Machine learning (ML) is a branch of artificial intelligence (AI) in which machines learn from data and past experience to recognize patterns, make predictions, and perform cognitive tasks, without being explicitly programmed. Machine learning models can learn and adapt to new patterns by training on datasets that provide relevant examples.
Machine learning systems learn through an iterative process, using their training data to build a mathematical model that can make predictions on the data. There are thousands of machine learning algorithms available.
Data scientists aim to select the most appropriate algorithm for their problem, train it on a high quality dataset, and tune its hyperparameters to achieve the best performance. However, training an ML model is not a one-time event. After a model is deployed to production and is used for inference (providing responses to real world queries), it is essential to monitor its performance and continue improving it with new data and ongoing tuning.
Types of machine learning
Supervised learning
According to Gartner, supervised learning is currently the most widely used type of machine learning among enterprises. This type of machine learning provides labeled data containing historical inputs and outputs to a machine learning algorithm, and the data is transformed into a model that can produce similar outputs for new, unseen data.
Common algorithms: deep neural networks, decision trees, linear regression, and support vector machines (SVM).
Use cases include: data classification, financial forecasting, fraud detection.
Unsupervised learning
While supervised learning requires data provided by its operators, unsupervised learning does not require a labeled training set and data. Instead, it tries to identify patterns directly in production data. This type of machine learning is useful when you need to identify patterns and make decisions using data, and historical data is not available.
Common algorithms: hidden Markov models, k-means, hierarchical clustering, and Gaussian mixture models.
Use cases include: customer segmentation, recommender systems, data visualization.
Semi-supervised learning
Semi-supervised learning is a machine learning algorithm that combines supervised and unsupervised learning algorithms. During training, it uses a combination of labeled and unlabeled data sets.
A disadvantage of supervised learning is that it requires expensive manual data labeling. On the other hand, unsupervised learning is limited in scope. To overcome these shortcomings, the concept of semi-supervised learning combines these two paradigms to provide models that can work with only a limited number of labeled data samples and still provide powerful capabilities.
Reinforcement learning
Reinforcement learning is a feedback-based process. The algorithm defines the actions of an agent, which can take action, learn from experience, and improve performance in an experimental way. The agent is rewarded for performing the correct step and penalized for performing the wrong step. Reinforcement learning agents aim to maximize rewards by taking the most appropriate actions.
Unlike supervised learning, in reinforcement learning there is no labeled data and the agent learns only from experience. For example, agents can learn via a game, in which they take actions and receive feedback through penalties and rewards that affect overall game score. The goal of the agent is to get a high score.
Common algorithms: SARSA-Lambda, DQN, DDPG, Actor-Critic
Use cases include: game theory, simulating synthetic environments, multi-agent systems
Deep learning
Deep learning is a branch of machine learning that uses layered algorithms to better understand complex data. Unlike previous generations of machine learning technology, such as regression models, deep learning algorithms are not limited to generating interpretable sets of relationships. Instead, deep learning relies on layers of non-linear connections to generate interactive, distributed representations based on thousands or even millions of factors.
Given a large training dataset, a deep learning algorithm can identify relationships between virtually any elements. These relationships can exist between shapes, colors, text, or any other input. When properly trained and tuned, the system can be used to generate predictions that approach the cognitive abilities of humans.
Automatic speech recognition (ASR), also known as computer speech recognition or speech-to-text, is the ability to use natural language processing (NLP) to convert human speech into written form. For example, many mobile devices have voice recognition built into the system to perform voice searches.
AI code assistants
AI code assistants are machine learning-driven tools designed to aid software developers by automating parts of the coding process. These assistants use traditional natural language processing (NLP), code analysis techniques, and large language models (LLMs) to understand programming languages and provide useful suggestions, such as code completion, error detection, and refactoring.
By analyzing large code repositories, AI coding assistants can recommend code snippets, auto-generate boilerplate code, and even offer real-time debugging assistance. These tools are particularly useful for speeding up repetitive tasks and improving code quality. Advanced AI coding assistants can also generate entire functions or modules from plain English descriptions, improving developer productivity and enabling non-technical users to create code.
For example, Tabnine is an AI coding assistant integrated into various development environments to enhance productivity by reducing the manual coding effort, minimizing errors, and helping developers learn new technologies.
Computer vision
This artificial intelligence technology allows computers to derive meaningful information from digital images, videos, and other visual inputs and take appropriate action. Computer vision with convolutional neural networks (CNN) has applications such as photo tagging in social media, medical radiography, and autonomous vehicles.
Face recognition uses machine learning algorithms to determine the similarity of two facial images, to evaluate a claim to identity. This technology is used for everything from logging a user into a mobile phone to searching a database of photos for a specific person.
Facial recognition converts facial images into digital representations, which are processed by neural networks to obtain high-quality features called face embeddings. These embeddings are compared to determine similarity.
