challenges with machine learning
• Collecting and supplying quality user data
• Making ML insights available and actionable
• The project: personalized product recommendations
• Collect data with a CDP
• Capture product interactions
• Create the AWS assets
• Create a Kinesis stream

… and more. Once we’ve set up the infrastructure to generate, continuously refine, and activate ML insights, the final piece of the puzzle is to figure out what works and what doesn’t. You will also get code and screen shots explaining the steps in the article. Well done!

The days of implicitly trusting connected devices that are behind the traditional enterprise firewalled network with its “hard” perimeter are no longer. By Jason Soroko.

Risk officers and security professionals should consider ALL connected traffic to be on a hostile network. This requires authentication at the user, device, and application level and therefore digital identities comprise the new perimeter. If every endpoint is its own edge, it is becoming increasingly challenging to secure thanks to the ever-expanding ecosystem of multi-cloud environments, BYOD devices, IoT, and unprecedented levels of remote work accelerated by COVID-19.

The article main points are:

• Zero Trust is a set of principles, not a check-the-box-activity
• Public Key Infrastructure (PKI) is foundational to Zero Trust
• Zero Trust requires governance, policy, and enforcement through a centralized place
• Migration to Zero Trust can be step-by-step
  • Secure servers and applications
  • Secure network access endpoints
  • Secure device endpoints
  • Secure email
  • Replace passwords for people with user certificates

Even with the help of automation and single-pane-of-glass management, migrating an entire organization to Zero Trust may seem daunting. Fortunately, organizations don’t have to implement certificates en masse, all at once. IT teams can ease the transition by implementing Zero Trust on a step-by-step basis to make the process as painless as possible. Nice one!

As you complete each project as a consultant, you start noticing patterns. No matter what the differences are between the various architectures, tech stacks and teams, you find yourself following the same steps. By Brendan Kamp.

This is especially true when we start talking about concepts like cloud migrations or Cloud Native transformations. One of the most underrated yet effective steps is simplicity, It might come as a shock, but often the most scalable and effective systems are also the simplest. When moving to the cloud you will often hear the phrase ‘lift and shift’. It is sold as the way to leverage ‘quick’ wins in the cloud.

The guide then give you information on:

• Embrace simplicity
• Harvest low-hanging fruit
• Stay away from buzzwords
• Throw away what’s not working
• Define your standards

Throw away what’s not working. This cannot be stressed enough. Working for a consultancy, more often than not you are put into teams that have already tried cloud migrations and failed. In the worst-case scenario, the people that created these migrations are no longer with the company, but the company has sunk X amount of cost into this endeavour and is not willing to forfeit that expense. Good read!

This article will focus on the two featured AWS weapons for microservices and cloud architecture: Amazon Elastic Container Service (Amazon ECS) and Amazon Elastic Kubernetes Service (Amazon EKS). By Serkan Özal.

Both services support various compute options and have rich integration points to work with the other AWS services. However, there are critical differences between these services in design and operations.

The main points mentioned in the article:

• Amazon Elastic Container Service
• Amazon Elastic Kubernetes Service
• Shared Roots
• Key Differences
  • Pricing
  • Simplicity
  • Portability
  • Community Support

ECS and EKS are both reliable and competent container management systems provided by AWS. Although we have discussed their shared roots and critical differences, choosing one depends on your team and project. While it’s a decision each organization needs to make for itself, there are a few guidelines that will help you to decide. Good read!

Taking the labor out of labor-intensive tasks is what Ansible is all about. Learn the basics here. By Pratheek Prabhakaran.

The life of a sysadmin involves installation, configuration, performing regular system upgrade and maintenance activities, provisioning, system monitoring, vulnerability mitigation, troubleshooting issues, and much more. Many sysadmin actions consist of step-by-step tasks performed methodically. So how can we make the life of a sysadmin easier?

Ansible is an open source automation engine. It is easy to deploy and use because you do not need any special coding skills to use Ansible for automation. It uses a playbook to describe automation jobs using Yet Another Markup Language (YAML).

The article is divided into:

• Automate to accelerate
• Why Ansible?
• How does Ansible work?
• Ansib-Lingo
• Use cases for sysadmin

This article gives you a brief introduction and overview of the concepts and terms associated with Ansible. You will get links to further reading. Good one!

In the wake of Covid-19, organizations are fundamentally rethinking their product and service portfolios, reinventing their supply chains, pursuing large-scale organizational restructuring and digital transformation, and rebuilding to correct systemic racism from the ground up. Traditional change management process won’t cut it. The author borrows from agile software development processes to reinvent the change management playbook. By Sarah Jensen Clayton.

