In our final “Meet the Team” of 2024, we’d like to introduce you to Erik Schön, Managing Director at Erlang Solutions.

Erik shares his journey with Erlang, Elixir, and the BEAM ecosystem, from his work at Ericsson to joining Erlang Solutions in 2019. He also reflects on a key professional highlight in 2024 and looks ahead to his goals for 2025. Erik also reveals his festive traditions, including a Swedish-Japanese twist.

About Erik

So tell us about yourself and your role at Erlang Solutions .

Hello, I’m Erik! I’ve been a big fan of all things Erlang/Elixir/BEAM since the 90s, having seen many successful applications of it when working at Ericsson as an R&D manager for many years.

Since 2019, I’ve been part of the Erlang Solutions Nordic Fjällrävens (“Arctic Foxes”) team based in Stockholm, Sweden. I love helping our customers succeed by delivering faster, safer, and more efficient solutions.

What has been a professional highlight of yours in 2024?

The highlight of 2024 for me was our successful collaboration with BoardClic, a startup that helps its customers with digital board and C-suite level performance evaluations.

We started our collaboration with a comprehensive code-/architecture review of their Elixir codebase, using our 25 years of experience in delivering software for societal infrastructure, including all the do’s and don’ts for future-proof, secure, resilient, and scalable solutions.

Based on this, we boosted their development of new functionality for a strategically important customer—from idea to live, commercial operation. Two of our curious, competent collaborators, with 10+ years of practical, hands-on Elixir/Erlang/BEAM expertise, worked closely with BoardClic on-site to deliver on time and with quality.

What professional and personal achievements are you looking forward to achieving in 2025?

Professionally, I look forward to continued success with our customers. This includes strengthening our long-standing partnerships with TV4, Telia, Ericsson, and Cisco . I’m also excited about the start of new partnerships, both inside and outside the BEAM community where we will continue to deliver more team-based, full-stack, end-to-end solutions.

Personally, I look forward to continuing to talk about my trilogy of books – The Art of Change, The Art of Leadership and The Art of Strategy – in podcasts, meetups and conferences.

Do you have any festive traditions that you’re looking forward to this holiday season?

In Sweden, “ julbord “ (a buffet-style table of small dishes including different kinds of marinated fish like herring and salmon, meatballs, ham, porridge, etc)  is a very important tradition to look forward to. Since my wife is from Japan, we always try to spice things up a bit by including suitable dishes from the Japanese kitchen, like different kinds of sushi.

Final thoughts

As we wrap up our 2024 meet-the-team series, a big thank you to Erik and all the incredible team members we’ve highlighted this year. Their passion, expertise, and dedication continue to drive our success.

Stay tuned for more insights and profiles in the new year as we introduce even more of the talented people who make Erlang Solutions what it is! if you’d like to speak more with our team, please get in touch .

The post Meet the team: Erik Schön appeared first on Erlang Solutions .

Welcome to Advent of Code 2024!

Like every year, I start the challenge with the best attitude and love of being an Elixir programmer. Although I know that at some point, I will go to the “what is this? I hate it” phase, unlike other years, this time, I am committed to finishing Advent of Code and, more importantly, sharing it with you.

I hope you enjoy this series of December posts, where we will discuss the approach for each exercise. But remember that it is not the only one, and the idea of ​​this initiative is to have a great time and share knowledge, so don’t forget to post your solutions and comments and tag us to continue the conversation.

Let’s go for it!

Day 1: Historian Hysteria

Before starting any exercise, I suggest spending some time defining the structure that best fits the problem’s needs. If the structure is adequate, it will be easy to reuse it for the second part without further complications.

In this case, the exercise itself describes lists as the input, so we can skip that step and instead consider which functions of the Enum or List modules can be helpful.

We have this example input:

3 4

4 3

2 5

1 3

3 9

3   3

The goal is to transform it into two separate lists and apply sorting, comparison, etc.

List 1: [3, 4, 2, 1, 3, 3 ]

List 2: [ 4, 3, 5, 3, 9, 3 ]

Let’s define a function that reads a file with the input. Each line will initially be represented by a string, so use String . split to separate it at each line break.

 def get_input(path) do
   path
   |> File.read!()
   |> String.split("\n", trim: true)
 end


["3   4", "4   3", "2   5", "1   3", "3   9", "3   3"]

We will still have each row represented by a string, but we can now modify this using the functions in the Enum module. Notice that the whitespace between characters is constant, and the pattern is that the first element should go into list one and the second element into list two. Use Enum.reduce to map the elements to the corresponding list and get the following output:

%{
 first_list: [3, 3, 1, 2, 4, 3],
 second_list: [3, 9, 3, 5, 3, 4]
}

I’m using a map so that we can identify the lists and everything is clear. The function that creates them is as follows:

 @doc """
 This function takes a list where the elements are strings with two
 components separated by whitespace.


