In the infinite world of Technological updates and features, a new technology is being developed by Direct-to-Mobile (D2M) broadcast technology, it will allow television and multimedia content to be sent directly to mobile phones without using mobile data or cellular networks. Instead of relying on 4G or 5G, D2M will work like traditional TV broadcasting, enabling users to watch live channels even without an internet connection. With this technology, it is expected to be useful during emergencies, reduce network congestion, and expand access to free-to-air public broadcasts. What Is D2M Technology? Add Zee News as a Preferred Source D2M technology works in a similar way to FM radio and direct-to-home (DTH) television services. Just as FM radio stations send signals that are received by radios on specific frequencies, D2M will broadcast signals that can be received directly by smartphones. Like DTH, where a dish antenna receives signals from satellites and sends them to a set-top box, D2M will send content straight to mobile devices. The technology will use terrestrial transmission infrastructure along with a dedicated spectrum to deliver signals directly to smartphones, without needing an internet connection. (Also Read: Stop Scrolling! Check WhatsApp’s Latest Group Chat Updates: Text Stickers, Member Tags, Event Reminders And More) Spectrum Allocation for D2M The government plans to reserve the 470–582 MHz frequency band for D2M services. This spectrum will be specifically set aside to support the rollout of this new broadcast technology across the country, according to Information and Broadcasting Secretary Apurva Chandra, quoted by the Times of India. Live TV Without Internet One of the key benefits of D2M is that users will be able to watch live TV content, such as sports matches and news, without using mobile data. This could be especially useful in areas with poor internet connectivity or during situations where networks are overloaded. Government Push In June last year, IIT Kanpur released a white paper on D2M broadcasting in collaboration with Prasar Bharati and the Telecommunications Development Society. Later, in August 2023, the Ministry of Communications listed several use cases for D2M, including content delivery, education, and sharing important information during emergencies and disasters. Benefits for Users and Networks The government believes D2M can shift around 25–30% of video traffic away from mobile networks, helping reduce pressure on 5G services. According to reports, with nearly 80 crore smartphones in India and 69% of consumed content being video, the technology could play a major role in easing network congestion.

import dataclasses import os   import datasets import tokenizers import torch import torch.distributed as dist import torch.nn as nn import torch.nn.functional as F import torch.optim.lr_scheduler as lr_scheduler import tqdm from torch import Tensor from torch.distributed.checkpoint import load, save from torch.distributed.checkpoint.state_dict import StateDictOptions, get_state_dict, set_state_dict from torch.distributed.pipelining import PipelineStage, ScheduleGPipe     # Build the model @dataclasses.dataclass class LlamaConfig:     “”“Define Llama model hyperparameters.”“”     vocab_size: int = 50000  # Size of the tokenizer vocabulary     max_position_embeddings: int = 2048  # Maximum sequence length     hidden_size: int = 768  # Dimension of hidden layers     intermediate_size: int = 4*768  # Dimension of MLP’s hidden layer     num_hidden_layers: int = 12  # Number of transformer layers     num_attention_heads: int = 12  # Number of attention heads     num_key_value_heads: int = 3  # Number of key-value heads for GQA     class RotaryPositionEncoding(nn.Module):     “”“Rotary position encoding.”“”       def __init__(self, dim: int, max_position_embeddings: int) -> None:         “”“Initialize the RotaryPositionEncoding module.           Args:             dim: The hidden dimension of the input tensor to which RoPE is applied             max_position_embeddings: The maximum sequence length of the input tensor         ““”         super().__init__()         self.dim = dim         self.max_position_embeddings = max_position_embeddings         # compute a matrix of n\theta_i         N = 10_000.0         inv_freq = 1.0 / (N ** (torch.arange(0, dim, 2) / dim))         inv_freq = torch.cat((inv_freq, inv_freq), dim=–1)         position = torch.arange(max_position_embeddings)         sinusoid_inp = torch.outer(position, inv_freq)         # save cosine and sine matrices as buffers, not parameters         self.register_buffer(“cos”, sinusoid_inp.cos())         self.register_buffer(“sin”, sinusoid_inp.sin())       def forward(self, x: Tensor) -> Tensor:         “”“Apply RoPE to tensor x.           