Automated image and video editing
With the proliferation of rich media on websites and social networks, image and video editing is becoming more common among organizations and individuals around the world. Traditionally, these were time-consuming manual tasks, but many image and video editing tasks can now be performed by AI algorithms that surpass humans.
AI algorithms analyze photos and make intelligent predictions about how to edit, adjust, and enhance them. This eliminates manual labor and saves time and money for content creators. For large media organizations, this can result in significant cost savings and a more flexible content creation process.
With the help of artificial intelligence, organizations can also create more personalized videos to increase engagement. AI-powered video applications provide end-users with powerful features such as video search for important moments, and the ability to automatically create professional-looking video clips in a few clicks.
Recommendation engines
AI algorithms analyze historical behavior data to identify data trends that can be used to develop more effective cross-sell strategies. Online retailers use this method to recommend related products to customers.
Fraud detection involves identifying commercial or financial transactions that have illegal or malicious intent. Traditionally, fraud detection systems were based on static rule-based systems, which were maintained by expert human analysts. They were difficult to maintain and could miss new types of fraud that existing rules did not capture.
Modern fraud detection systems are based on machine learning algorithms, which detect special features in fraudulent transactions that legitimate transactions do not have. ML models can detect suspicious patterns in transactions, calculate a probability that the transaction is fraudulent, and if it passes a certain threshold, flag it for human investigation.
Banks and other financial institutions can use machine learning to find suspicious transactions. Supervised learning allows you to train a model using information about known fraudulent transactions. Anomaly detection identifies transactions that are unusual and require further investigation.
Advanced threat protection
Advanced Threat Protection (ATP) is a set of practices and solutions that can be used to detect and prevent advanced malware and attacks.
Advanced threat protection solutions leverage User and Entity Behavior Analysis (UEBA), based on machine learning algorithms, to reduce false positives and identify real security incidents.
Fuzzing is a technique for automatically detecting errors. The purpose of fuzzing is to overload an application, causing unexpected behavior, resource leaks, or crashes.
This process uses invalid, unexpected data, or random data as input to a computer system. The fuzzer repeats this process, monitoring the environment until it detects a vulnerability. Fuzzing often leverages machine learning to create new and unexpected inputs that could help uncover weaknesses in the application.
AI summarization uses natural language processing (NLP) and machine learning algorithms to automatically condense large volumes of text into shorter, more digestible summaries. This technology is invaluable for quickly extracting the main ideas from long documents, articles, research papers, or even meetings and video transcripts.
AI summarization is widely used in applications such as news aggregation, where it can provide concise versions of news stories, and in content management systems, where it helps users quickly understand large sets of information. Businesses also leverage this technology to summarize customer feedback, technical documents, and legal contracts, enabling faster decision-making and improving productivity.
Customer analytics involves using machine learning to analyze customer behavior, preferences, and interactions. By examining large datasets generated from customer interactions, machine learning models can identify patterns and trends that help businesses optimize their marketing strategies, improve customer service, and increase customer retention.
Common applications of customer analytics include segmentation, where customers are grouped based on similar behaviors or preferences, and predictive analytics, where future behaviors (such as churn or purchase likelihood) are forecasted. For example, e-commerce platforms use customer analytics to recommend personalized product suggestions, while telecom companies may predict which customers are likely to switch to competitors.
AI infrastructure refers to the hardware, software, and tools required to develop, train, deploy, and scale artificial intelligence and machine learning models. It forms the backbone of AI operations, enabling data scientists and engineers to process large datasets, optimize models, and generate predictions in real time or near real time.
AI infrastructure is designed to handle the intensive computational workloads necessary for training complex machine learning models, especially in deep learning, where the processing of large datasets and massive numbers of parameters requires significant computational power. Below are some of the key components of AI infrastructure in modern organizations.
Computing hardware
High-performance processors like GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), and AI accelerators
These provide the parallel processing power needed for large-scale model training and inference
Scalable cloud-based resources enable on-demand access to computational power, reducing the need for expensive on-premise infrastructure
Software frameworks
Tools and libraries like TensorFlow, PyTorch, and Scikit-learn
These frameworks simplify the creation, training, and fine-tuning of machine learning models
They support a wide range of machine learning tasks, from deep learning to more traditional algorithms
Data storage and management
Scalable storage solutions like data lakes, cloud storage, and distributed databases
These are crucial for handling the large datasets used in training and inference
Efficient data pipelines for managing, processing, and preparing data for machine learning tasks
Machine learning operations (MLOps) Tools
Automates the deployment, monitoring, and lifecycle management of machine learning models
Supports version control, continuous integration/continuous deployment (CI/CD) pipelines, and performance monitoring
Ensures models remain effective and up-to-date in production environments
One of the most significant trends in machine learning is the move towards cloud-based solutions. Cloud platforms offer a host of benefits for machine learning, including scalability, flexibility, and cost-effectiveness. They allow businesses to quickly scale up their machine learning efforts without the need for significant upfront investment.