 Example: "3   4"


 It assigns the first element to list one and the second to list two,
 assuming both are numbers.
 """
 def define_separated_lists(input) do
   Enum.reduce(input, %{first_list: [], second_list: []}, fn row, map_with_lists ->
     [elem_first_list, elem_second_list] = String.split(row, "   ")


     %{
       first_list: [String.to_integer(elem_first_list) | map_with_lists.first_list],
       second_list: [String.to_integer(elem_second_list) | map_with_lists.second_list]
     }
   end)
 end

Once we have this format, we can move on to the first part of the exercise.

Part 1

Use Enum.sort to sort the lists ascendingly and pass them to the Enum.zip_with function that will calculate the distance between the elements of both. Note that we are using abs to avoid negative values, and finally, Enum.reduce to sum all the distances.

first_sorted_list = Enum.sort(first_list)
   second_sorted_list = Enum.sort(second_list)


   first_sorted_list
   |> Enum.zip_with(second_sorted_list, fn x, y -> abs(x-y) end)
   |> Enum.reduce(0, fn distance, acc -> distance + acc end)

Part 2

For the second part, you don’t need to sort the lists; use Enum. frequencies and Enum.reduce to get the multiplication of the elements.

 frequencies_second_list = Enum.frequencies(second_list)


   Enum.reduce(first_list, 0, fn elem, acc ->
     elem * Map.get(frequencies_second_list, elem, 0) + acc
   end)

That’s it. As you can see, once we have a good structure, the corresponding module, in this case, Enum, makes the operations more straightforward, so it’s worth spending some time defining which input will make our life easier.

You can see the full version of the exercise here .

The post Advent of Code 2024 appeared first on Erlang Solutions .

MongooseIM is a scalable, efficient, high-performance instant messaging server using the proven, open, and extensible XMPP protocol. With each new version, we introduce new features and improvements. For example, version 6.2.0 introduced our new CETS in-memory storage, making setup and autoscaling in cloud environments easier than before (see the blog post for details). The latest release 6.3.0 is no exception. The main highlight is the complete instrumentation rework, allowing seamless integration with modern monitoring solutions like Prometheus.

Additionally, we have added CockroachDB to the list of supported databases, so you can now let this highly scalable database grow with your applications while avoiding being locked into your cloud provider.

Observability and instrumentation

In software engineering, observability is the ability to gather data from a running system to figure out what is going inside: is it working as expected? Does it have any issues? How much load is it handling, and could it do more? There are many ways to improve the observability of a system, and one of the most important is instrumentation . Just like adding extra measuring equipment to a physical system, this means adding additional code to the software. It allows the system administrator to observe the internal state of the system. This comes with a price. There is more work for the developers, increased complexity, and potential performance degradation caused by the collection and processing of additional data.

However, the benefits usually outweigh the costs, and the ability to inspect the system is often a critical requirement. It is also worth noting that the metrics and events gathered by instrumentation can be used for further automation, e.g. for autoscaling or sending alarms to the administrator.

Instrumentation in MongooseIM

Even before our latest release of MongooseIM, there have been multiple means to observe its behaviour:

Metrics provide numerical values of measured system properties. The values change over time, and the metric can present current value, sum from a sliding window, or a statistic (histogram) of values from a given time period. Prior to version 6.3, MongooseIM used to store such metrics with the help of the exometer library. To view the metrics, one had to configure an Exometer exporter, which would periodically send the metrics to an external service using the Graphite protocol. Because of the protocol, the metrics would be exported to Graphite or InfluxDB version 1 . One could also query a limited subset of metrics using our GraphQL API (or the legacy REST API) or with the command line interface. Alternatively, metrics could be retrieved from the Erlang shell of a running MongooseIM node.

Logs are another type of instrumentation present in the code. They inform about events occurring in the system and since version 4, they are events with extensible map-like structure and can be formatted e.g. as plain text or JSON. Subsequently, they can be shown in the console or stored in files. You can also set up a log management system like the Elastic (ELK) Stack or Splunk – see the documentation for more details.