Args:             x: Input tensor of shape (batch_size, seq_length, num_heads, head_dim)           Returns:             Output tensor of shape (batch_size, seq_length, num_heads, head_dim)         ““”         batch_size, seq_len, num_heads, head_dim = x.shape         dtype = x.dtype         # transform the cosine and sine matrices to 4D tensor and the same dtype as x         cos = self.cos.to(dtype)[:seq_len].view(1, seq_len, 1, –1)         sin = self.sin.to(dtype)[:seq_len].view(1, seq_len, 1, –1)         # apply RoPE to x         x1, x2 = x.chunk(2, dim=–1)         rotated = torch.cat((–x2, x1), dim=–1)         output = (x * cos) + (rotated * sin)         return output     class LlamaAttention(nn.Module):     “”“Grouped-query attention with rotary embeddings.”“”       def __init__(self, config: LlamaConfig) -> None:         super().__init__()         self.hidden_size = config.hidden_size         self.num_heads = config.num_attention_heads         self.head_dim = self.hidden_size // self.num_heads         self.num_kv_heads = config.num_key_value_heads  # GQA: H_kv < H_q           # hidden_size must be divisible by num_heads         assert (self.head_dim * self.num_heads) == self.hidden_size           # Linear layers for Q, K, V projections         self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)         self.k_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=False)         self.v_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=False)         self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)       def forward(self, hidden_states: Tensor, rope: RotaryPositionEncoding) -> Tensor:         bs, seq_len, dim = hidden_states.size()           # Project inputs to Q, K, V         query_states = self.q_proj(hidden_states).view(bs, seq_len, self.num_heads, self.head_dim)         key_states = self.k_proj(hidden_states).view(bs, seq_len, self.num_kv_heads, self.head_dim)         value_states = self.v_proj(hidden_states).view(bs, seq_len, self.num_kv_heads, self.head_dim)           # Apply rotary position embeddings         query_states = rope(query_states)         key_states = rope(key_states)           # Transpose tensors from BSHD to BHSD dimension for scaled_dot_product_attention         query_states = query_states.transpose(1, 2)         key_states = key_states.transpose(1, 2)         value_states = value_states.transpose(1, 2)           # Use PyTorch’s optimized attention implementation         # setting is_causal=True is incompatible with setting explicit attention mask         attn_output = F.scaled_dot_product_attention(             query_states,             key_states,             value_states,             is_causal=True,             dropout_p=0.0,             enable_gqa=True,         )           # Transpose output tensor from BHSD to BSHD dimension, reshape to 3D, and then project output         attn_output = attn_output.transpose(1, 2).reshape(bs, seq_len, self.hidden_size)         attn_output = self.o_proj(attn_output)         return attn_output     class LlamaMLP(nn.Module):     “”“Feed-forward network with SwiGLU activation.”“”       def __init__(self, config: LlamaConfig) -> None:         super().__init__()         # Two parallel projections for SwiGLU         self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)         self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)         self.act_fn = F.silu  # SwiGLU activation function         # Project back to hidden size         self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)       def forward(self, x: Tensor) -> Tensor:         # SwiGLU activation: multiply gate and up-projected inputs         gate = self.act_fn(self.gate_proj(x))         up = self.up_proj(x)         return self.down_proj(gate * up)     class LlamaDecoderLayer(nn.Module):     “”“Single transformer layer for a Llama model.”“”       def __init__(self, config: LlamaConfig) -> None:         super().__init__()         self.input_layernorm = nn.RMSNorm(config.hidden_size, eps=1e–5)         self.self_attn = LlamaAttention(config)         self.post_attention_layernorm = nn.RMSNorm(config.hidden_size, eps=1e–5)         self.mlp = LlamaMLP(config)       def forward(self, hidden_states: Tensor, rope: RotaryPositionEncoding) -> Tensor:         # First residual block: Self-attention         residual = hidden_states         hidden_states = self.input_layernorm(hidden_states)         attn_outputs = self.self_attn(hidden_states, rope=rope)         hidden_states = attn_outputs + residual           # Second residual block: MLP         residual = hidden_states         hidden_states = self.post_attention_layernorm(hidden_states)         hidden_states = self.mlp(hidden_states) + residual         return hidden_states     class LlamaModel(nn.Module):     “”“The full Llama model without any pretraining heads.”“”       def __init__(self, config: LlamaConfig) -> None:         super().