Deep learning is a subset of machine learning that employs artificial neural networks with multiple abstraction layers to create patterns of algorithms and data. These networks imitate the human brain's functioning to process data and generate patterns for decision making.

Deep Learning Frameworks are software libraries designed to interface easily with deep learning algorithms, enabling users to deploy and construct models conveniently without getting involved in the intricate details of the algorithms.

• Multiple Network Architectures: Deep learning frameworks usually support a wide range of network architectures like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), etc.
• Pre-Trained Models: Most frameworks come with pre-trained models. These are enormously beneficial as these models have already been trained on vast datasets and have proven to be high performing.
• Wide Platform Availability: Frameworks are often available across various devices, from powerful GPUs for enterprise-level MLM implementations and lightweight libraries for mobile integration.
• Vendor Support: Generally, frameworks are backed by large technology companies providing constant updates, maintenance, and community support to smooth functioning.

Implementing deep learning frameworks requires planning and evaluation, focusing on the specific needs of the organization. Factors considered include the organization's expertise, system infrastructure, and what the models will be used for. Once the most suitable framework is selected, the focus shifts to creating productive models that meet the organization's needs.

• Reduced Development Time: Since deep learning frameworks provide built-in functions and pre-trained models, developers do not need to write codes from scratch. It significantly reduces development time.
• Proven reliability: These frameworks are used by scientists, researchers, and developers worldwide. As a result, these tools have been tried and tested in various environments, leading to improved reliability and stability.
• Access to Advanced Features: Deep learning frameworks enable developers to leverage the power of deep learning without the need to understand the complex algorithms underlying these processes. They offer pre-compiled functions to handle tasks like optimized mathematical computations, data structure management, and model assessment.
• Ease of Integration: Many deep learning frameworks seamlessly integrate with other libraries or frameworks, thereby increasing their usefulness.
• Community Support: Since these frameworks are open-source and widely used, there exists a vast community providing robust support.
• Cost-Effective: Using deep learning frameworks is generally more cost-effective than building models from scratch since it saves significant development time and leverages built-in, optimized functions.

• Steep Learning Curve: Even though these frameworks abstract many details of the underlying algorithms, learning to use them can be challenging for novice users.
• Limited Customization: While these frameworks offer several built-in features, there may be limitations in customization as per a need-specific basis as they are designed considering generic needs.
• Dependencies and Compatibility Issues: Different frameworks may have different dependencies and incompatibility issues with certain types of hardware or other software.
• Vendor lock-in: Developers may run into the risk of being locked into one ecosystem, making it challenging to move into another framework if required.

In conclusion, while there are some disadvantages, the benefits outweigh the challenges, making deep learning frameworks an essential tool for any organization planning to leverage machine learning. With careful planning and strategic implementation, these frameworks can significantly impact accelerating the development and deployment of effective machine learning solutions.