The diagram below shows how these two types of instrumentation can work together:

The first observation is that the instrumented code needs to separately call the log and metric API. Updating a metric and logging an event requires two distinct function calls. Moreover, if there are multiple metrics (e.g. execution time and total number of calls), there would be multiple function calls required. There is potential for inconsistency between metrics, or between metrics and logs, because an error could happen between the function calls. The main issue of this solution is however the hardcoding of Exometer as the metric library and the limitation of the Graphite protocol used to push the metrics to external services.

Instrumentation rework in MongooseIM 6.3

The lack of support for the modern and widespread Prometheus protocol was one of the main reasons for the complete rework of instrumentation in version 6.3. Let’s see the updated diagram of MongooseIM instrumentation:

The most noticeable difference is that in the instrumented code, there is just one event emitted. Such an event is identified by its name and a key-value map of labels and contains measurements (with optional metadata) organised in a key-value map. Each event has to be registered before its instances are emitted with particular measurements. The point of this preliminary step is not only to ensure that all events are handled but also to provide additional information about the event, e.g. the measurement keys that will be used to update metrics. Emitted events are then handled by configurable handlers . Currently, there are three such handlers. Exometer and Logger work similarly as before, but there is a new Prometheus handler as well, which stores the metrics internally in a format compatible with Prometheus and exposes them over an HTTP API. This means that any external service can now scrape the metrics using the Prometheus protocol. The primary case would be to use Prometheus for metrics collection, and a graphical tool like Grafana for display. If you however prefer InfluxDB version 2, you can easily configure a scraper , which would periodically put new data into InfluxDB.

As you can see in the diagram, logs can be also emitted directly, bypassing the instrumentation API. This is the case for multiple logs in the system, because often there is no need for any metrics, and a log message is enough. In the future though, we might decide to fully replace logs with instrumentation events, because they are more extensible.

Apart from supporting the Prometheus protocol, additional benefits of the new solution include easier configuration, extensibility, and the ability to add more handlers in the future. You can also have multiple handlers enabled simultaneously, allowing you to gradually change your metric backend from Exometer to Prometheus. Conversely, you can also disable all instrumentation, which was not possible prior to version 6.3. Although it might make little sense at first glance, because it can render the system a black box, it can be useful to gain extra performance in some cases, e.g. if the external metrics like CPU usage are enough, in case of an isolated embedded system, or if resources are very limited.

The table below compares the legacy metrics solution with the new instrumentation framework:

There are about 140 events in total, and some of them have multiple dimensions. You can find an overview in the documentation . In terms of dashboards for tools like Grafana, we believe that each use case of MongooseIM deserves its own. If you are interested in getting one tailored to your needs, don’t hesitate to contact us .

Using the instrumentation

Let’s see the new instrumentation in action now. Starting with configuration, let’s examine the new additions to the default configuration file :

[[listen.http]]
  port = 9090
  transport.num_acceptors = 10

  [[listen.http.handlers.mongoose_prometheus_handler]]
    host = "_"
    path = "/metrics"

(...)

[instrumentation.prometheus]

[instrumentation.log]

The first section, [[listen.http]] , specifies the Prometheus HTTP endpoint. The following [instrumentation.*] sections enable the Prometheus and Log handlers with the default settings – in general, instrumentation events are logged on the DEBUG level, but you can change it. This configuration is all you need to see the metrics at http://localhost:9091/metrics when you start MongooseIM.

As a second example, let’s say that you want only the Graphite protocol integration. In this case, you might configure MongooseIM to use only the Exometer handler, which would push the metrics prefixed with mim to the influxdb1 host every 60 seconds:

[[instrumentation.exometer.report.graphite]]
  interval = 60_000
  prefix = "mim"
  host = "influxdb1"

There are more options possible, and you can find them in the documentation .

Tracing – ad-hoc instrumentation

There is one more type of observability available in Erlang systems, which is tracing . It enables a user to have a more in-depth look into the Erlang processes, including the functions being called and the internal messages being exchanged. It is meant to be used by Erlang developers, and should not be used in production environments because of the impact it can have on a running system. It is good to know, however, because it could be helpful to diagnose unusual issues. To make tracing more user-friendly, MongooseIM now includes erlang_doctor with some MongooseIM-specific utilities (see the tr_util module). This tool provides low-level ad-hoc instrumentation, allowing you to instrument functions in a running system, and gather the resulting data in an in-memory table, which can be then queried, processed, and – if needed – exported to a file. Think of it as a backup solution, which could help you diagnose hidden issues, should you ever experience one.