__init__()         self.rope = RotaryPositionEncoding(             config.hidden_size // config.num_attention_heads,             config.max_position_embeddings,         )           self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)         self.layers = nn.ModuleDict({             str(i): LlamaDecoderLayer(config) for i in range(config.num_hidden_layers)         })         self.norm = nn.RMSNorm(config.hidden_size, eps=1e–5)       def forward(self, input_ids: Tensor) -> Tensor:         # Convert input token IDs to embeddings         if self.embed_tokens is not None:             hidden_states = self.embed_tokens(input_ids)         else:             hidden_states = input_ids         # Process through all transformer layers, then the final norm layer         for n in range(len(self.layers)):             if self.layers[str(n)] is not None:                 hidden_states = self.layers[str(n)](hidden_states, self.rope)         if self.norm is not None:             hidden_states = self.norm(hidden_states)         # Return the final hidden states, and copy over the attention mask         return hidden_states     class LlamaForPretraining(nn.Module):     def __init__(self, config: LlamaConfig) -> None:         super().__init__()         self.base_model = LlamaModel(config)         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)       def forward(self, input_ids: Tensor) -> Tensor:         hidden_states = self.base_model(input_ids)         if self.lm_head is not None:             hidden_states = self.lm_head(hidden_states)         return hidden_states     # Generator function to create padded sequences of fixed length class PretrainingDataset(torch.utils.data.Dataset):     def __init__(self, dataset: datasets.Dataset, tokenizer: tokenizers.Tokenizer,                  seq_length: int, device: torch.device = None):         self.dataset = dataset         self.tokenizer = tokenizer         self.device = device         self.seq_length = seq_length         self.bot = tokenizer.token_to_id(“[BOT]”)         self.eot = tokenizer.token_to_id(“[EOT]”)         self.pad = tokenizer.token_to_id(“[PAD]”)       def __len__(self):         return len(self.dataset)       def __getitem__(self, index):         “”“Get a sequence of token ids from the dataset. [BOT] and [EOT] tokens         are added. Clipped and padded to the sequence length.         ““”         seq = self.dataset[index][“text”]         tokens: list[int] = [self.bot] + self.tokenizer.encode(seq).ids + [self.eot]         # pad to target sequence length         toklen = len(tokens)         if toklen < self.seq_length+1:             pad_length = self.seq_length+1 – toklen             tokens += [self.pad] * pad_length         # return the sequence         x = torch.tensor(tokens[:self.seq_length], dtype=torch.int64, device=self.device)         y = torch.tensor(tokens[1:self.seq_length+1], dtype=torch.int64, device=self.device)         return x, y     def load_checkpoint(model: nn.Module, optimizer: torch.optim.Optimizer) -> None:     dist.barrier()     model_state, optimizer_state = get_state_dict(         model, optimizer, options=StateDictOptions(full_state_dict=True),     )     load(         {“model”: model_state, “optimizer”:

WhatsApp New Features: WhatsApp has rolled out several new updates to make group chats more organised and expressive. Since many users depend on group chats for planning events, running communities, and staying in touch, the app has now refined how people communicate in shared conversations. The biggest update is the introduction of member tags. This feature lets users add custom labels to their names within a group. For example, someone can appear as “Anna’s Dad” in a school group, “Secretary” in a society group, or “Striker” in a football chat. These labels are visible only inside that group.  Users can choose different tags for different groups. WhatsApp said the feature will be available gradually to users worldwide. WhatsApp is also adding new ways to express yourself. With text stickers, users can now turn any word or phrase into a sticker by typing it into the sticker search bar. These stickers can be saved directly to the sticker pack without sending them first. This makes it quicker to reuse favourite expressions without depending only on emojis or GIFs. Add Zee News as a Preferred Source Group planning is also getting simpler. WhatsApp now allows custom event reminders inside group chats. Users can set early alerts while creating events. This helps reduce missed calls, late arrivals, or forgotten plans. The reminders work for both physical meetups and online events. These updates add to WhatsApp’s existing group features. Users can already share files up to 2GB, send HD photos and videos, share screens during calls, and start voice chats without making a formal call. WhatsApp says these changes are part of its effort to improve the group chat experience across devices. As group conversations play a bigger role in daily life, the app is clearly aiming to make chats more organised, clearer, and more user-friendly.