Moreover, cloud platforms provide access to cutting-edge machine learning tools and frameworks, enabling businesses to tap into the latest advancements in the field. They also offer a collaborative environment where data scientists, developers, and business stakeholders can work together to develop and deploy machine learning models.
MLOps stands for Machine Learning operations. It is a key function in machine learning engineering, focused on simplifying the process of deploying, maintaining and monitoring machine learning models in production. MLOps is often a collaborative function of data scientists, DevOps engineers, and IT operations.
MLOps is a way to help create and improve the quality of machine learning and AI solutions. By adopting the MLOps approach, data scientists and machine learning engineers can work together to implement continuous integration and deployment (CI/CD) practices and appropriate monitoring, validation, and governance of ML models. The end goal is to accelerate model development and production, while improving model performance and quality.
Deep learning frameworks are software libraries and tools that simplify the development, training, and deployment of neural networks. These frameworks handle complex computations behind the scenes, allowing developers to focus on designing models rather than dealing with low-level mathematical operations. They are a critical part of the machine learning ecosystem, offering reusable components and scalable infrastructure to accelerate AI research and applications.
As models grow more complex, these frameworks make it easier to build, experiment with, and deploy neural networks. They also support distributed computing, enabling the training of large models on multiple GPUs or cloud environments. This trend is fueled by the need for faster prototyping, scalability, and more accessible machine learning development for both researchers and businesses.
Here are some of the most popular deep learning frameworks:
TensorFlow: Developed by Google, TensorFlow is widely used for building scalable machine learning models. It supports both research and production with features like distributed training and model deployment on various platforms.
PyTorch: Created by Facebook, PyTorch is known for its ease of use and dynamic computation graph, making it popular among researchers for prototyping and experimentation. It’s also gaining adoption in production environments.
Keras: A high-level API that runs on top of TensorFlow, Keras simplifies deep learning model building with an easy-to-use interface, making it a favorite for beginners and quick prototyping.
MXNet: Backed by Amazon, MXNet is designed for efficiency and scalability, particularly in cloud environments. It’s optimized for both speed and resource usage, making it suitable for training large models.
JAX: A relatively new framework from Google, JAX focuses on high-performance numerical computing and automatic differentiation, making it ideal for research in deep learning and scientific computing.
These frameworks have become essential tools for advancing deep learning, offering the flexibility, scalability, and resources needed to support cutting-edge AI innovations.
ML monitoring is a set of techniques for observing the performance of ML models in production. ML models are typically trained by observing an example dataset, and minimizing errors that indicate how well the model performs on the training task.
Once deployed to production, ML models apply the learnings from their training data to new, real-world. However, many factors, including differences between the initial training data and real-world production data, can degrade production model performance over time.
An effective machine learning monitoring system can detect these changes and help data science teams continuously improve models and datasets. In the absence of monitoring, a model can fail silently, which can have a serious negative impact on business performance and end-user experience.
Explainable AI
Explainable Artificial Intelligence (XAI) is a set of processes and methods that enable human stakeholders to understand and trust the outputs of machine learning algorithms.
Explainable AI is used to describe AI models and explain their decisions, expected impacts, and potential biases. It helps characterize model accuracy, fairness, transparency, and outcomes in AI-powered decision-making.
Explainable AI is critical to building trust and confidence when organizations deploy AI models into production. AI explainability also helps organizations adopt a responsible approach to AI development.
AI needs a lot of data to produce good results. Synthetic data is an important source of large data sets, which can help model phenomena where data is difficult to obtain, or to capture edge cases that don’t occur often in real life.
Synthetic data is artificially generated through machine learning algorithms. It reflects the statistical nature of real-world data, but does not use any identifying characteristics (such as names or personal information). Therefore, it reduces the privacy and compliance risks raised by AI datasets.
Feature importance
Feature importance is a measure that indicates how much each feature in a machine learning model contributes to its overall predictive power. It helps in understanding which features are more relevant in making predictions and can be used for model interpretability.
There are different methods to compute feature importance, such as permutation importance, mean decrease impurity, and coefficient magnitudes. These methods assign a score or weight to each feature, indicating its relative importance in the model.
Model interpretability is the process of understanding how a model makes its predictions or decisions. By examining the feature importance scores, data scientists can identify which features have the most significant impact on the model’s predictions. This information can be used to explain how the model works to stakeholders and to gain insight into the underlying patterns in the data.
Furthermore, feature importance can be used to identify and remove redundant or irrelevant features from the model. This can simplify the model and make it more interpretable, while also improving its performance by reducing the risk of overfitting.