CockroachDB – a database that scales with MongooseIM

MongooseIM works best when paired with a relational database like PostgreSQL or MySQL, enabling easy cluster node discovery with CETS and persistent storage for users’ accounts, archived messages and other kinds of data. Although such databases are not horizontally scalable out of the box, you can use managed solutions like Amazon Aurora , AlloyDB or Azure Cosmos DB for PostgreSQL . The downsides are the possible vendor lock-in and the fact that you cannot host and manage the DB yourself. With version 6.3 however, the possibilities are extended to CockroachDB . This PostgreSQL-compatible distributed database can be used either as a provider-independent cloud-based solution or as an internally hosted cluster. You can instantly set it up in your local environment and take advantage of the horizontal scalability of both MongooseIM and CockroachDB. If you want to learn how to deploy both MongooseIM and CockroachDB in Kubernetes, see the documentation for CockroachDB and the Helm chart for MongooseIM, together with our recent blog post about setting up an auto-scalable cluster. If you are interested in having an auto-scalable solution deployed for you, please consider our MongooseIM Autoscaler .

Summary

MongooseIM 6.3.0 opens new possibilities for observability – the Prometheus protocol is supported instantly with a new reworked instrumentation layer underneath, guaranteeing ease of future extensions. Regarding database integration, you can now use CockroachDB to store all your persistent data. Apart from these changes, the latest version introduces a multitude of improvements and updates – see the release notes for more information. As the next step, we recommend visiting our product page to see the possible options of support and the services we offer. You can also try the server out at trymongoose.im . In any case, should you have any further questions, feel free to contact us .

The post MongooseIM 6.3: Prometheus, CockroachDB and more appeared first on Erlang Solutions .

This blog post will guide you through the process of setting up an ejabberd Community Server using Docker and Docker Compose , and will also introduce Watchtower for automatic updates. This approach ensures that your configuration remains secure and up to date.

Furthermore, we will examine the potential risks associated with automatic updates and suggest Diun as an alternative tool for notification-based updates.

1. Prerequisites

Please ensure that Docker and Docker Compose are installed on your system.
It would be beneficial to have a basic understanding of Docker concepts, including containers, volumes, and bind-mounts.

2. Set up ejabberd in a docker container

Let’s first create a minimal Docker Compose configuration to start an ejabberd instance.

2.1: Prepare the directories

For this setup, we will create a directory structure to store the configuration, database, and logs. This will assist in maintaining an organised setup, facilitating data management and backup.

mkdir ejabberd-setup && cd ejabberd-setup
touch docker-compose.yml
mkdir conf
touch conf/ejabberd.yml
mkdir database
mkdir logs

This should give you the following structure:

ejabberd-setup/
├── conf
│   └── ejabberd.yml
├── database
├── docker-compose.yml
└── logs

To verify the structure, use the tree command. It is a very useful tool which we use on a daily basis.

Set permissions

Since we&aposll be using bind mounts in this example, it’s important to ensure that specific directories (like database and logs) have the correct permissions for the ejabberd user inside the container (UID 9000 , GID 9000 ).

Customize or skip depending on your needs:

sudo chown -R 9000:9000 database
sudo chown -R 9000:9000 logs

Based on this Issue .

2.2: The docker-compose.yml file

Now, create a docker-compose.yml file inside, containing:

services:
  ejabberd:
    image: ejabberd/ecs:latest
    container_name: ejabberd
    ports:
      - "5222:5222"  # XMPP Client
      - "5280:5280"  # Web Admin Interface, optional
    volumes:
      - ./database:/home/ejabberd/database
      - ./ejabberd.yml:/home/ejabberd/conf/ejabberd.yml
      - ./logs:/home/ejabberd/logs
    restart: unless-stopped

2.3: The ejabberd.yml file

A basic configuration file for ejabberd will be required. we will name it conf/ejabberd.yml .

loglevel: 4
hosts:
- "localhost"

acl:
  admin:
    user:
      - "admin@localhost"

access_rules:
  local:
    allow: all

listen
  -
    port: 5222
    module: ejabberd_c2s

  -
    port: 5280                       # optional
    module: ejabberd_http            # optional
    request_handlers:                # optional
      "/admin": ejabberd_web_admin   # optional

Did you know? Since 23.10 , ejabberd now offers users the option to create or update the relevant MySQL, PostgreSQL or SQLite tables automatically with each update. You can read more about it here .

