Top 10 AI Tools for Network Engineers

In the ever-evolving world of technology, network engineers play a vital role in ensuring that our digital communications run smoothly. With the increasing complexity of networks and the growing demand for efficiency, artificial intelligence (AI) is becoming an indispensable tool for network professionals. In this blog post, we will explore the top 10 AI tools for network engineers, highlighting their functionalities, benefits, and how they can enhance network management. Whether you are a seasoned professional or just starting in the field, this guide will provide you with valuable insights into how AI can transform your work.

1. Cisco DNA Center

Cisco DNA Center is a comprehensive network management platform that leverages AI to automate and optimize network operations. It provides insights and analytics that empower network engineers to make informed decisions quickly.

Key Features:

Automation: Automates routine tasks, reducing manual workload.
Insights: Offers analytics to understand network performance and user experiences.
Policy Management: Simplifies the application of network policies across devices.

Benefits:

Reduces the time spent on network management tasks.
Enhances decision-making with data-driven insights.
Improves overall network performance and user satisfaction.

2. Juniper Mist AI

Juniper Mist AI is designed to provide proactive insights and automation across the network. It enhances user experiences and operational efficiency through its AI-driven capabilities.

Key Features:

Proactive Insights: Offers real-time analytics on network performance.
Automation: Automates troubleshooting processes to minimize downtime.
User Experience: Monitors user experiences to optimize connectivity.

Benefits:

Helps identify and resolve issues before they impact users.
Increases network reliability and performance.
Streamlines operations with automated processes.

3. Darktrace

Darktrace is an AI-driven cybersecurity tool that detects and responds to cyber threats in real-time. It learns the normal behavior of network devices to identify anomalies and potential security breaches.

Key Features:

Anomaly Detection: Recognizes unusual patterns in network behavior.
Self-Learning: Adapts to new threats using machine learning.
Real-time Response: Provides immediate alerts and response options for security incidents.

Benefits:

Enhances network security by identifying threats early.
Reduces the risk of data breaches and cyberattacks.
Provides peace of mind with continuous monitoring.

4. Trellix

Trellix combines security and performance management, utilizing AI to provide insights into network traffic and potential vulnerabilities. It is designed to give network engineers a comprehensive view of their network’s health.

Key Features:

Traffic Analysis: Monitors network traffic to identify patterns and potential issues.
Vulnerability Assessment: Scans for vulnerabilities in real-time.
Integrated Security: Combines security features with performance management.

Benefits:

Improves network performance by identifying bottlenecks.
Strengthens security posture through continuous monitoring.
Offers a holistic view of network operations.

5. LangChain

LangChain is a tool for building complex workflows and integrating various services, particularly useful for automating network management tasks. It allows engineers to create custom solutions that fit their specific needs.

Key Features:

Workflow Automation: Simplifies the creation of automated workflows.
Service Integration: Connects multiple services for seamless operations.
Custom Solutions: Allows for tailored workflows based on unique requirements.

Benefits:

Enhances efficiency by reducing manual processes.
Increases flexibility in network management.
Facilitates collaboration between different tools and services.

6. Spinach

Spinach is an AI tool that helps engineers streamline their workflows, focusing on automation and efficiency in engineering tasks. It is particularly beneficial for network engineers looking to optimize their processes.

Key Features:

Workflow Optimization: Analyzes and improves existing workflows.
Task Automation: Automates repetitive engineering tasks.
Performance Tracking: Monitors performance metrics for continuous improvement.

Benefits:

Reduces time spent on mundane tasks.
Increases overall productivity and efficiency.
Encourages innovation by freeing up time for complex problem-solving.

7. PyTorch

PyTorch is a popular machine learning library that can be utilized by network engineers for developing AI models to enhance network performance. Its flexibility and ease of use make it a favorite among engineers.

Key Features:

Dynamic Computation Graphs: Allows for flexible model building.
Extensive Libraries: Offers a wide range of tools for machine learning.
Community Support: Large community providing resources and support.

Benefits:

Empowers engineers to create custom AI solutions.
Facilitates experimentation with different models and approaches.
Enhances the ability to analyze and optimize network performance.

Simple PyTorch Example:

Here’s a basic example of using PyTorch to create a simple linear regression model:


import argparse
import gym
import numpy as np
from itertools import count
from collections import namedtuple

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Categorical

# Cart Pole

parser = argparse.ArgumentParser(description='PyTorch actor-critic example')
parser.add_argument('--gamma', type=float, default=0.99, metavar='G',
                    help='discount factor (default: 0.99)')
parser.add_argument('--seed', type=int, default=543, metavar='N',
                    help='random seed (default: 543)')
parser.add_argument('--render', action='store_true',
                    help='render the environment')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                    help='interval between training status logs (default: 10)')
args = parser.parse_args()

env = gym.make('CartPole-v1')
env.reset(seed=args.seed)
torch.manual_seed(args.seed)

SavedAction = namedtuple('SavedAction', ['log_prob', 'value'])

class Policy(nn.Module):
    """
    implements both actor and critic in one model
    """
    def __init__(self):
        super(Policy, self).__init__()
        self.affine1 = nn.Linear(4, 128)

