Best Machine Learning Software for MySQL

Deepnote is building the best data science notebook for teams. Connect your data, explore and analyze it within the notebook with real-time collaboration and versioning. Share links to your projects with other analysts and data scientists on your team, or present your polished, published notebooks to end users and stakeholders. All of this is done through a powerful, browser-based UI that runs in the cloud.

Dagster is the cloud-native open-source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. It is the platform of choice data teams responsible for the development, production, and observation of data assets. With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.

Immuta's Data Access Platform is built to give data teams secure yet streamlined access to data. Every organization is grappling with complex data policies as rules and regulations around that data are ever-changing and increasing in number. Immuta empowers data teams by automating the discovery and classification of new and existing data to speed time to value; orchestrating the enforcement of data policies through Policy-as-code (PaC), data masking, and Privacy Enhancing Technologies (PETs) so that any technical or business owner can manage and keep it secure; and monitoring/auditing user and policy activity/history and how data is accessed through automation to ensure provable compliance. Immuta integrates with all of the leading cloud data platforms, including Snowflake, Databricks, Starburst, Trino, Amazon Redshift, Google BigQuery, and Azure Synapse. Our platform is able to transparently secure data access without impacting performance. With Immuta, data teams are able to speed up data access by 100x, decrease the number of policies required by 75x, and achieve provable compliance goals.

InsightFinder Unified Intelligence Engine platform (UIE) provides human-centered AI solutions to identify root causes of incidents and prevent them from happening. InsightFinder uses patented self-tuning, unsupervised machine learning to continuously learn from logs, traces and triage threads of DevOps Engineers and SREs to identify root causes and predict future incidents. Companies of all sizes have adopted the platform and found that they can predict business-impacting incidents hours ahead of time with clearly identified root causes. You can get a complete overview of your IT Ops environment, including trends and patterns as well as team activities. You can also view calculations that show overall downtime savings, cost-of-labor savings, and the number of incidents solved.

Connect your data to the Engine by uploading a file, or connecting to a database. You can then analyze your data with visualizations or prepare it for machine learning modeling using the data wrangling recipes. Build machine learning models using algorithms such as clustering, classification, or regression. All without writing any code. Discover insights into your data using the feature importance tools, prediction explanations, and what-ifs. Our connectors allow you to make predictions and integrate them into your existing systems.

The Accern No-Code NLP Platform empowers citizen data scientists to extract insights from unstructured data, minimize time to value and maximize ROI with pre-built AI/ML/NLP solutions. Recognized as the first No-Code NLP platform and industry leader with the highest accuracy scores, Accern also enables data scientists to customize end-to-end workflows that enhance existing models and enrich BI dashboards.

Datatron provides tools and features that are built from scratch to help you make machine learning in production a reality. Many teams realize that there is more to deploying models than just the manual task. Datatron provides a single platform that manages all your ML, AI and Data Science models in production. We can help you automate, optimize and accelerate your ML model production to ensure they run smoothly and efficiently. Data Scientists can use a variety frameworks to create the best models. We support any framework you use to build a model (e.g. TensorFlow and H2O, Scikit-Learn and SAS are supported. Explore models that were created and uploaded by your data scientists, all from one central repository. In just a few clicks, you can create scalable model deployments. You can deploy models using any language or framework. Your model performance will help you make better decisions.

Apache PredictionIO®, an open-source machine-learning server, is built on top a state of the art open-source stack that allows data scientists and developers to create predictive engines for any type of machine learning task. It allows you to quickly create and deploy an engine as web service on production using customizable templates. Once deployed as a web-service, it can respond to dynamic queries immediately, evaluate and tune multiple engine variations systematically, unify data from multiple platforms either in batch or real-time for comprehensive predictive analysis. Machine learning modeling can be speeded up with pre-built evaluation methods and systematic processes. These measures also support machine learning and data processing libraries like Spark MLLib or OpenNLP. You can create your own machine learning models and integrate them seamlessly into your engine. Data infrastructure management simplified. Apache PredictionIO®, a complete machine learning stack, can be installed together with Apache Spark, MLlib and HBase.