3: Starting ejabberd

Finally, we&aposre set: you can run the following command to start your stack: docker-compose up -d

Your ejabberd instance should now running in a Docker container! Good job! 🎉

From there, customize ejabberd to your liking! Naturally, in this example we&aposre going to keep ejabberd in its barebones configuration, but we recommend that you configure it as you wish at this stage, to suit your needs (Domains, SSL, favorite modules, chosen database, admin accounts, etc.)

Example: You could register your admin account at this stage

To use the admin interface, you need to create an admin account. You can do so by running the following command:

$ docker exec -it ejabberd bin/ejabberdctl register admin localhost very_secret_password
> User admin@localhost successfully registered

Once this step is complete, you will then be able to access the web admin interface at http://localhost:5280/admin .

4. Set up automatic updates

Finally, we come to the most interesting part: how do I keep my containers up to date?

To keep your ejabberd instance up-to-date, you can use Watchtower , a Docker container that automatically updates other containers when new versions are available.

Warning: Auto-updates are undoubtedly convenient, but they can occasionally cause issues if an update includes breaking changes. Always test updates in a staging environment and back up your data before enabling auto-updates. Further information can be found at the end of this post.

If greater control over updates is required (for example, for mission-critical production servers or clusters), we recommend using Diun , which can notify you of available updates and allow you to decide when to apply them.

4.1: Add Watchtower to your docker-compose.yml

To include Watchtower , add it as a service in docker-compose.yml :

services:
  ejabberd:
    image: ejabberd/ecs:latest
    container_name: ejabberd
    ports:
      - "5222:5222"  # XMPP Client
      - "5280:5280"  # Web Admin Interface, optional
    volumes:
      - ./database:/home/ejabberd/database
      - ./ejabberd.yml:/home/ejabberd/conf/ejabberd.yml
      - ./logs:/home/ejabberd/logs
    restart: unless-stopped

  watchtower:
    image: containrrr/watchtower
    container_name: watchtower
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
    environment:
      - WATCHTOWER_POLL_INTERVAL=3600 # Sets how often Watchtower checks for updates (in seconds).
      - WATCHTOWER_CLEANUP=true # Ensures old images are cleaned up after updating.
    restart: unless-stopped

Watchtower offers a wide range of additional features, including the ability to set up notifications, exclude specific containers, and more. For further information, please refer to the Watchtower Docs .

Once the docker-compose.yml has been updated, please bring it up using the following command: docker-compose up -d

And.... here you go, you&aposre all set!

5. Best Practices & closing words

Now Watchtower will now perform periodic checks for updates to your ejabberd container and apply them automatically.

Well to be fair, by default if other containers are running on the same server, Watchtower will also update them. This behaviour can be controlled with the help of environment variables (see Container Selection ), which will assist in excluding containers from updates.

One important thing to understand is that Watchtower will only update containers tagged with the :latest tag.

In an environment with numerous Docker containers, using the latest tag streamlines the process of automatic updates. However, it may introduce unanticipated changes with each new, disruptive update. Ideally, we recommend always setting a speficic version like ejabberd/ecs:24.10 and deciding how/when to update it manually (especially if you&aposre into infra-as-code ).

However, we recognise that some users may prefer the convenience of automatic updates, personnally that&aposs what I do my homelab but I&aposm not scared to dig in if stuff breaks.

tl;dr: For a small community server/homelab/personnal instance, Watchtower will help keep things up to date with minimal effort. However, for bigger production environments, it is advisable to tag specific versions to ensure greater control and resilience and update them manually.

With this setup, you now have a fully functioning XMPP server using ejabberd, with automatic updates. You can now start building your chat applications or integrate it with your existing services! 🚀

In the first part of this blog post , I explained how the Matrix protocol works, contrasted its design philosophy with XMPP, and discussed why these differences lead to performance costs in Matrix. Matrix processes each conversation as a graph of events, merged in real-time ^[1] .

Merge operations can be costly in Matrix for large rooms, affecting both database storage and load and disk usage when memory is exhausted, reaching swap level .

That said, there is still room for improvement in the protocol. We have designed and tested slight changes that could make Matrix much more efficient for large rooms.

A Proposal to Simplify and Speed Up Merge Operations

Here is the rationale behind a proposal we have made to simplify and speed up merge operations:

State resolution v2 uses certain graph algorithms, which can result in at least linear processing time for the number of state events in a room’s DAG, creating a significant load on servers.