The Machine Learning Engineer’s Checklist: Best Practices for Reliable ModelsImage by Editor Introduction Building newly trained machine learning models that work is a relatively straightforward endeavor, thanks to mature frameworks and accessible computing power. However, the real challenge in the production lifecycle of a model begins after the first successful training run. Once deployed, a model operates in a dynamic, unpredictable environment where its performance can degrade rapidly, turning a successful proof-of-concept into a costly liability. Practitioners often encounter issues like data drift, where the characteristics of the production data change over time; concept drift, where the underlying relationship between input and output variables evolves; or subtle feedback loops that bias future training data. These pitfalls — which range from catastrophic model failures to slow, insidious performance decay — are often the result of lacking the right operational rigor and monitoring systems. Building reliable models that keep performing well in the long run is a different story, one that requires discipline, a robust MLOps pipeline, and, of course, skill. This article focuses on exactly that. By providing a systematic approach to tackle these challenges, this research-backed checklist outlines essential best practices, core skills, and sometimes not-to-miss tools that every machine learning engineer should be familiar with. By adopting the principles outlined in this guide, you will be equipped to transform your initial models into maintainable, high-quality production systems, ensuring they remain accurate, unbiased, and resilient to the inevitable shifts and challenges of the real world. Without further ado, here is the list of 10 machine learning engineer best practices I curated for you and your upcoming models to shine at their best in terms of long-term reliability. The Checklist 1. If It Exists, It Must Be Versioned Data snapshots, code for training models, hyperparameters used, and model artifacts — everything matters, and everything is subject to variations across your model lifecycle. Therefore, everything surrounding a machine learning model should be properly versioned. Just imagine, for instance, that your image classification model’s performance, which used to be great, starts to drop after a concrete bug fix. With versioning, you will be able to reproduce the old model settings and isolate the root cause of the problem more safely. There is no rocket science here — versioning is widely known across the engineering community, with core skills like managing Git workflows, data lineage, and experiment tracking; and specific tools like DVC, Git/GitHub, MLflow, and Delta Lake. 2. Pipeline Automation As part of continuous integration and continuous delivery (CI/CD) principles, repeatable processes that involve data preprocessing through training, validation, and deployments should be encapsulated in pipelines with automated running and testing underneath them. Suppose a nightly set-up pipeline that fetches new data — e.g. images captured by a sensor — runs validation tests, retrains the model if needed (because of data drift, for example), re-evaluates business key performance indicators (KPIs), and pushes the updated model(s) to staging. This is a common example of pipeline automation, and it takes skills like workflow orchestration, fundamentals of technologies like Docker and Kubernetes, and test automation knowledge. Commonly useful tools here include: Airflow, GitLab CI, Kubeflow, Flyte, and GitHub Actions. 3. Data Are First-Class Artifacts The rigor with which software tests are applied in any software engineering project must be present for enforcing data quality and constraints. Data is the essential nourishment of machine learning models from inception to serving in production; hence, the quality of whatever data they ingest must be optimal. A solid understanding of data types, schema designs, and data quality issues like anomalies, outliers, duplicates, and noise is vital to treat data as first-class assets. Tools like Evidently, dbt tests, and Deequ are designed to help with this. 4. Perform Rigorous Testing Beyond Unit Tests Testing machine learning systems involves specific tests for aspects like pipeline integration, feature logic, and statistical consistency of inputs and outputs. If a refactored feature engineering script applies a subtle modification in a feature’s original distribution, your system may pass basic unit tests, but through distribution tests, the issue might be detected in time. Test-driven development (TDD) and knowledge of statistical hypothesis tests are strong allies to “put this best practice into practice,” with imperative tools under the radar like the pytest library, customized data drift tests, and mocking in unit tests. 5. Robust Deployment and Serving Having a robust machine learning model deployment and serving in production entails that the model should be packaged, reproducible, scalable to large settings, and have the ability to roll back safely if needed. The so-called blue–green strategy, based on deploying into two “identical” production environments, is a way to ensure incoming data traffic can be shifted back quickly in the event of latency spikes. Cloud architectures together with containerization help to this end, with specific tools like Docker, Kubernetes, FastAPI, and BentoML. 