Retrieval-augmented generation (RAG) is an emerging trend in machine learning that combines the power of retrieval-based systems with generative models. In generative models, such as large language models (LLMs), the system typically generates text based solely on patterns learned from training data. RAG enhances this process by incorporating an additional step where relevant information is retrieved from a large corpus of documents or databases to inform the generation process.
This hybrid approach allows models to generate more accurate, contextually relevant, and up-to-date responses, especially in applications like conversational AI, question answering, and content creation. By leveraging a retrieval mechanism, RAG models can access specific facts or details that might not be fully captured in the training data alone. This makes them particularly useful in dynamic environments where the knowledge base is continuously evolving.
RAG models represent a significant advancement in machine learning, bridging the gap between static, pretrained models and the need for dynamic, knowledge-based reasoning. This approach is especially valuable for organizations that require their AI systems to provide reliable, fact-based outputs in real time, enhancing both the quality and trustworthiness of the generated content.
A machine learning model is a program trained to recognize certain types of patterns to perform a useful cognitive task (for example, see the Machine Learning Use Cases above). It contains algorithms that can be trained based on a dataset, and can then learn from that data, and apply it to make predictions on new unseen data.
Developing machine learning models is a new activity for many organizations. It is a complex process that requires diligence, experimentation, and creativity. Below we describe the key steps involved in the process.
1. Selecting an algorithm
There are thousands of machine learning algorithms, and it can be difficult to determine the best algorithm for a given model. In most cases, you will try multiple algorithms to find one that provides the most accurate results.
Key considerations for selecting an algorithm include the size of the training data, the required accuracy and interpretability of model outputs, the training speed required, linearity of the training data, and number of features in the data set.
2. Splitting the dataset
By splitting the training data into two or more groups, you can train and validate the model using a single data source. This allows you to determine if the model is overfitting—meaning that it works well on the training data, but not on the unseen test data.
Most machine learning projects divide the dataset into three groups:
Training—used for initial model training.
Validation—used to test different versions of the model and compare their performance.
Testing—used to test the final version of the model and estimate its real-world performance.
What is cross-validation?
Cross-validation is a common technique to partition training data to maximize its value for model training. For example, 10-fold cross-validation splits the data into 10 groups, so you can train and test the data 10 times. This works as follows:
Divide the data into 10 equal parts
Hold one part and train the model on the remaining 9 parts
Test the model with the remaining part
Repeat the process 10 times, each time holding a different part and testing the model on the remaining 9 parts
The average performance of the model across all 10 tests is called the cross-validation score.
Note that in some types of data, such as time series data sets, cross-validation works differently than described above.
3. Tuning hyperparameters
Hyperparameters are model properties that data science teams set before building and training models. They are external parameters that determine how the model operates, and are treated separately from model parameters, which are dynamically determined as the model trains.
For example, in a neural network, there are several hyperparameters including the number of neural layers and the learning rate. Data scientists set these hyperparameters and then train the neural network to get the model parameters, which are the weights and biases.
It is common to re-run the model with multiple combinations of hyperparameters to see which combination provides the best results.
4. Training and testing
Once a data science team has selected an algorithm, the data is ready, and model hyperparameters have been determined, it’s time to start training the model. This process cycles through each set of hyperparameter values you decide to investigate. Data points are typically fed to the model in groups known as batches. The model may process the data in one or more cycles, known as epochs – each including one or more batches. After each epoch, cross validation is performed to see the model’s performance.
At this stage It is common to test multiple algorithms, each with multiple hyperparameter variations, to see which provides the best results.
5. Evaluating the model
Earlier in the process, the dataset was divided into three groups—training set, validation set, and testing set. Now that a data science team has obtained a final version of the model, they can subject it to a realistic performance test using the testing set, which the model has not seen yet.
Applying the final version of the model to the testing dataset, and measuring performance metrics, simulates how the model will perform on real-world data. The team can compare performance to other, state of the art models, or other experiments conducted by themselves or their colleagues.
If a model’s performance is insufficient, the team can go back to the drawing board and try to build a better model, by changing the algorithm, hyperparameters, or improving the dataset.
What is machine learning inference?
Machine learning inference is the process of using a trained model to make predictions on new, unseen data. Once a model has been trained and evaluated, it is deployed in a production environment where it processes real-world data to generate useful outputs, such as predictions, classifications, or recommendations.
Unlike the training phase, where the model learns from large datasets and adjusts its internal parameters, inference involves applying the fixed model parameters to new input data. The goal is to deliver fast and accurate results in real-time or batch scenarios.
Inference typically includes the following steps:
Data preprocessing: New input data must often be transformed in the same way as the training data, using normalization, scaling, or encoding techniques.
Model prediction: The preprocessed data is fed into the model, which computes an output based on its learned patterns and parameters.