The goal of this issue is to discuss and develop changes to state resolution to achieve O(n log ⁡ n) total processing time when handling a room with n state events (i.e., O(log ⁡ n) on average) in realistic scenarios, while maintaining a good user experience.

The approach described below is closer to state resolution v1 but seeks to address state resets in a different way.

For more detail, you can read our proposal on the Matrix spec tracker: Make state resolution faster .

In simpler terms, we propose adding a version associated with each event_id to simplify conflict management and introduce a heuristic that skips traversal of large parts of the graph.

Impact of the Proposal

From our initial assessment, in a very large room — such as one with 100,000 members — our approach could improve processing performance by 100x to 1000x, as the current processing cost scales with the number of users in the room. This improvement would enable smoother conversations, reduced lag, and more responsive interactions for end-users, while also reducing server infrastructure load and resource usage.

While our primary goal is to improve performance in very large rooms, these changes benefit all users by reducing overall server load and improving processing times across various room sizes.

We plan to implement this improvement in our own code to evaluate its real-world effectiveness while the Matrix team considers its potential value for the reference protocol.

1. For those who remember, a conversation in Matrix is similar to the collaborative editing protocol built on top of XMPP for the Google Wave platform.

The Ignite Realtime community is happy to be able to announce the immediate availability of version 4.9.1 of Openfire , its cross-platform real-time collaboration server based on the XMPP protocol!

4.9.1 is a bugfix and maintenance release. Among its most important fixes is one for a memory leak that affected all recent versions of Openfire (but was likely noticeable only on those servers that see high volume of users logging in and out). The complete list of changes that have gone into this release can be seen in the change log .

Please give this version a try! You can download installers of Openfire here . Our documentation contains an upgrade guide that helps you update from an older version.

The integrity of these artifacts can be checked with the following sha256sum values:

8c489503f24e35003e2930873037950a4a08bc276be1338b6a0928db0f0eb37d  openfire-4.9.1-1.noarch.rpm
1e80a119c4e1d0b57d79aa83cbdbccf138a1dc8a4086ac10ae851dec4f78742d  openfire_4.9.1_all.deb
69a946dacd5e4f515aa4d935c05978b5a60279119379bcfe0df477023e7a6f05  openfire_4_9_1.dmg
c4d7b15ab6814086ce5e8a1d6b243a442b8743a21282a1a4c5b7d615f9e52638  openfire_4_9_1.exe
d9f0dd50600ee726802bba8bc8415bf9f0f427be54933e6c987cef7cca012bb4  openfire_4_9_1.tar.gz
de45aaf1ad01235f2b812db5127af7d3dc4bc63984a9e4852f1f3d5332df7659  openfire_4_9_1_x64.exe
89b61cbdab265981fad4ab4562066222a2c3a9a68f83b6597ab2cb5609b2b1d7  openfire_4_9_1.zip

We would love to hear from you! If you have any questions, please stop by our community forum or our live groupchat . We are always looking for volunteers interested in helping out with Openfire development!

For other release announcements and news follow us on Mastodon or X

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Adopting machine learning for business is necessary for companies that want to sharpen their competitive industries. With the global market for machine learning projected to reach an impressive $210 billion by 2030 , businesses are keen to seek active solutions that streamline processes and improve customer interactions.

While organisations may already employ some form of data analysis, traditional methods can need more sophistication to address the complexities of today’s market. Businesses that consider optimising machines unlock valuable data insights, make accurate predictions and deliver personalised experiences that truly resonate with customers, ultimately driving growth and efficiency.

What is Machine Learning?

Machine learning (ML) is a subset of artificial intelligence (AI). It uses machine learning algorithms , designed to learn from data, identify patterns, and make predictions or decisions, without explicit programming. By analysing patterns in the data, a machine learning algorithm identifies key features that define a particular data point, allowing it to apply this knowledge to new, unseen information.

Fundamentally data-driven, machine learning relies on vast information to learn, adapt, and improve over time. Its predictive capabilities allow models to forecast future outcomes based on the patterns they uncover. These models are generalisable, so they can apply insights from existing data to make decisions or predictions in unfamiliar situations.

You can read more about machine learning and AI in our previous post .

Approaches to Machine Learning

Machine learning for business typically involves two key approaches: supervised and unsupervised learning , each suited to different types of problems. Below, we explain each approach and provide examples of machine learning use cases where these techniques are applied effectively.