        # actor's layer
        self.action_head = nn.Linear(128, 2)

        # critic's layer
        self.value_head = nn.Linear(128, 1)

        # action & reward buffer
        self.saved_actions = []
        self.rewards = []

    def forward(self, x):
        """
        forward of both actor and critic
        """
        x = F.relu(self.affine1(x))

        # actor: choses action to take from state s_t
        # by returning probability of each action
        action_prob = F.softmax(self.action_head(x), dim=-1)

        # critic: evaluates being in the state s_t
        state_values = self.value_head(x)

        # return values for both actor and critic as a tuple of 2 values:
        # 1. a list with the probability of each action over the action space
        # 2. the value from state s_t
        return action_prob, state_values

model = Policy()
optimizer = optim.Adam(model.parameters(), lr=3e-2)
eps = np.finfo(np.float32).eps.item()

def select_action(state):
    state = torch.from_numpy(state).float()
    probs, state_value = model(state)

    # create a categorical distribution over the list of probabilities of actions
    m = Categorical(probs)

    # and sample an action using the distribution
    action = m.sample()

    # save to action buffer
    model.saved_actions.append(SavedAction(m.log_prob(action), state_value))

    # the action to take (left or right)
    return action.item()

def finish_episode():
    """
    Training code. Calculates actor and critic loss and performs backprop.
    """
    R = 0
    saved_actions = model.saved_actions
    policy_losses = [] # list to save actor (policy) loss
    value_losses = [] # list to save critic (value) loss
    returns = [] # list to save the true values

    # calculate the true value using rewards returned from the environment
    for r in model.rewards[::-1]:
        # calculate the discounted value
        R = r + args.gamma * R
        returns.insert(0, R)

    returns = torch.tensor(returns)
    returns = (returns - returns.mean()) / (returns.std() + eps)

    for (log_prob, value), R in zip(saved_actions, returns):
        advantage = R - value.item()

        # calculate actor (policy) loss
        policy_losses.append(-log_prob * advantage)

        # calculate critic (value) loss using L1 smooth loss
        value_losses.append(F.smooth_l1_loss(value, torch.tensor([R])))

    # reset gradients
    optimizer.zero_grad()

    # sum up all the values of policy_losses and value_losses
    loss = torch.stack(policy_losses).sum() + torch.stack(value_losses).sum()

    # perform backprop
    loss.backward()
    optimizer.step()

    # reset rewards and action buffer
    del model.rewards[:]
    del model.saved_actions[:]

def main():
    running_reward = 10

    # run infinitely many episodes
    for i_episode in count(1):

        # reset environment and episode reward
        state, _ = env.reset()
        ep_reward = 0

        # for each episode, only run 9999 steps so that we don't
        # infinite loop while learning
        for t in range(1, 10000):

            # select action from policy
            action = select_action(state)

            # take the action
            state, reward, done, _, _ = env.step(action)

            if args.render:
                env.render()

            model.rewards.append(reward)
            ep_reward += reward
            if done:
                break

        # update cumulative reward
        running_reward = 0.05 * ep_reward + (1 - 0.05) * running_reward

        # perform backprop
        finish_episode()

        # log results
        if i_episode % args.log_interval == 0:
            print('Episode {}\tLast reward: {:.2f}\tAverage reward: {:.2f}'.format(
                  i_episode, ep_reward, running_reward))

        # check if we have "solved" the cart pole problem
        if running_reward > env.spec.reward_threshold:
            print("Solved! Running reward is now {} and "
                  "the last episode runs to {} time steps!".format(running_reward, t))
            break

if __name__ == '__main__':
    main()

these are 2 files running pytorch

import argparse
import gym
import numpy as np
from itertools import count
from collections import deque
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Categorical

parser = argparse.ArgumentParser(description='PyTorch REINFORCE example')
parser.add_argument('--gamma', type=float, default=0.99, metavar='G',
                    help='discount factor (default: 0.99)')
parser.add_argument('--seed', type=int, default=543, metavar='N',
                    help='random seed (default: 543)')
parser.add_argument('--render', action='store_true',
                    help='render the environment')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                    help='interval between training status logs (default: 10)')
args = parser.parse_args()

env = gym.make('CartPole-v1')
env.reset(seed=args.seed)
torch.manual_seed(args.seed)

class Policy(nn.Module):
    def __init__(self):
        super(Policy, self).__init__()
        self.affine1 = nn.Linear(4, 128)
        self.dropout = nn.Dropout(p=0.6)
        self.affine2 = nn.Linear(128, 2)

        self.saved_log_probs = []
        self.rewards = []

    def forward(self, x):
        x = self.affine1(x)
        x = self.dropout(x)
        x = F.relu(x)
        action_scores = self.affine2(x)
        return F.softmax(action_scores, dim=1)

policy = Policy()
optimizer = optim.Adam(policy.parameters(), lr=1e-2)
eps = np.finfo(np.float32).eps.item()

def select_action(state):
    state = torch.from_numpy(state).float().unsqueeze(0)
    probs = policy(state)
    m = Categorical(probs)
    action = m.sample()
    policy.saved_log_probs.append(m.log_prob(action))
    return action.item()