Post-processing: The model’s raw output may require additional steps, such as converting a probability score into a final decision or formatting the result for downstream use.
Efficiency during inference is critical, especially in real-time applications like recommendation systems, fraud detection, or autonomous vehicles. Optimizing inference often involves techniques like model compression, quantization, and the use of specialized hardware (e.g., GPUs, TPUs) to ensure low-latency predictions.
Machine learning engineering is the use of engineering principles, tools, and techniques to design and build complex machine learning systems. Machine learning engineers are responsible for data collection, model training, building and deploying a machine learning system that customers can use. They enable machine learning algorithms to be implemented as part of efficient production systems.
Difference between data scientists and data engineers
Data analysts and data scientists are generally interested in understanding business problems. They build models and evaluate them in a limited development environment.
Machine learning engineers collect data from a variety of sources, preprocess it, and prepare it for efficient model training. They are also concerned with ensuring models can run in production and coexist well with other production processes.
Why are GPUs important in machine and deep learning?
The longest and most resource-intensive phase of most deep learning projects is the training phase. For a model with a large number of parameters, training time can be significant. When training takes longer, and insufficient computing power is available, teams wait and waste valuable time. This also makes it difficult to experiment with multiple variations of algorithms and hyperparameters.
Traditional central processing units (CPUs) can be slow to process machine learning computations; graphics processing units (GPUs) can accelerate training of machine learning models, and are especially suited for deep learning.
GPUs make it possible to run models with large numbers of parameters quickly and efficiently. This is because GPUs can parallelize training tasks, distribute them across a large number of processors, and perform computational tasks concurrently.
Some data science teams acquire AI workstations, with multiple GPUs that provide huge concurrent processing power. Other teams take advantage of cloud-based compute instances with GPUs, which can be easily scaled up according to project needs without an upfront investment.
Large language models (LLMs) are advanced machine learning models designed to understand and generate human language. These models are trained on vast amounts of text data and can perform a wide range of natural language processing (NLP) tasks, including text generation, translation, summarization, and question answering.
LLMs like GPT-4, Claude, and Gemini utilize deep learning techniques, particularly transformer architectures, to learn patterns and relationships within text data. This allows them to produce coherent and contextually relevant responses, making them valuable tools in applications ranging from chatbots to content creation and beyond.
Key LLM concepts
Transformer architecture
The transformer architecture is the backbone of modern LLMs and represents a significant leap from previous sequence models like RNNs and LSTMs. At its core, the transformer model uses a mechanism called self-attention, which allows it to weigh the relevance of different words in a sentence relative to each other, regardless of their position.
This architecture is composed of an encoder-decoder structure, where the encoder processes input text and the decoder generates output text. Transformers enable LLMs to handle long-range dependencies in text and efficiently manage parallel processing, making them highly effective for large-scale language tasks.
Training on large datasets
Training LLMs requires immense datasets, often containing billions or even trillions of words. These datasets are typically collected from a variety of sources, including books, articles, websites, and social media. The training process involves feeding the model with text data and adjusting its parameters to minimize prediction errors.
As the model is exposed to more data, it learns to capture the nuances of language, including grammar, facts, and even some level of reasoning. The scale of training data is crucial for the model’s ability to generalize well across different tasks and domains, contributing to its versatility and effectiveness.
Model size and parameters
The effectiveness of an LLM is often correlated with its size, which is typically measured by the number of parameters it contains. Parameters are the internal variables that the model adjusts during training to make predictions. Modern LLMs can have hundreds of billions or even trillions of parameters, making them highly complex and capable of capturing subtle patterns in data.
However, larger models also require more computational resources for training and deployment, and they can be prone to issues like overfitting if not managed properly. Despite these challenges, larger models generally perform better across a wider range of tasks due to their enhanced capacity to learn from data.
Fine-tuning LLM
Fine-tuning is a crucial process in making LLMs more applicable to specific tasks. After an LLM has been pre-trained on a large and diverse corpus of text, it can be fine-tuned on a smaller, task-specific dataset to optimize its performance for particular applications. This involves further training the model on new data that is closely related to the target task, such as sentiment analysis, legal document review, or medical diagnostics.
Fine-tuning allows the LLM to adapt its general language understanding to the nuances of the task at hand, improving accuracy and relevance.
Developing applications with LLMs involves integrating these models into software systems to perform specific tasks, such as automated customer support, content generation, or data analysis. The development process typically includes selecting an appropriate LLM, fine-tuning it for the intended use case, and deploying it within an application infrastructure.
Developers must also consider factors like latency, scalability, and user experience, ensuring that the LLM can handle real-world demands. Moreover, application development with LLMs often requires ongoing maintenance to update models and refine their performance as new data becomes available.