6. Continuous Monitoring and Observability This is probably already in your checklist of best practices, but as an essential of machine learning engineering, it is worth pointing it out. Continuous monitoring and observability of the deployed model involves monitoring data drift, model decay, latency, cost, and other domain-specific business metrics beyond just accuracy or error. For example, if the recall metric of a fraud detection model drops upon the emergence of new fraud patterns, properly set drift alerts may trigger the need for retraining the model with fresh transaction data. Prometheus and business intelligence tools like Grafana can help a lot here. 7. Explainability, Fairness, and Governance of ML Systems Another essential for machine learning engineers, this best practice aims at ensuring the delivery of models with transparent, compliant, and responsible behavior, understanding and adhering to existing national or regional regulations — for instance, the European Union AI Act. An example of the application of these principles could be a loan classification model that triggers fairness checks before being deployed to guarantee no protected groups are unreasonably rejected. For interpretability and governance, tools like SHAP, LIME, model registries, and Fairlearn are highly recommended. 8. Optimizing Cost and Performance

Seoul: Samsung Electronics on Thursday reported a record-breaking operating profit for the fourth quarter, touching the 20 trillion-won ($13.8 billion) mark for the first time, driven by a supercycle in the chip industry. The fourth-quarter operating profit marked a more than 200 percent rise from a year earlier, the company said in a preliminary earnings report. If confirmed, it would mark the first time for the company’s quarterly earnings to reach the 20 trillion-won level, reports Yonhap news agency. Sales increased 22.7 percent to 93 trillion won. It was also the first time for the quarterly sales to surpass the 90 trillion-won mark. The data for net profit was not available. The operating profit was 1.8 percent higher than the average estimate, according to a survey by Yonhap Infomax, the financial data firm of Yonhap News Agency. Samsung Electronics did not disclose a detailed earnings breakdown for its individual business divisions. Add Zee News as a Preferred Source The company will release its final earnings report later this month. Analysts said the increased earnings apparently came amid improved profitability at the Device Solutions (DS) division, which covers the company’s core semiconductor business. According to Korea Investment & Securities Co., global prices of dynamic random-access memory (DRAM) and NAND flash jumped about 40 percent in the fourth quarter from the previous three-month period. Market observers estimate the operating profit of the DS division at around 16 trillion to 17 trillion won. The projection represents a sharp rise from just 7 trillion won posted in the third quarter. Analysts said Samsung’s non-memory business is also likely to have narrowed its operating losses, leading to an overall improvement in the division’s performance. Samsung’s mobile business is estimated to have posted an operating profit in the 2 trillion-won range, while the home appliance business likely suffered an operating loss of 100 billion won, according to market watchers. For the entire year of 2025, Samsung Electronics estimated its annual operating earnings at 43.53 trillion won, up 33 percent from a year earlier. Annual sales increased 10.6 percent to 332.77 trillion won. Data for net profit was not available as well. For 2026, analysts said Samsung Electronics is anticipated to maintain its robust performance, backed by its expanded high bandwidth memory (HBM) capacity. “This year, Samsung Electronics is expected to post an annual operating profit of 123 trillion won, driven by a sharp rise in DRAM prices and increased HBM shipments,” said Kim Dong-won, a researcher at KB Securities Co.

WhatsApp Update: In its latest announcement, the widely used messaging platform WhatsApp has introduced a set of new features focused on improving interaction and organisation in group chats. WhatsApp said the updates aim to help users communicate more clearly and manage group conversations more easily. One of the key updates is the introduction of member tags. This feature allows users to add a short label to their name within a group, explaining their role or identity in that specific chat. These tags are customisable for each group. WhatsApp said that member tags are designed to give context, especially in large or active groups where participants may not know each other well. The company added that the feature is being rolled out gradually to users. Text Stickers From Typed Words Add Zee News as a Preferred Source WhatsApp has also launched a new text stickers feature. With this update, users can turn any typed word into a sticker by entering it in the Sticker Search bar. Once created, these text-based stickers can either be sent directly in chats or saved into sticker lists for future use. This feature gives users another way to share their reactions during conversations without needing to download third-party sticker packs. (Also Read: Why Is Spacebar The Largest Key On Laptop Or Desktop Keyboard? Details Inside) Event Reminders In Group Chats Another new addition is event reminders in group chats. When users create an event in a group, they can now set custom early reminders for invitees. This helps group members remember upcoming events such as parties, meetings, or online calls. WhatsApp already supports creating events, pinning them for visibility, collecting RSVPs, and sharing event updates in one place. The new reminder option builds on these tools to make event planning more organised. WhatsApp said these features are part of a broader effort to improve group chat experiences. In recent years, the platform has added support for large file sharing up to 2GB, HD photo and video sharing, screen sharing, and voice chats within groups. Looking ahead, WhatsApp is also preparing to introduce more features in 2026.