def finish_episode():
    R = 0
    policy_loss = []
    returns = deque()
    for r in policy.rewards[::-1]:
        R = r + args.gamma * R
        returns.appendleft(R)
    returns = torch.tensor(returns)
    returns = (returns - returns.mean()) / (returns.std() + eps)
    for log_prob, R in zip(policy.saved_log_probs, returns):
        policy_loss.append(-log_prob * R)
    optimizer.zero_grad()
    policy_loss = torch.cat(policy_loss).sum()
    policy_loss.backward()
    optimizer.step()
    del policy.rewards[:]
    del policy.saved_log_probs[:]

def main():
    running_reward = 10
    for i_episode in count(1):
        state, _ = env.reset()
        ep_reward = 0
        for t in range(1, 10000):  # Don't infinite loop while learning
            action = select_action(state)
            state, reward, done, _, _ = env.step(action)
            if args.render:
                env.render()
            policy.rewards.append(reward)
            ep_reward += reward
            if done:
                break

        running_reward = 0.05 * ep_reward + (1 - 0.05) * running_reward
        finish_episode()
        if i_episode % args.log_interval == 0:
            print('Episode {}\tLast reward: {:.2f}\tAverage reward: {:.2f}'.format(
                  i_episode, ep_reward, running_reward))
        if running_reward > env.spec.reward_threshold:
            print("Solved! Running reward is now {} and "
                  "the last episode runs to {} time steps!".format(running_reward, t))
            break

if __name__ == '__main__':
    main()

Breakdown of the Code:

Data Generation: We create synthetic data that follows a linear trend.
Model Definition: A simple linear model with one input and one output.
Loss Function and Optimizer: We use Mean Squared Error as the loss function and Stochastic Gradient Descent for optimization.
Training Loop: We train the model for 100 epochs, updating the weights based on the loss.

8. TensorFlow

TensorFlow is another widely used framework for machine learning, useful for building complex predictive models that can analyze network traffic patterns. Its scalability and robustness make it suitable for large-scale applications.

Key Features:

Scalability: Designed to handle large datasets and complex models.
Versatility: Supports various machine learning and deep learning tasks.
Community and Documentation: Strong community support with extensive documentation.

Benefits:

Enables the development of sophisticated AI solutions.
Improves the ability to predict and analyze network traffic.
Facilitates collaboration and sharing of models across teams.

9. Cisco’s AI-Reinforcement Learning Course

Cisco offers a specialized course focusing on using AI and reinforcement learning for managing networks. This course is ideal for network engineers looking to enhance their skills and knowledge in AI applications.

Key Features:

Comprehensive Curriculum: Covers foundational and advanced topics.
Hands-on Learning: Provides practical exercises and projects.
Expert Instructors: Learn from industry experts and experienced instructors.

Benefits:

Enhances understanding of AI in network management.
Provides practical skills that can be applied immediately.
Increases career opportunities in the growing field of AI.

10. Apache MXNet

Apache MXNet is a flexible and efficient deep learning framework that can be applied in network engineering for building scalable AI applications. It is particularly suited for tasks requiring high performance and scalability.

Key Features:

Efficiency: Optimized for speed and resource management.
Flexibility: Supports multiple programming languages and APIs.
Scalability: Can scale across multiple GPUs and machines.

Benefits:

Enables the development of high-performance AI applications.
Supports a wide range of deep learning tasks in network engineering.
Facilitates collaboration across different programming environments.

Conclusion

The integration of AI tools in network engineering represents a significant shift in how network management is approached. These tools not only enhance network performance but also improve security and operational efficiency. As networks become more complex, the need for automated and intelligent solutions will continue to grow. By incorporating these AI tools into their workflows, network engineers can streamline processes, make better decisions, and ultimately provide a better experience for users.

In summary, the top 10 AI tools for network engineers—Cisco DNA Center, Juniper Mist AI, Darktrace, Trellix, LangChain, Spinach, PyTorch, TensorFlow, Cisco’s AI-Reinforcement Learning Course, and Apache MXNet—offer various functionalities that cater to the diverse needs of network professionals. Embracing these technologies is essential for staying competitive in the field and ensuring the security and efficiency of network operations.

As the landscape of networking continues to evolve, so too will the tools and techniques available to engineers. Staying informed about these advancements and continuously seeking out new knowledge will be key to success in this dynamic field.

References

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10 Must-Have AI Tools for Engineers – Spinach AI 10 AI tools for engineers to explore · 1. Spinach · 2. PyTorch · …
What is artificial intelligence (AI) for networking? AI for networking enhances both end user and IT operator experiences by sim…
AI for Network Engineers – Udemy AI for Network Engineers. AI-Reinforcement learning for creating P…
What are the 10 AI tools? – Quora Some popular AI tools include TensorFlow, Microsoft Cognitive Toolkit (CNTK), …
70 Best Networking AI tools – 2024 – TopAI.tools Discover the best 70 paid and free AI Networking AI, and find their featur…
11 Best Generative AI Tools and Platforms in 2024 – Turing The top 11 generative AI tools and platforms that empo…
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