Prompt engineering is the practice of designing input prompts that guide LLMs to produce desired outputs. Because LLMs generate text based on the context provided by the input, carefully crafted prompts can significantly influence the quality and relevance of the responses. This involves experimenting with different phrasing, context settings, personas the model should assume, and input structures, to elicit the most useful results from the model.
Effective prompt engineering can improve the performance of LLMs across various tasks, making them more adaptable and efficient in applications like automated writing, decision support, and conversational agents.
Security is a critical consideration when deploying LLMs, particularly because these models can be susceptible to misuse and exploitation. Potential security risks include data leakage, where sensitive information inadvertently appears in model outputs, and adversarial attacks, where inputs are manipulated to trick the model into producing harmful or misleading content.
To mitigate these risks, developers must implement robust security measures, such as input sanitization, output monitoring, and regular updates to the model to patch vulnerabilities. Ensuring the ethical and secure use of LLMs is essential for maintaining trust and protecting users.
OpenAI’s GPT-4 is a highly advanced large language model featuring over 1 trillion parameters and multimodal capabilities, processing both text and image inputs. It supports a large context window of up to 128,000 tokens (at the time of this writing), enabling detailed responses for long documents. GPT-4 achieves near-human performance in various benchmarks.
In May 2024, OpenAI introduced GPT-4o (Omnimodel), which further enhances multimodal input and output, allowing the model to process and generate text, audio, and images. GPT-4o offers improved response times, better non-English language performance, and is 50% cheaper and twice as fast as previous models. Its integrated architecture captures more context and subtleties, enhancing real-time applications. GPT-4o is also widely available, including to the general public at no cost, making its capabilities more accessible.
In September 2024, OpenAI released o1, the first of a new type of LLM that spends time thinking about problems before responding, a process known as chain of thought reasoning. The model is capable of performing complex cognitive tasks, and is less suited to creative writing and conversational tasks. OpenAI claims that the model performs similarly to PhD students on benchmark tasks in fields like physics, chemistry, biology, mathematics, and coding.
Anthropic Claude
Anthropic’s Claude is a family of advanced large language models, first released in March 2023. The latest iteration, Claude 3.5 Sonnet, launched in June 2024, brings enhanced intelligence, particularly in graduate-level reasoning, coding, and vision tasks. Built on the principles of Constitutional AI, Claude models are designed to be helpful, honest, and harmless.
Claude 3.5 Sonnet operates at twice the speed of its predecessor, Claude 3 Opus, while offering cost-effective pricing. It supports a 200K token context window, making it ideal for complex tasks like customer support and multi-step workflows. The model excels in visual reasoning, coding proficiency, and content generation with a natural tone.
Claude can be deployed via API, Amazon Bedrock, and Google Cloud. The model also introduces “Artifacts,” a new feature that facilitates collaborative work by integrating AI-generated content into real-time projects.
Meta LLaMA is a series of open-source large language models developed by Meta, designed to push the boundaries of what open-source AI can achieve. LLama models are built to be accessible to developers and researchers, promoting transparency and collaboration in AI development. Meta’s commitment to open-source AI is intended to democratize advanced AI tools, allowing a broader community to contribute to and benefit from these powerful technologies.
LLama 3.1 is the latest version of the LLama series, featuring the 405B parameter model. It is the first open-source AI model of its scale, designed to rival the best closed-source models in terms of capability, flexibility, and control. LLama 3.1 was developed to handle a wide range of complex tasks, from general knowledge queries to advanced multilingual translation, and includes significant improvements in processing power and contextual understanding.
Google Gemini, developed by Google DeepMind, is a multimodal LLM capable of processing text, code, audio, images, and video. Unlike many competitors, Gemini offers real-time knowledge by integrating with Google’s search index, enabling it to respond to live events without a knowledge cutoff date. It is available in various editions, from the lightweight Gemini Flash to the advanced Gemini Pro.
In February 2024, the release of Gemini Pro 1.5 introduced an extended context window of up to 1 million tokens (recently expanded to 2 million in some model versions), enhancing its ability to handle large datasets, hour-long videos, and extensive codebases. With flexible deployment options and support from Google’s latest TPUs, Gemini is optimized for both high-speed training and versatile real-world applications.
Cohere Command R+ is a language model optimized for enterprise use, particularly in handling complex conversational interactions and long-context tasks. Designed for scenarios such as retrieval augmented generation (RAG) and multi-step tool use, it is ideal for businesses transitioning from proof of concept to full-scale production.
Command R+ supports on-premises deployments and can also operate within an organization’s cloud environment, making it versatile for various enterprise needs. The model features an extended context length of up to 128,000 tokens, allowing for detailed and prolonged interactions. Its multilingual capabilities enable high performance in languages like English, French, Spanish, Italian, and German, with additional support for 13 more languages.