The business world has arguably seen more disruption in the last nine months than in the last nine years, bringing new and urgent demand for change.

The main points:

• Declare your change vision
• Empower the people who are best positioned to drive change from the beginning
• Encourage self-organizing teams to supplement your efforts
• Use internal social channels and influencers to drive employee awareness and engagement
• Embrace a “test-and-learn” approach
• Shift from long-term to short-term accountability

Formal adoption of agile may or may not be right for your organization but now is the time to consider how to make change management work faster and harder. If we know anything for certain in this moment, it’s that more change is coming. Good read!

A comprehensive guide to handling imbalanced datasets. By Francis Adrian Viernes.

One of the mistakes I made as a rookie data scientist was placing heightened importance on the accuracy metric. Now, this is not to dismiss the importance of accuracy as a measure of machine learning (ML) performance. In some models, we aim to have high accuracy. After all, this metric is the one most understood by executive and business leaders.

For the purposes of our discussion, let’s refer to the classifier we have developed in the introduction as a ‘naive classifier’. A naive classifier (not the same as a Naive Bayes classifier) is called as such because it oversimplifies assumptions in producing or labeling an output.

The article then discusses the following:

• Use the proper metrics
• Set a new threshold: How do you Choose the Right Threshold?
• Collect More Data
• Augment Dataset and Undersampling
• Rethink the Features of the Model
• Methods (Ensemble Methods)

This is detailed article with example code and charts explaining the main points of the content. Imbalanced datasets are shining examples of a data science paradox. On the one hand, while it is very common for data scientists to deal with imbalanced classifiers (even maybe more than balanced ones), the purpose is usually to identify the uncommon class. Nice one!

Computed properties are part of a family of property types in Swift. Stored properties are the most common which save and return a stored value whereas computed ones are a bit different. By Antoine van der Lee.

A computed property, it’s all in the name, computes its property upon request. It can be a valuable addition to any of your types to add a new property value based on other conditions. This post explains how and when to use them, as well as the differences between a computed value and a method. A Computed Property provides a getter and an optional setter to indirectly access other properties and values. It can be used in several ways.

The article walks you through:

• What is a Computed Property?
• Using a Computed Property to disclose rich data
• Computed Properties inside an extension
• Overriding Computed Properties
• When should I use a Computed Property?
• Taking into account performance when defining properties
• Computed Properties vs Methods in Swift: When to use which?

Computed Properties are part of the basics in Swift. They can be used in many different ways and it’s important to know the different use-cases. Always take performance into account and try to prevent unneeded calculations if possible. Good read!

Duplicated code is a code smell that hides the potential of better design. How can we find it? Is it 100 % identical code token by token? Are methods getName() and getName() on 2 entities duplicated? By Tomas Votruba.

The article main points are:

• Why even care about duplicated code?
• What is not duplicated method?
• How can we spot the duplicated method with X-Rays?
• How to teach it phpStan?
• 3 steps to get rid of duplications in your code today

PHPStan changes an approach to duplicated code. It runs on php-parser and uses an abstract syntax tree. So why should we care? PHPStan gives human output, so we know what to do.

"Class methods ProductSorter::sortResults() and PictureSorter::sortResults() are identical

PHPStan does this hard work on each commit and tells us precisely the duplicated code. When we know where duplications are, we can do something about it. Our project code will be more consistent, logical, and each piece of code will fit into another. Good read!

Not all apps are the same - when we deploy them to our Kubernetes cluster, we have to take that into account. One classification of apps is between stateful & stateless. By Dimitris Kapanidis.

A stateless app is an application program that does not save client data generated in one session for use in the next session with that client. As an example a REST API backend service that performs requests on a database is stateless, though the database itself is stateful. One of the main benefit of stateless apps is that their instances can be scaled on multiple replicas without issues

The article then pays attention to:

• Stateless apps
• Start with Stateless
• Stateful apps
• Deploying an etcd cluster
• Testing High Availability

There are different patterns to accomplish high availability (HA) for stateful apps, autthor will use as example how etcd distributed key-value store works. Making a stateful app to work in Kubernetes is not difficult, but making it work so that it is resilient and in high-availability mode depends on the architecture of the app itself, etcd is a battle-tested example in Kubernetes, but other solutions such as relational databases are a bit more complicated. You will also get all the configuration files and commands for running the kubernetes. Easy!