Gradient Descent: Visualizing the Foundations of Machine LearningImage by Author Editor’s note: This article is a part of our series on visualizing the foundations of machine learning. Welcome to the first entry in our series on visualizing the foundations of machine learning. In this series, we will aim to break down important and often complex technical concepts into intuitive, visual guides to help you master the core principles of the field. Our first entry focuses on the engine of machine learning optimization: gradient descent. The Engine of Optimization Gradient descent is often considered the engine of machine learning optimization. At its core, it is an iterative optimization algorithm used to minimize a cost (or loss) function by strategically adjusting model parameters. By refining these parameters, the algorithm helps models learn from data and improve their performance over time. To understand how this works, imagine the process of descending the mountain of error. The goal is to find the global minimum, which is the lowest point of error on the cost surface. To reach this nadir, you must take small steps in the direction of the steepest descent. This journey is guided by three main factors: the model parameters, the cost (or loss) function, and the learning rate, which determines your step size. Our visualizer highlights the generalized three-step cycle for optimization: Cost function: This component measures how “wrong” the model’s predictions are; the objective is to minimize this value Gradient: This step involves calculating the slope (the derivative) at the current position, which points uphill Update parameters: Finally, the model parameters are moved in the opposite direction of the gradient, multiplied by the learning rate, to move closer to the minimum Depending on your data and computational needs, there are three primary types of gradient descent to consider. Batch GD uses the entire dataset for each step, which is slow but stable. On the other end of the spectrum, stochastic GD (SGD) uses just one data point per step, making it fast but noisy. For many, mini-batch GD offers the best of both worlds, using a small subset of data to achieve a balance of speed and stability. Gradient descent is crucial for training neural networks and many other machine learning models. Keep in mind that the learning rate is a critical hyperparameter that dictates success of the optimization. The mathematical foundation follows the formula \[\theta_{new} = \theta_{old} – a \cdot \nabla J(\theta),\] where the ultimate goal is to find the optimal weights and biases to minimize error. The visualizer below provides a concise summary of this information for quick reference. Gradient Descent: Visualizing the Foundations of Machine Learning (click to enlarge)Image by Author You can click here to download a PDF of the infographic in high resolution. Machine Learning Mastery Resources These are some selected resources for learning more about gradient descent: Gradient Descent For Machine Learning – This beginner-level article provides a practical introduction to gradient descent, explaining its fundamental procedure and variations like stochastic gradient descent to help learners effectively optimize machine learning model coefficients.Key takeaway: Understanding the difference between batch and stochastic gradient descent. How to Implement Gradient Descent Optimization from Scratch – This practical, beginner-level tutorial provides a step-by-step guide to implementing the gradient descent optimization algorithm from scratch in Python, illustrating how to navigate a function’s derivative to locate its minimum through worked examples and visualizations.Key takeaway: How to translate the logic into a working algorithm and how hyperparameters affect results. A Gentle Introduction To Gradient Descent Procedure – This intermediate-level article provides a practical introduction to the gradient descent procedure, detailing the mathematical notation and providing a solved step-by-step example of minimizing a multivariate function for machine learning applications.Key takeaway: Mastering the mathematical notation and handling complex, multi-variable problems. Be on the lookout for for additional entries in our series on visualizing the foundations of machine learning. About Matthew Mayo Matthew Mayo (@mattmayo13) holds a master’s degree in computer science and a graduate diploma in data mining. As managing editor of KDnuggets & Statology, and contributing editor at Machine Learning Mastery, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, language models, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.