Command R+ excels in cross-lingual tasks, including translation and multilingual responses, and uses retrieval augmented generation to ground its outputs in document snippets, ensuring accuracy and providing citations for generated content.
Tabnine Protected is a proprietary model from Tabnine and is purpose-built for software development. It is trained exclusively on permissively licensed code. This ensures that the recommendations from Tabnine never match any proprietary code and removes any concerns around legal risks associated with accepting the code suggestions. When using this model, Tabnine offers a zero data retention policy – we don’t store customer code, don’t share customer code or usage data with third parties, and don’t use customer code to train our models.
What are the key challenges of machine learning projects?
Data collection
The first step in any ML or data science project is to find and collect the necessary data assets. However, the availability of adequate data remains one of the most common challenges facing organizations and data scientists, which directly impacts their ability to build robust ML models.
There are several reasons that data can be hard to collect and prepare for machine learning projects:
Data exists in many different sources, both inside and outside the organization. Each source might have a different data format.
Machine learning projects might require huge data volumes, requiring big data systems that can transfer, store, and process data at large scale.
Data quality is critical for model performance, and might be difficult to ascertain. If data quality is determined to be low, it can be difficult to improve it.
Most machine learning projects require labeled data. Manual labeling of data is expensive and time-consuming and might affect a project’s time to market.
Data drift
Data drift is one of the main reasons for the decrease of model accuracy over time. Data drift is a gradual change in input data that can affect model performance. Common reasons for data drift are:
Privacy concerns and growing compliance requirements make it difficult for data scientists to make use of datasets. Cybersecurity is becoming a bigger concern with the move to the public cloud. These two factors make it difficult for data scientists and machine learning teams to access the datasets they need.
Ensuring continued security and compliance with data protection regulations, such as the EU’s GDPR, presents an additional challenge for organizations. Datasets can contain personally identifiable information (PII), and failure to protect this data could result in severe financial penalties as well as pressure from regulators and costly audits.
Tabnine: The enterprise-grade AI code assistant
Tabnine is the AI code assistant that you control — helping development teams of every size use AI to accelerate and simplify the software development process without sacrificing privacy, security, or compliance. Tabnine boosts engineering velocity, code quality, and developer happiness by automating the coding workflow through AI tools customized to your team. It’s trusted by more than 1,000,000 developers across thousands of organizations.
Key features
Best-in-class AI code generation: Let Tabnine’s AI coding assistant streamline AI code generation and automate mundane tasks so you can spend more time on the work you love. Get accurate and personalized code completions. Add comments and other natural language prompts in-line or via chat and Tabnine will automatically convert them into code.
Supports all popular languages and IDEs: Tabnine supports more than 80 programming languages and frameworks such as Python, Java, JavaScript, C, C++, Go, and more. Tabnine is easy to integrate with popular development environments, with plugins available for VS Code, the JetBrains family of IDEs (e.g., IntelliJ, Android Studio), Visual Studio, and Eclipse.
Protection from IP issues: Tabnine has trained its proprietary models (Tabnine Protected for Chat, and the universal model for code completion) exclusively on permissively licensed code. This ensures that the recommendations from Tabnine never match any proprietary code and removes any concerns around legal risks associated with accepting the code suggestions. Tabnine is transparent about the data used to train our proprietary model and shares it with customers under NDA. Additionally, Tabnine offers an IP indemnification to enterprise users for peace of mind.
Tabnine Chat: Tabnine includes Tabnine Chat, the enterprise-grade, code-centric chat application that allows developers to interact with AI models using natural language. It supports numerous use cases such as planning (i.e., asking general coding questions or better understanding code in an existing project), code generation, explaining code, creating tests, fixing code, creating documentation, and maintaining code.
AI personalized to you: In AI, context is everything. To increase the effectiveness of AI code assistants, it’s imperative to provide contextual awareness to the LLMs so that they can understand the subtle nuances that make a developer and organization unique.Tabnine leverages locally available data in the developer’s IDE to provide more accurate and relevant results. This includes: – Runtime errors – Imported libraries – Other open files – Current files – Compile / syntax errors – Noncode sources of information – Current selected code – Connected repositories – Conversation history – Git history – Project metadata and other project files
Personalized AI recommendations based on awareness of a developer’s IDE are accepted 40% more often than AI suggestions generated without these integrations. Developers can connect Tabnine to their organization code repos (e.g., GitHub, GitLab, Bitbucket) to gain global context. Tabnine also offers model customization — you can fine-tune Tabnine’s proprietary model using your own code to create a custom model. Model customization is extremely valuable when you have code in a bespoke programming language or a language that’s underrepresented in the training data set, such as System Verilog.