Data engineering workflows that are powerful, but without the headaches of infrastructure. Simple, reusable Python is used to define complex streaming, scheduling and data backfill pipelines. Fetch all your data in real time, no matter how complicated. Deep learning and LLMs can be used to make decisions along with structured business data. Don't pay vendors for data that you won't use. Instead, query data right before online predictions. Experiment with Jupyter and then deploy into production. Create new data workflows and prevent train-serve skew in milliseconds. Instantly monitor your data workflows and track usage and data quality. You can see everything you have computed, and the data will replay any information. Integrate with your existing tools and deploy it to your own infrastructure. Custom hold times and withdrawal limits can be set.

Scalable Python framework designed to build real-time intelligent applications, data pipelines, and integrate AI/ML models

Our intelligent data and analytics platform will help you scale faster by overcoming application silos, cost overruns, and skill obsolescence. A platform that is intelligently designed to perform advanced analytics and data management. Scales across all technology landscapes. Our architecture understands the data flow and transformations throughout its entire lifecycle. Developing future-proof enterprise AI apps. A highly extensible and modular platform that allows the handling of multiple components. Scalable architecture with multi-tenant design. Advanced data visualization makes it easy to analyze complex data structures. This results in enhanced enterprise AI apps in a low-code, intuitive platform. Our hybrid multi-cloud platform was built using community open source software, making it highly adaptable, secure, and low-cost.

Alteryx AI Platform will help you enter a new age of analytics. Empower your organization through automated data preparation, AI powered analytics, and accessible machine learning - all with embedded governance. Welcome to a future of data-driven decision making for every user, team and step. Empower your team with an intuitive, easy-to-use user experience that allows everyone to create analytical solutions that improve productivity and efficiency. Create an analytics culture using an end-toend cloud analytics platform. Data can be transformed into insights through self-service data preparation, machine learning and AI generated insights. Security standards and certifications are the best way to reduce risk and ensure that your data is protected. Open API standards allow you to connect with your data and applications.

TIMi allows companies to use their corporate data to generate new ideas and make crucial business decisions more quickly and easily than ever before. The heart of TIMi’s Integrated Platform. TIMi's ultimate real time AUTO-ML engine. 3D VR segmentation, visualization. Unlimited self service business Intelligence. TIMi is a faster solution than any other to perform the 2 most critical analytical tasks: data cleaning, feature engineering, creation KPIs, and predictive modeling. TIMi is an ethical solution. There is no lock-in, just excellence. We guarantee you work in complete serenity, without unexpected costs. TIMi's unique software infrastructure allows for maximum flexibility during the exploration phase, and high reliability during the production phase. TIMi allows your analysts to test even the most crazy ideas.

An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.

Machine learning uncovers hidden patterns in enterprise data and generates new value for businesses. Oracle Machine Learning makes it easier to create and deploy machine learning models for data scientists by using AutoML technology and reducing data movement. It also simplifies deployment. Apache Zeppelin notebook technology, which is open-source-based, can increase developer productivity and decrease their learning curve. Notebooks are compatible with SQL, PL/SQL and Python. Users can also use markdown interpreters for Oracle Autonomous Database to create models in their preferred language. No-code user interface that supports AutoML on Autonomous Database. This will increase data scientist productivity as well as non-expert users' access to powerful in-database algorithms to classify and regression. Data scientists can deploy integrated models using the Oracle Machine Learning AutoML User Interface.

Any data source can be extracted and transformed with Natural Language Connect to your data source - everything from your DB to CRM to your billing system. Increase Productivity by 5-10x. Forget about wasting your time with clunky tools. Natural Language allows you to query complex data in seconds. You can move from DIY tools to AI-powered non-DIY tools. In a matter of seconds, you can create complex dashboards or reports. Layerup AI will do all the heavy lifting. Layerup AI not only provides instant answers to queries that would take 5-40 hours per month, but also acts as your personal data analyst 24/7 and can provide complex dashboards/charts you can embed anywhere.