Oppo Reno15 Series: Oppo India has launched its premium Reno15 Series with three models: Reno15 Pro, Reno15 Pro Mini, and Reno15. The new phones feature advanced cameras, smart AI features, and sleek designs inspired by nature. The standout feature is the first-ever HoloFusion Technology with compact, durable builds. Both Reno15 Pro and Pro Mini offer a powerful 200MP main camera paired with a 50MP 3.5x optical zoom lens, offering up to 120x digital zoom. These are enhanced by PureTone Imaging Technology and latest AI photo editing tools. Add Zee News as a Preferred Source

Top 5 Vector Databases for High-Performance LLM ApplicationsImage by Editor Introduction Building AI applications often requires searching through millions of documents, finding similar items in massive catalogs, or retrieving relevant context for your LLM. Traditional databases don’t work here because they’re built for exact matches, not semantic similarity. When you need to find “what means the same thing or is similar” rather than “what matches exactly,” you need infrastructure designed for high-dimensional vector searches. Vector databases solve this by storing embeddings and facilitating super-fast similarity searches across billions of vectors. This article covers the top five vector databases for production LLM applications. We’ll explore what makes each unique, their key features, and practical learning resources to help you choose the right one. 1. Pinecone Pinecone is a serverless vector database that removes infrastructure headaches. You get an API, push vectors, and it handles scaling automatically. It’s the go-to choice for teams that want to ship fast without worrying about administrative overhead. Pinecone provides serverless auto-scaling where infrastructure adapts in real time based on demand without manual capacity planning. It combines dense vector embeddings with sparse vectors for BM25-style keyword matching through hybrid search capabilities, It also indexes vectors upon upsert without batch processing delays, enabling real-time updates for your applications. Here are some learning resources for Pinecone: 2. Qdrant Qdrant is an open-source vector database written in Rust, which offers both speed and memory efficiency. It’s designed for developers who need control over their infrastructure while maintaining high performance at scale. Qdrant offers memory-safe performance with efficient resource usage and exceptional speed through its Rust implementation. It supports payload indexing and other indexing types for efficient structured-data filtering alongside vector search, and reduces memory footprint by using scalar and product quantization techniques for large-scale deployments. Qdrant supports both in-memory and on-disk payload storage, and enables horizontal scaling with sharding and replication for high availability in distributed mode. Learn more about Qdrant with these resources: 3. Weaviate Weaviate is an open-source vector database that works well for combining vector search with traditional database capabilities. It’s built for complex queries that need both semantic understanding and structured-data filtering. Weaviate combines keyword search with vector similarity in a single unified query through native hybrid search. It supports GraphQL for efficient search, filtering, and retrieval, and integrates directly with OpenAI, Cohere, and Hugging Face models for automatic embedding through built-in vectorization. It also provides multimodal support that enables search across text, images, and other data types simultaneously. Qdrant’s modular architecture offers a plugin system for custom modules and third-party integrations. Check out these Weaviate resources for more information: 4. Chroma Chroma is a lightweight, embeddable vector database designed for simplicity. It works well for prototyping, local development, and applications that don’t need massive scale but want zero operational overhead. Chroma runs in process with your application without requiring a separate server through embedded mode. It has a simple setup with minimal dependencies, and is a great option for rapid prototyping. Chroma saves and loads data locally with minimal configuration through persistence. These Chroma learning resources may be helpful: 5. Milvus Milvus is an open-source vector database built for billion-scale deployments. When you need to handle massive datasets with distributed architecture, Milvus delivers the scalability and performance required for enterprise applications. Milvus is capable of handling billions of vectors with millisecond search latency for enterprise-scale performance requirements. It separates storage from compute through cloud-native architecture built on Kubernetes for flexible scaling, and supports multiple index types including HNSW, IVF, DiskANN, and more for different use cases and optimization strategies. Zilliz Cloud offers a fully managed service built on Milvus for production deployments. You may find these Milvus learning resources useful: Wrapping Up Choosing the right vector database depends on your specific needs. Start with your constraints: Do you need sub-10ms latency? Multimodal search? Billion-scale data? Self-hosted or managed? The right choice balances performance, operational complexity, and cost for your application. Most importantly, these databases are mature enough for production; the real decision is matching capabilities to your requirements. If you already use PostgreSQL and would like to explore a vector search extension, you can also consider pgvector. To learn more about how vector databases work, read The Complete Guide to Vector Databases for Machine Learning. About Bala Priya C Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.

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