AI Code Review Agent: Tabnine AI Code Review Agent enables customers to codify their institutional knowledge (e.g., accepted standards for software development, unique best practices, or corporate policies) into rules that can be applied in code review at the pull request or in the IDE. You provide the parameters you’d like to see your code comply with via plain language (no complex setup required) and Tabnine converts this into a set of comprehensive rules (also reviewable via plain language). When developers create a pull request, the Code Review Agent checks the code and information in the pull request against that set of rules. If the code doesn’t conform to your expectations in any way, the agent flags it to the code reviewer and provides guidance and suggested edits to fix the issue.
Switchable model selection: Access new state-of-the-art models in Tabnine Chat as soon as they become available. You can choose from Tabnine Protected, Tabnine + Mistral, GPT-4o, GPT-4 Turbo, GPT-3.5 Turbo, Codestral, Claude3, and Cohere’s Command R. You’re not locked into any one of these models and can switch instantly between models for specific projects, use cases, or to meet the requirements of specific teams.
Tabnine AI agents for Atlassian Jira: With Tabbnine Jira Implementation Agent and Jira Validation Agent, you can now implement Jira issues and generate code based on the requirements in Jira issues. With just a single click, Tabnine can implement a Jira issue, generating code from the requirements outlined in those issues. In addition to generating code for issues, you can also use Tabnine on either human- or AI-generated code to validate and review your implementation. The Jira Validation Agent will verify that your code accurately captures the requirements outlined in the Jira issue, offering guidance and code suggestions if it doesn’t.
Total deployment flexibility: Tabnine offers its customers numerous deployment options. Customers can consume Tabnine as a secure SaaS offering (in a multitenant environment or a single-tenant environment) or do a fully private installation (on-premises or on VPC) to ensure that their code stays in the boundaries of their corporate network and isn’t shared with any external party.
Enterprise-grade security: Tabnine offers key compliances like SOC 2 Type 2, GDPR, and ISO 9001 to ensure the security and privacy of your data.
Onboard onto projects in minutes: The Code Explorer agent for Tabnine helps developers ramp on a new project faster. For developers who are new to an organization or existing developers who are new to a project, Code Explorer provides a comprehensive overview of key project elements, including runnable scripts, dependencies, and overall structure to help them get up to speed effortlessly.
Plan your approach to development tasks: Ask Tabnine coding questions, learn how things work in your specific project, and get solutions and references relevant to your workspace. You can also use Tabnine to search your codebase. For example, if you were planning an approach to fixing errors in your log files, you can ask Tabnine to “find the errors in these log files,” and then prompt it to “generate an ASCII table showing the errors and their locations.” Then you could move on to fixing the errors by asking Tabnine to “provide solutions to fix and resolve these errors.”
Natural language code generation: Use natural language to generate code based on your design specs. Create software components, features, functionality, and more. As you continue coding, Tabnine will also provide in-line code completions, offering real-time, context-aware suggestions that seamlessly blend with your coding style. Tabnine can also support high-complexity tasks. For example, if you needed to create a function to parse an array and return specific values if criteria were found, you can use natural language to describe your requirements and prompt the AI agent to generate code matching those requirements. With Tabnine, you could also use “@” mentions to tag elements in the workspace to instruct the AI to generate code with specific context taken into account.
Unit test generation: Ask Tabnine to create tests for a specific function or code in your project, and get back the actual test cases, implementation, and assertion. Tabnine takes in the context from existing tests in your project and codebase to suggest tests that align with your project’s testing framework and variables.
Error fixing: Select or reference code with an error and Tabnine will recommend fixes. As your tools identify errors within your code and error notifications emerge in the problems tab or in-line using colored indicators, simply click on the error indicator and prompt Tabnine to suggest a fix. Rapidly accelerate error fixing without leaving your IDE and try multiple solutions to fix your errors. You can even use Tabnine to help resolve security issues identified by tools like Snyk.
AI documentation generation: Generate documentation for specific sections of your code to enhance readability and make it easy for other team members to understand. As you write code, use Tabnine to generate documentation including format documentation of classes and functions, comments, and in-line docs. Tabnine will generate standardized documentation, enhancing the readability of your code with every function, method, class, or line as needed clearly documented in a standardized format that’s easy to understand. If you want docs written in a specific format, you can even prompt Tabnine to do so. If you’re already using a documentation (format for example Google Java Style Guide), Tabnine will pick up on that and automatically generate documentation in your code that matches the context of existing documentation.
Code explanations: Tabnine Chat can provide you with an explanation for a block of existing code, which is especially useful when reading a new codebase or reading legacy code in languages you don’t know as well. This AI chat function allows for a pair programming experience, where the AI contributes to coding tasks, making it easier to work with unfamiliar codebases, frameworks, APIs, and languages.
Maintain and improve existing code: In addition to writing new code, Tabnine can help you change the existing code by adding functionality, refactoring, or fixing specific code with contextually relevant recommendations.