• Supervised Machine Learning: This approach demands labelled data, where the input is matched with the correct output. The algorithms learn to map inputs to outputs based on this training set, honing their accuracy over time.
• Unsupervised Machine Learning: In contrast, unsupervised learning tackles unlabelled data, compelling the algorithm to uncover patterns and structures independently. This method can involve tasks like clustering and dimensionality reduction. While unsupervised techniques are powerful, interpreting their results can be tricky, leading to challenges in assessing whether the model is truly on the right track.

Example of Supervised vs unsupervised learning

Supervised learning uses historical data to make predictions, helping businesses optimise performance based on past outcomes. For example, a retailer might use supervised learning to predict customer churn . By feeding the algorithm data such as customer purchase history and engagement metrics, it learns to identify patterns that indicate a high risk of churn, allowing the business to implement proactive retention strategies.

Unsupervised learning , on the other hand, uncovers hidden patterns within data. It is particularly useful for discovering new customer segments without prior labels. For instance, an e-commerce platform might use unsupervised learning to group customers by their browsing habits, discovering niche audiences that were previously overlooked.

The Impact of Machine Learning on Business

A recent survey by McKinsey revealed that 56% of organisations surveyed are using machine learning in at least one business function to optimise their operations. This growing trend shows how machine learning for business is becoming integral to staying competitive.

The AI market as a whole is also on an impressive growth trajectory, projected to reach USD 407.0 billion by 2027 .

AI Global Market Forecast to 2030

We’re expected to see an astounding compound growth rate (CAGR) of 35.7% by 2030, proving that business analytics is no longer just a trend; it’s moving into a core component of modern enterprises.

Machine Learning for Business Use Cases

Machine learning can be used in numerous ways across industries to enhance workflows. From image recognition to fraud detection , businesses are actively using AI to streamline operations.

Image Recognition

Image recognition, or image classification is a powerful machine learning technique used to identify and classify objects or features in digital images.

Artificial intelligence (AI) and machine learning (ML) are revolutionising image recognition systems by uncovering hidden patterns in images that may not be visible to the human eye. This technology allows these systems to make independent and informed decisions, significantly reducing the reliance on human input and feedback.

As a result, visual data streams can be processed automatically at an ever-increasing scale, streamlining operations and enhancing efficiency. By harnessing the power of AI, businesses can leverage these insights to improve their decision-making processes and gain a competitive edge in their respective markets.

It plays a crucial role in tasks like pattern recognition, face detection, and facial recognition, making it indispensable in security and social media sectors.

Fraud Detection

With financial institutions handling millions of transactions daily, distinguishing between legitimate and fraudulent activity can be a challenge. As online banking and cashless payments grow, so too has the volume of fraud. A 2023 report from TransUnion revealed a 122% increase in digital fraud attempts in the US between 2019 and 2022.

Machine learning helps businesses by flagging suspicious transactions in real-time, with companies like Mastercard using AI to predict and prevent fraud before it occurs, protecting consumers from potential theft.

Speech Recognition

Voice commands have become a common feature in smart devices, from setting timers to searching for shows.

Thanks to machine learning, devices like Google Nest speakers and Amazon Blink security systems can recognise and act on voice inputs, making hands-free operation more convenient for users in everyday situations.

Improved Healthcare

Machine learning in healthcare has led to major improvements in patient care and medical discoveries. By analysing vast amounts of healthcare data, machine learning enhances the accuracy of diagnoses, optimises treatments, and accelerates research outcomes.

For instance, AI systems are already employed in radiology to detect diseases in medical images, such as identifying cancerous growths. Additionally, machine learning is playing a crucial role in genomic research by uncovering patterns linked to genetic disorders and potential therapies. These advancements are paving the way for improved diagnostics and faster medical research, offering tremendous potential for the future of healthcare.

Key applications of machine learning in healthcare include:

• Developing predictive modelling
• Improving diagnostic accuracy
• Personalising patient care
• Automating clinical workflows
• Enhancing patient interaction

Machine learning in healthcare utilises algorithms and statistical models to analyse large medical datasets, facilitating better decision-making and personalised care. As a subset of AI, machine learning identifies patterns, makes predictions, and continuously improves by learning from data. Different types of learning, including supervised and unsupervised learning, find applications in disease classification and personalised treatment recommendations.

Chatbots

Many businesses rely on customer support to maintain satisfaction. However, staffing trained specialists can be expensive and inefficient. AI-powered chatbots, equipped with natural language processing (NLP), assist by handling basic customer queries. This frees up human agents to focus on more complicated issues. Companies can provide more efficient and effective support without overburdening their teams.

