When we think about artificial intelligence and machine learning, languages like Python, R, and Julia typically come to mind. However, there’s a powerful player that deserves attention in the AI landscape: Elixir. While Elixir might not be the first choice for training neural networks, it excels at building scalable, fault-tolerant AI systems that can handle real-world production workloads.
In this post, we’ll explore why Elixir is an excellent choice for AI applications, how to integrate machine learning models into Elixir systems, and real-world use cases where Elixir shines in the AI space.
Elixir brings unique strengths to AI and machine learning applications that address many challenges faced in production environments:
Elixir, built on the Erlang VM (BEAM), provides lightweight processes that make concurrent processing trivial. When dealing with AI applications, you often need to:
Elixir handles these scenarios effortlessly. Each request can run in its own process without the complexity of threading or async/await patterns.
# Process multiple predictions concurrently
predictions = images
|> Task.async_stream(&predict_image/1, max_concurrency: 100)
|> Enum.map(fn {:ok, result} -> result end)Machine learning models can fail for various reasons: invalid input, resource exhaustion, or model errors. Elixir’s “let it crash” philosophy and supervision trees ensure that failures are isolated and don’t bring down your entire system.
defmodule AISystem.Supervisor do
use Supervisor
def start_link(init_arg) do
Supervisor.start_link(__MODULE__, init_arg, name: __MODULE__)
end
def init(_init_arg) do
children = [
{ModelServer, name: :sentiment_model},
{ModelServer, name: :image_classifier},
{PredictionCache, []}
]
Supervisor.init(children, strategy: :one_for_one)
end
endIf a model server crashes, the supervisor automatically restarts it while other models continue serving requests.
The BEAM VM is designed for soft real-time systems. This makes Elixir perfect for:
Elixir applications routinely handle millions of concurrent connections with predictable latency, making it ideal for AI services that need to serve predictions at scale.
Deploy new model versions without downtime. Elixir’s hot code reloading allows you to update ML models in production without disrupting ongoing requests.
Elixir provides multiple approaches for integrating machine learning capabilities:
Nx is Elixir’s answer to NumPy, providing numerical computing capabilities with support for CPUs, GPUs, and TPUs.
# Tensor operations with Nx
import Nx
tensor = Nx.tensor([[1, 2, 3], [4, 5, 6]])
result = Nx.multiply(tensor, 2)
# Result: #Nx.Tensor<
# s64[2][3]
# [
# [2, 4, 6],
# [8, 10, 12]
# ]
# >Axon is a neural network library for Elixir, similar to Keras or PyTorch, but designed to work seamlessly with Nx.
model =
Axon.input("input", shape: {nil, 784})
|> Axon.dense(128, activation: :relu)
|> Axon.dropout(rate: 0.2)
|> Axon.dense(10, activation: :softmax)
# Train the model
trained_model =
model
|> Axon.Loop.trainer(:categorical_cross_entropy, :adam)
|> Axon.Loop.run(train_data, epochs: 10)Bumblebee brings pre-trained neural network models from Hugging Face to Elixir. This is a game-changer for production AI applications.
{:ok, model_info} = Bumblebee.load_model({:hf, "bert-base-uncased"})
{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "bert-base-uncased"})
serving = Bumblebee.Text.fill_mask(model_info, tokenizer)
text = "The capital of France is [MASK]."
Nx.Serving.run(serving, text)
# => %{predictions: [%{score: 0.9, token: "Paris"}, ...]}For models trained in Python, use ports or NIFs to integrate them:
defmodule MLModel do
def predict(input) do
# Call Python model via port
Port.open({:spawn, "python ml_model.py"}, [:binary])
|> Port.command(:erlang.term_to_binary(input))
|> receive_response()
end
endAlternatively, use ErlPort or Rustler for more efficient communication.