With a fully automated cloud infrastructure, experts can explore data and write stable codes at the same time. Zerve’s data science environment gives data scientists and ML teams a unified workspace to explore, collaborate and build data science & AI project like never before. Zerve provides true language interoperability. Users can use Python, R SQL or Markdown in the same canvas and connect these code blocks. Zerve offers unlimited parallelization, allowing for code blocks and containers to run in parallel at any stage of development. Analysis artifacts can be automatically serialized, stored and preserved. This allows you to change a step without having to rerun previous steps. Selecting compute resources and memory in a fine-grained manner for complex data transformation.

Platform for customer intelligence data that helps brands analyze user behavior across multiple channels. Analyze data quickly and accurately. AI and ML solutions can help you identify customer behavior patterns to make informed decisions. B2Metric integrates with a wide range of sources, including databases that you use most often. Optimize your retention strategy by predicting churn in customers and taking preventive action accordingly. To enable targeted marketing, categorize customers based on their characteristics, behaviors, and preferences. Optimize marketing strategies by leveraging data-driven insights. This will improve performance, target, personalization and budget. Optimizing touchpoints and tailoring your marketing efforts will help you provide unique customer experiences. AI-based marketing analytics to reduce user churn and increase growth. Advanced ML algorithms can identify customers at risk of churning and develop proactive retention strategy.

Kraken is suitable for all data scientists and analysts. It is designed to be easy-to-use and no-code automated machine-learning platform. The Kraken no code automated machine learning platform (AutoML), simplifies and automates data science tasks such as data prep, data cleaning and algorithm selection. It also allows for model training and deployment. Kraken was designed with engineers and analysts in mind. If you've done data analysis before, you're ready! Kraken's intuitive interface and integrated SONAR(c), training make it easy for citizens to become data scientists. Data scientists can work more efficiently and faster with advanced features. You can use Excel or flat files for daily reporting, or just ad-hoc analysis. With Kraken's drag-and-drop CSV upload feature and the Amazon S3 connector, you can quickly start building models. Kraken's Data Connectors allow you to connect with your favorite data warehouse, business intelligence tool, or cloud storage.

Kepler's Automated Data Science workflows make it easy to eliminate the need for programming and machine learning. You can quickly join and get data-driven insights that are unique to your company and your data. Our SaaS-based model allows you to receive continuous updates and additional Workflows from our AI and ML teams. With a platform that grows with you business, scale AI and accelerate time to value using the skills and team already within your company. Advanced AI and machine learning capabilities can solve complex business problems without the need to have any technical ML knowledge. You can leverage state-of the-art, end to end automation, a large library of AI algorithms, as well as the ability to quickly deploy machine-learning models. Organizations use Kepler to automate and augment critical business processes in order to increase productivity and agility.

Declarative machine-learning systems offer the best combination of flexibility and simplicity, allowing for the fastest way to implement state-of-the art models. The system works by asking users to specify the "what" and then the system will figure out the "how". Start with smart defaults and iterate down to the code level on parameters. With Ludwig at Uber, and Overton from Apple, our team pioneered declarative machine-learning systems in industry. You can choose from our pre-built data connectors to support your databases, data warehouses and lakehouses as well as object storage. You can train state-of the-art deep learning models without having to manage infrastructure. Automated Machine Learning achieves the right balance between flexibility and control in a declarative manner. You can train and deploy models quickly using a declarative approach.

Open-Source AI layer for databases. Machine Learning capabilities can be integrated directly into your data domain to increase efficiency and productivity. MindsDB makes it easy to create, train, and then test ML models. Then publish them as virtual AI tables into databases. Integrate seamlessly with all major databases. SQL queries can be used to manipulate ML models. You can increase model training speed using GPU without affecting the performance of your database. Learn how the ML model arrived at its conclusions and what factors affect prediction confidence. Visual tools that allow you to analyze model performance. SQL and Python queries that return explanation insights in a single code. You can use What-if analysis to determine confidence based upon different inputs. Automate the process for applying machine learning using the state-of the-art Lightwood AutoML library. Machine Learning can be used to create custom solutions in your preferred programming language.