Each of these applications offers businesses the chance to streamline operations and improve customer experiences.

Machine Learning Case Studies

Machine learning for business is transforming industries by enabling companies to enhance their operations, improve customer experiences, and drive innovation.

Here are a few machine learning case studies showing how leading organisations have integrated machine learning into their business strategies.

PayPal

PayPal, a worldwide payment platform, faced huge challenges in identifying and preventing fraudulent transactions.

To tackle this issue, the company implemented machine learning algorithms designed for fraud detection . These algorithms analyse various aspects of each transaction, including the transaction location, the device used, and the user’s historical behaviour. This approach has significantly enhanced PayPal’s ability to protect users and maintain the integrity of its payment platform.

YouTube

YouTube has long employed machine learning to optimise its operations, particularly through its recommendation algorithms . By analysing vast amounts of historical data, YouTube suggests videos to its viewers based on their preferences. Currently, the platform processes over 80 billion data points for each user, requiring large-scale neural networks that have been in use since 2008 to effectively manage this immense dataset.

Dell

Recognising the importance of data in marketing, Dell’s marketing team sought a data-driven solution to enhance response rates and understand the effectiveness of various words and phrases. Dell partnered with Persado, a firm that leverages AI to create compelling marketing content. This collab led to an overhaul of Dell’s email marketing strategy, resulting in a 22% average increase in page visits and a 50% boost in click-through rates (CTR). Dell now utilises machine learning methods to refine its marketing strategies across emails, banners, direct mail, Facebook ads, and radio content.

Tesla

Tesla employs machine learning to enhance the performance and features of its electric vehicles. A key application is its Autopilot system , which combines cameras, sensors, and machine learning algorithms to provide advanced driver assistance features such as lane centring, adaptive cruise control, and automatic emergency braking.

The Autopilot system uses deep neural networks to process vast amounts of real-world driving data, enabling it to predict driving behaviour and identify potential hazards. Additionally, Tesla leverages machine learning in its battery management systems to optimise battery performance and longevity by predicting behaviour under various conditions.

Netflix

Netflix is a leader in personalised content recommendations. It uses machine learning to analyse user viewing habits and suggest shows and movies tailored to individual preferences. This feature has proven essential for improving customer satisfaction and increasing subscription renewals. To develop this system, Netflix utilises viewing data—including viewing durations, metadata, release dates, timestamps etc. Netflix then employs collaborative filtering, matrix factorisation, and deep learning techniques to accurately predict user preferences.

Benefits of Machine Learning in Business

If you’re still contemplating the value of machine learning for your business, consider the following key benefits:

As organisations increasingly adopt machine learning, they position themselves to not only meet current demands but poise them for future innovation.

Elixir and Erlang in Machine Learning

As organisations explore machine learning tools, many are turning to Erlang and Elixir programming languages to develop customised solutions that cater to their needs. Erlang’s fault tolerance and scalability make it ideal for AI applications, as described in our blog on adopting AI and machine learning for business . Additionally, Elixir’s concurrency features and simplicity enable businesses to build high-performance AI applications.

Learn more about how to build a machine-learning project in Elixir here .

As organisations become more familiar with AI and machine learning tools, many are turning to Erlang and Elixir programming languages to develop customised solutions that cater to their needs.

Elixir, built on the Erlang virtual machine (BEAM), delivers top concurrency and low latency. Designed for real-time, distributed systems, Erlang prioritises fault tolerance and scalability, and Elixir builds on this foundation with a high-level, functional programming approach. By using pure functions and immutable data, Elixir reduces complexity and minimises unexpected behaviours in code. It excels at handling multiple tasks simultaneously, making it ideal for AI applications that need to process large amounts of data without compromising performance.

Elixir’s simplicity in problem-solving also aligns perfectly with AI development, where reliable and straightforward algorithms are essential for machine learning. Furthermore, its distribution features make deploying AI applications across multiple machines easier, meeting the high computational demands of AI systems.

With a rich ecosystem of libraries and tools, Elixir streamlines development, so AI applications are scalable, efficient, and reliable. As AI and machine learning become increasingly vital to business success, creating high-performing solutions will become a key competitive advantage.

Final Thoughts

Embracing machine learning for business is no longer optional for companies that want to remain competitive. Machine learning tools empower businesses to make faster, data-driven decisions, streamline operations, and offer personalised customer experiences. Contact the Erlang Solutions team today if you’d like to discuss building AI systems using Elixir and Erlang or for more insights into implementing machine learning solutions,

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