Elixir’s Phoenix framework with LiveView is perfect for building real-time recommendation interfaces:
defmodule MyAppWeb.RecommendationsLive do
use Phoenix.LiveView
def mount(_params, _session, socket) do
if connected?(socket) do
# Stream recommendations as they're computed
:timer.send_interval(1000, self(), :update_recommendations)
end
{:ok, assign(socket, recommendations: [])}
end
def handle_info(:update_recommendations, socket) do
user_id = socket.assigns.user_id
recommendations = AIEngine.get_recommendations(user_id)
{:noreply, assign(socket, recommendations: recommendations)}
end
endUse Elixir’s distributed capabilities to serve models across multiple nodes:
defmodule ModelCluster do
def predict(model_name, input) do
# Distribute prediction requests across cluster
node = :pg.get_closest_pid(:models, model_name)
GenServer.call(node, {:predict, input})
end
endCombine Phoenix Channels with AI models for responsive chatbots:
defmodule ChatChannel do
use Phoenix.Channel
def handle_in("message", %{"text" => text}, socket) do
# Process message through AI model
response =
text
|> AIModel.process()
|> generate_response()
broadcast!(socket, "response", %{text: response})
{:noreply, socket}
end
endProcess transactions in real-time with ML-based fraud detection:
defmodule FraudDetection do
use GenStage
def handle_events(transactions, _from, state) do
results =
transactions
|> Task.async_stream(&check_fraud/1)
|> Enum.map(fn
{:ok, result} -> result
{:error, _} -> %{fraud: false, confidence: 0}
end)
{:noreply, results, state}
end
defp check_fraud(transaction) do
features = extract_features(transaction)
FraudModel.predict(features)
end
endWhen building AI systems with Elixir, keep these performance tips in mind:
For heavy numerical computations, consider writing NIFs (Native Implemented Functions) in Rust or C:
defmodule FastCompute do
use Rustler, otp_app: :my_app
# Implemented in Rust for performance
def matrix_multiply(_a, _b), do: :erlang.nif_error(:nif_not_loaded)
endThe BEAM VM’s preemptive scheduling ensures fair resource allocation. Structure your AI workloads to benefit from this:
# Break large workloads into chunks
def process_large_dataset(data) do
data
|> Stream.chunk_every(1000)
|> Task.async_stream(&process_chunk/1, max_concurrency: System.schedulers_online())
|> Stream.run()
endUse ETS or Redis for caching frequent predictions:
defmodule PredictionCache do
def get_or_compute(input, model_fn) do
case :ets.lookup(:predictions, input) do
[{^input, result}] -> result
[] ->
result = model_fn.(input)
:ets.insert(:predictions, {input, result})
result
end
end
endA practical approach is combining Python’s ML ecosystem with Elixir’s operational strengths:
# Load ONNX model in Elixir
defmodule ModelServer do
use GenServer
def init(_) do
model = Ortex.load("model.onnx")
{:ok, %{model: model}}
end
def handle_call({:predict, input}, _from, state) do
output = Ortex.run(state.model, input)
{:reply, output, state}
end
endElixir’s testing framework makes it easy to test AI components:
defmodule AIModelTest do
use ExUnit.Case
test "model returns valid predictions" do
input = generate_test_input()
result = AIModel.predict(input)
assert result.confidence >= 0.0
assert result.confidence <= 1.0
assert is_binary(result.label)
end
test "handles concurrent requests" do
tasks = for _ <- 1..100 do
Task.async(fn -> AIModel.predict(random_input()) end)
end
results = Task.await_many(tasks)
assert length(results) == 100
end
endThe Elixir AI ecosystem is rapidly evolving:
The combination of Elixir’s operational excellence and the expanding ML ecosystem makes it an increasingly attractive choice for production AI systems.
While Python dominates ML research and experimentation, Elixir excels at deploying and scaling AI systems in production. Its concurrency model, fault tolerance, and low-latency characteristics make it perfect for:
If you’re building AI systems that need to serve millions of users with predictable performance and reliability, Elixir deserves serious consideration. The growing ecosystem of Nx, Axon, and Bumblebee makes it easier than ever to bring machine learning capabilities to your Elixir applications.
Ready to explore AI with Elixir? Here are your next steps:
At AsyncSquad Labs, we specialize in building scalable, production-ready AI systems using Elixir and Phoenix. Whether you’re looking to integrate machine learning into your existing application or build a new AI-powered product from scratch, we can help.
Contact us to discuss your AI project and learn how we can leverage Elixir’s strengths for your use case.
